SIMULATION OF HAULAGE PERFORMANCE OF 2 WD AGRICULTURAL TRACTORS IN VISUAL BASIC

by

Bharat Devaba Pawar

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in

Agricultural Systems and Engineering

Examination Committee: Dr. Peeyush Soni (Chairperson)Prof. Athapol NoomhormDr. Roongruang Kalsirisilp (External Expert)

Nationality: Indian

Previous Degree: Bachelor of Technology in Agricultural Engineering Dr. Balasaheb Sawant Konkan Agricultural University,

Dapoli, Maharashtra, India

Scholarship Donor: AIT Fellowship

Asian Institute of TechnologySchool of Environment, Resources and Development

ThailandMay 2010

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ACKNOWLEDGEMENTS

The author wished to express his sincere gratitude and profound regards to his advisor Dr. Peeyush Soni, chairman of the advisory committee, for his invaluable guidance, constant encouragement, constructive suggestions and moral support during the entire study period.

Special acknowledgement is due to Prof. Athapol and Dr. Roongruang Kalsirisilp, advisory committee members for their guidance and suggestions in improving this thesis.

The author would like to express his sincere gratitude to the Technicians Mr. Sawatdi Khugkhalo, Mr. Choengchay Thuntarucks, and Mr. Karn Intoop of the ASE for providing valuable information and help during the investigation which made this study possible.

He would to appreciate the assistance provided by his friends, Mr. Madhav Gholkar in analysis and compiling the data. He would like to express sincere gratitude to his brother Mr. Mayuresh Pawar at India (Mumbai) for his love and care which made the investigation possible.

The author wishes to extend special thanks to Mr. Vikramsinh Shinde for his constant support and encouragement during his entire stay in AIT.

He is thankful for the friendship and cooperation extended by his friends in AIT, specially Itesh, Pallavi, Jothiganesh, Ancy, Senthil, Kartik, Girish and Khagendra. Also, encouragement provided by Shankar is highly appreciated.

The author is indebted to Prof V.M. Salokhe for providing Protected Cultivation Project fellowship to pursue master study at AIT. He is equally grateful to Asian Institute of Technology for providing scholarship and research grant to pursue master study at AIT during the entire study period.

Finally, the author is beholden to his parents and grandparents, Devaba Bhivsan Pawar – Pushpalata Devaba Pawar and Bhivsan Gangaram Pawar – Mulkanbai Bhivsan Pawar and all the family members for their constant love and inspiration which encouraged him to accomplish the work successfully.

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ABSTRACT

An investigation was carried out with a two wheel drive tractor (Kubota L 345 II DT and John Deere 2450) and a two wheel trailer to find out the effects of load, hitch point height, travel speed, rear wheel ballasting and inflation pressure on fuel consumption in cc/ton-km for haulage operations. A stretch of 700 meters (0.7 km) long tar macadam road was selected for the research study. Over all 11 experiments were carried out in four phases, in the first phase of the study, 5 experiments were carried out with first five selected variables using Kubota L345 II DT Tractor. The main objective was to find out the effect of individual parameters on fuel consumption, keeping others constant. In the second phase another experiment was carried out to determine the combined effect of all the five selected parameters on fuel consumption using the same tractor. The third and fourth phases comprised of repetition of experiments as those of first and second phases respectively using the John Deere 2450 tractor. The fuel consumption and slip data obtained was analyzed statistically and ANOVA was computed using software sigma stat. A visual basic program in C was used to develop mathematical models. It was observed from the results that with the increase in load the fuel consumption decreased to a certain value and then increased again. The similar trend was observed with inflation pressure, rear wheel ballasting and hitch point height for both the selected type of tractors. It was also observed that with the increase in travel speed there was a decrease in fuel consumption. John Deere 2450 tractor was found to be relatively more fuel efficient as compared to Kubota L 345 II DT. The Sigma stat software was used to optimize the selected variables for best fuel economy. These optimum levels of the parameters for the Kubota L 345 II DT were, 4.0438 tones of load, 330 mm of hitch point height, 5.4 km/h of travel speed, 338.8 kg of ballasting and 2.12 kg/cm2 of Inflation Pressure. The Optimum levels of the parameters for John Deere 2450 were 4 tones of load, 490 mm of hitch point height, 9.27 km/h of travel speed and 1.06 kg/cm2 of Inflation Pressure.

Keywords: 2 Wheel drive tractor, 2 wheel trailer, tire size, inflation pressure, ballasting, hitch point height, tar macadam road

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TABLE OF CONTENTS

Chapter

Title Page. No.

Title Page iAcknowledgements iiAbstract iiiTable of Contents ivList of Figures viList of Tables viiAbbreviations viii

I INTRODUCTION 11.1 Background 11.2 Problem Identification 21.3Scope of Study 31.4 Objectives of the Study 4

II REVIEW OF LITERATURE 52.1 Basic Terminologies for Haulage Performance 52.2 Application of Optimization 52.3 Application of Optimization in Farm Power 62.4 Effect of Haulage Performance Parameters on Fuel Consumption 72.5 Measurement of Sleep, Draft and Speed 92.6 Methods of Ballasting 112.7 Simulation of Haulage Performance 122.8 Type of Trailer Hitches 14

III METHODOLOGY 163.1 General 163.2 Software Development and Validation 163.3 Selection of Tractor and Trailer 163.4 Selection of Tires and Inflation Pressure 163.5 Selection of other Variables 173.6 Parameters maintained Constant during Investigation 183.7 Experimental Setup 193.8 Effect of Load, Hitch Point Height, Travel Speed, Rear Wheel

Ballast and Inflation Pressure on Fuel Consumption 19

3.9 Evaluation of Maximum limit of Ballasting 233.10 Analysis of Data 283.11Combine Effect of Load, Hitch Point Height, Travel Speed, Rear

Wheel Ballast and Inflation Pressure on Fuel Consumption28

3.12 ANOVA for Five selected Parameters 283.13 Multiple Regression Analysis 283.14 Flow Chart for Simulation of Haulage Performance 29

IV RESULTS AND DISCUSSION 304.1 General 304.2 Effect of Load on Fuel Consumption 30

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4.3 Effect of Hitch Point Height on Fuel Consumption 324.4 Effect of Travel Speed on Fuel Consumption 344.5 Effect of Rear Wheel Ballasting on Fuel Consumption 354.6 Effect of Inflation Pressure on Fuel Consumption 364.7 Combined Effect of Five Selected Parameters on Fuel Consumption 394.8 Multiple Regression Analysis 39

V CONCLUSIONS 41

VI RECOMMENDATIONS FOR FUTURE WORK 43

REFERENCES 44

APPENDICES 49

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LIST OF FIGURES

Figure No. Title Page. No.

3.1 View of the test tractor Kubota L 345 II DT with loaded trailer 20

3.2Arrangement of hitch point height for Kubota L 345 II DT Tractor 63, 53 and 43 cm

20

3.3Arrangement of hitch point height for Kubota L 345 II DT Tractor 33 and 23 cm

20

3.4 Ballasted rear wheel of Kubota L 345 II DT tractor 25

3.5Weight transfer on rear wheels when the tractor is on an upward slope

26

3.6Weight transfer on rear wheels of a tractor with a loaded trailer when it is moving at an upward slope

27

3.7 View of test tractor (John Deere 2450) with loaded trailer 27

3.8Arrangement of hitch point height at rear of John Deere 2450 tractor (65.5 and 58 cm)

27

3.9Arrangement of hitch point height at rear of John Deere 2450 tractor (49, 37.5 and 27.4 cm)

27

3.10 Flow Chart for Simulation of Haulage Performance 294.1 Effect of Load on Fuel consumption for Kubota L 345 II DT tractor 314.2 Effect of Load on Fuel consumption for John Deere 2450 Tractor 32

4.3Effect of hitch point height on fuel consumption for Kubota L 345 II DT tractor

33

4.4Effect of Hitch Point Height on Fuel consumption for John Deere 2450 Tractor

33

4.5Effect of Travel Speed on Fuel consumption for Kubota L 345 II DT Tractor

35

4.6Effect of Travel Speed on Fuel consumption for John Deere 2450 Tractor

35

4.7Effect of Rear wheel ballasting on Fuel consumption for Kubota L 345 II DT Tractor

36

4.8Effect of Inflation Pressure on Fuel consumption for Kubota L 345 II DT Tractor

38

4.9Effect of Inflation Pressure on Fuel consumption for John Deere 2450 Tractor

38

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LIST OF TABLES

Table No. Title Page. No.

3.1 List of parameters kept constant during the experiments and their relevant particulars

18

3.2 List of equipments, accessories, facilities used during experiments 193.3 Independent and dependent variables for Kubota L 345 II DT Tractor 213.4 Independent and dependent variables for John Deere 2450 Tractor 224.1 Calculated values of slip, fuel consumption and travel speed for

different levels of load for Kubota L 345 II DT Tractor.31

4.2 Calculated values of slip, fuel consumption and travel speed for different levels of load for John Deere 2450 Tractor

31

4.3 Calculated values of slip, fuel consumption and travel speed for different levels of hitch point height for Kubota L 345 II DT Tractor.

32

4.4 Calculated values of slip, fuel consumption and travel speed for different levels of hitch point height for John Deere 2450 Tractor

33

4.5 Calculated values of slip, fuel consumption and travel speed for different levels of travel speed for Kubota L 345 II DT Tractor.

34

4.6 Calculated values of slip, fuel consumption and travel speed for different levels of travel speed for John Deere 2450 Tractor

34

4.7 Calculated values of slip, fuel consumption and travel speed for different levels of drive wheel ballasting for Kubota L 345 II DT Tractor

36

4.8 Calculated values of slip, fuel consumption and travel speed for different levels of inflation pressure for Kubota L 345 II DT Tractor

37

4.9 Calculated values of slip, fuel consumption and travel speed for different levels of inflation pressure for John Deere 2450 tractor

37

4.10 Multiple regression coefficients values for Kubota L 345 II DT and John Deere 2450 Tractor

40

vii

ABBREVIATIONS

Agril : AgriculturalAm. : AmericanAIT : Asian Institute of Technologyamp. : Ampere (s)Anon. : AnonymousANOVA : Analysis of VarianceASAE : American Society of Agricultural EngineeringB.Tech : Bachelor of Technologyc.c : cubic centimeterco. : CompanyDF : Degree of FreedomDI : Direct Injectiondia. : DiameterEngg. : EngineeringEqn : Equationet al. : and othersEngrs. : EngineersFC : Fuel Consumptionfig : Figureha : Hectarehph : Hitch Point Heighthp : Horsepowerhr : HourJ : Journalkg : Kilogramkm : KilometerkW : KilowattkWh : Kilowatt-hourkmph : Kilometer per hourlit : Literm : Meterml : Millilitermn : Millionprof. : Professorrpm : Revolutions per minutesoc. : Societysq.cm : square centimetersTrans : TransactionWD : Wheel Drive

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CHAPTER I

INTRODUCTION

1.1 Background

Agriculture is the soul of human life on the earth as it provides food and clothing to human without which survival of the human on the earth is almost impossible. In the past, agricultural operations were performed with only two power sources: human and animals. A considerable change in agriculture has been introduced, due to the inventions in science and technology. Modern agriculture is based on highly efficient equipments, especially high-speed, powerful tractors and agricultural machineries. Existing agricultural methods mainly based on machines and implements with passive tools. A rapid mechanization of most of the agricultural practices is made possible with the use of tractors with mounted and trailed implements.

Traditionally, Thailand is an agricultural country; more than 68 % of its population is directly or indirectly dependent on agriculture. The total geographical area of this country is 513,115 square kilometers; of which 41 percent or (21,196,571 hectares) is devoted to agriculture. The net irrigated land is about 49000 sq. km. Land under cultivation includes 51 percent for rice production, 24 percent for field crops, and 17 percent for fruit trees and perennial crops. The total population of Thailand is 64, 631, 59 millions and it is expected to reach 70 million by the turn of decade. (Introduction to Thailand, www.hellosiam.com) Over the last four decades, agricultural production has increased significantly. However, a large increased in the production was due to the expansion of cultivated land through forest encroachment rather than increasing yield per unit area.

To meet the increasing food demand for the increasing population, can be achieved by increasing the productivity or by increasing the area under cultivation. The latter has some obvious limitations. One of the possible ways of augmenting the agricultural productivity is to practice selective mechanization in a wider scale. The farmers are conscious of this benefit. As a result, popularity of tractor is ever increasing. Agricultural tractors are designed to perform a wide range of agricultural operations like tillage, sowing, intercultural operations, harvesting and transportation. They are also used for the operations like lifting water and running a generator.

Transportation is an important component in the agricultural scenario of the country. Its role in both internal and international trade needs no elaboration. It plays also a significant part in social, economic, industrial and cultural development of a region and a country. The years ahead are likely to be more challenging ones for the transportation systems. In some parts of the country, there are accelerating demands for the extension of transportation services to meet the needs of growing population. Rural sector needs the transport where a large proportion of the food for domestic consumption and the cash crops for export are produced. The need of transportation at rural level is gaining much importance as majority of agricultural output is produced in the small farm sector.

In Thailand the transportation of commodities is done through human, animals, animal operated carts, automobiles, tractor trailers, railways, boats, cargos ships, air, ropeways etc. Mode depends upon a variety of factors. The problem of rural sector is little different.

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In the overall transportation system, road transport occupies a very important place. With growing pace of rural development, accent on increased agricultural productivity and industrialization, the demand for road transport is increasing at a phenomenal rate.

Worldwide trend showed that road transport was becoming the preferred mode for carriage of freight and passengers. Road transport system includes transportation by means of animal operated carts and automobiles (trucks and tractor-trailer combination). Even though the bullock cart is one of the major sources of transportation of commodities from one place to another in rural areas, it has some major draw backs. Its suitability is limited to smaller distances may be in order of 5-10 km. Also the tonnage carried by it is limited to a maximum of 2-3 tones, besides requiring animal power it is a slow method of transportation process.

There is an increasing trend to use the tractors more for haulage operation. It is believed that some of the customers are purchasing tractors exclusively for using in haulage operations. This is considered to be a more profitable proposition. These are used for tillage and various field operations and the next popular use is for transportation of men, animals and other materials including agriculture produce.

1.2 Problem Identification

Tractors are mainly designed for performing agricultural tillage operations. But, in view of their greater use for transportation operation it is necessary to check their suitability from the consideration of haulage operation too. One of the possible approaches is to optimize some of the operational parameters of tractor for haulage performance. The factors considered relevant in this context include load, speed of operation, hitch point height, ballasting of drive wheels, inflation pressure, slip, type of surface and type of trailer and fuel consumption.

Tractor – trailer combination played a key role in mechanization of agricultural haulage activities rapidly. Hauled tractors requires less time for different operations and also helps in altering to the peak labor demand that occurs over a relatively short period of time in each crop season. With the increasing population and its needs, agriculture too increasingly becoming competitive, it is simply good business to accomplish more work with less time and fuel by the use of sophisticated machines. To achieve this goal and to make agriculture haulage operation smooth and fast it is important to optimize the different operating parameters with a view to maximize fuel consumption and performance of haulage operation.

In, the light of the above discussion a project entitled “Simulation of Haulage Performance of 2 Wheel drive Agricultural Tractors in Visual Basic” had been taken up at Asian Institute of Technology, Bangkok, Thailand. In the view of the time constraints number of parameters were limited to five only. Fuel Consumption, because of its obvious importance was selected as the only dependent variable. The road surface was maintained constant for the investigation. With the change in tractor some parameters like performance of tractor, load, hitch point height, travel speed, inflation pressure, ballasting and fuel consumption will change. It was therefore decided to carry out experiments in two phases. In the first phase Kubota L 345 II DT tractor and in second phase John Deere 2450 tractor was used and tested with the selected variables and consideration of different parameters as well as optimization of different ballasting conditions.

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1.3 Scope of the Study

Investigation and research carried in the area of farm machinery needs gathering of information on the field to predict or actually find the performance of tractor attached to a trailed or mounted type implement. Also the study of the field research is very expensive and time consuming, agricultural computer models and simulation programs help researchers and engineers to find the relative effect of parameters on performance of the machine. Also during the curriculum students take few courses in agriculture mechanization. Thus agriculture models and simulation programs help students to analyze in detail and illustrate problems regarding tractor tire performance, soil and surface conditions, on road as well as off road performance and types of trailed or mounted implements to be used to predict the performance of agriculture machine. Beside this simulation software helps agriculture engineers to understand the performance aspects of tractor from the pure science aspects of the subject matter. An indeed simulation program helps to evaluate the tractor performance quickly by a relatively inexpensive method.

Interaction between users and computers tends to facilitate the rapid progress in developing new software in visual basic and enhancing the existing application software and programming languages. Many agricultural computer models and simulation programs have been developed to meet the ever increasing demand of considerable research work carried out to develop computer models in farm mechanization and to serve the educational and research needs.

Graphical user interface developed by visual basic helps to command and prompt easily; this makes visual basic excellent in programming and developing software. Visual basic program for tractor performance comprises a set of screens, object buttons, scroll bars and menu. The object forms and behavior are described through the use of a language scripted. Visual basic programming in C or C ++ allows us to change model parameters, add new models, save data with the help of spread sheet and printing of the results. Software developed using C or C ++ language in visual basic also helps in analyzing the factors involved and make necessary changes out of it for relative performance of a tractor and its implements and to manufacture the parts without error and make the mechanization operation steady and safe for tractor operators, designers and manufacturers. The features provided by visual basic makes the program more interactive and highly flexible that can be used in research and education. Thus visual basic is proved to be an excellent tool for developing and validating a flexible and user friendly software, and a new approach to develop a program for predicting the tractor haulage performance.

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1.3 Objectives of the Study

The main objective of this study is to simulate haulage performance of 2 WD agricultural tractors by varying haulage load, hitch point height, travel speed, rear wheel ballast, and inflation pressure in visual basic.

Specific Objectives:

1) To study effect of different levels of haulage load, travel speed, hitch point height, rear wheel ballast and inflation pressure of tractor on fuel consumption.

2) To measure the slip under the above conditions.

3) To determine the haulage parameters corresponding to minimum fuel consumption.

4) To develop software in visual basic for haulage performance to operate on minimum fuel consumption under given working conditions.

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CHAPTER II

REVIEW OF LITERATURE

Performance evaluation of tractor – trailer combination had been a subject of interest to many researchers for the past decades. An attempt has been made in this chapter to discuss the past research conducted on haulage performance having bearing on tractor- trailer performance. This chapter also describes the literature that have been cited for proper understanding and getting clear idea about the study of “Simulation of Haulage Performance of 2 Wheel Drive Agricultural Tractors in Visual Basic” Literature related to the applications of optimization, applications of optimization in farm power, effect of haulage performance parameters on fuel consumption, methods of measurement of slip, draft and speed, methods of ballasting, simulation of haulage performance and types of trailer hitches are presented in this chapter.

2.1 Basic Terminologies for Haulage Performance

1) Rated Engine Speed: Speed of engine for continuous operation at maximum power in revolutions per minute.

2) Maximum Drawbar Power: It is the maximum power that a tractor delivers at drawbar.

3) Maximum Available Drawbar Power: The average minimum retained power the tractor delivers at drawbar. Maximum available drawbar power is determined from average drawbar pull and average travel speed on the test runways.

4) Specific Fuel Consumption: It is the ratio between the volume of fuel consumed per unit time and the measured power.

5) Drawbar Pull versus Travel Speed: It is the ability of a tractor to produce drawbar pull with reduction in travel speed. It is also called as lugging ability of a tractor.

2.2 Applications of Optimization

Sowell et al. (1975) developed a mathematical programming system (MPS/360) keeping in a view the advances in the theory of mathematical programming, increased application and sophistication, including increase in problem size. This MPS/360 is a computer software package for solving linear and separable programming problems; it also provides procedures to simplify the preparation of input data and to translate output into a user oriented format. It is simple to use and claimed to be an efficient and effective package for linear and separable programming problems.

Loomba (1990) stated that there are number of optimizing methods and techniques which are helpful in solving a wide range of production and management problems. Linear programming problems of any significance are usually solved by the application of simplex method

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2.3 Applications of Optimization in Farm Power

Gee-Clough (1980) derived the empirical equations form which the tractive performance of tires in off-road conditions can be predicted. He varied the tire diameter and width on lightly loaded and heavily loaded rear axle. The effect of tire flexibility and ballasting were also demonstrated. The derived empirical equations can be used by designers to obtain above mentioned optimum parameters.

Burt et al. (1982) carried out a series of experiments to optimize the tractive efficiency of a tractor by varying wheel ballast and inflation pressure. The effects of these parameters on specific fuel consumption were also found out. Based on the results they concluded that definite improvements in efficiency and specific fuel consumption could be achieved by optimizing the tractor’s operating parameters for a particular operation and particular field condition. In this experiment the engine was operated within a limited range of 1000 to 1500 rpm. In order to evaluate both engine and tractive performance, additional static load and inflation pressure tests were done in a separate phase of this experiment using a greater range of engine power.

Mugucia et al. (1987) determined the tractor performance of front wheel assist tractor on asphalt surface by determining slip at its operating speed and relationship between performance of front and rear wheel in both 2 WD and 4 WD mode.

Saleque and Jangiev (1990) installed an instrumentation system on a 4 WD, 10 kW farm tractor to monitor some operational parameters during field work. These include drive wheel axle torque and rotational speed for predetermined tillage depths and soil conditions. They incorporated a microcomputer in the system and found out the optimal values of wheel slip, travel speed, tractive efficiency and area tilled per unit energy.

Abbouda et al. (2001) studied the effect of 3 wheel track setting, four levels of water ballast with a 2 WD tractor attached with mounted disc plough and trailed offset disc harrow. Tractor field performance improved with wider track width and high levels of water ballast. Fuel consumption was decreased up to 28% with disc plough having 25% to 50% of ballasting and track width 180 cm whereas dick harrow showed no reduction in fuel consumption. Slip was reduced from 21% to 9.04% with 0% to 75% water ballast using disk plough, whereas for harrow slip decreased by 26.7% increasing water ballast from 0% to 75%. Fuel consumption was also reduced when there was higher width track and higher water ballast with mounted implements. Field capacity was at its maximum level when track width was higher and water ballast levels were at 25% and 50% for both the implements.

Juostas and Janulevicius (2008) analyzed the tractor and engine working from economic point of view. Fuel consumption of tractor trailer combination can be reduced by reducing the throttle speed and keeping the theoretical speed constant was determined in this research study.

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2.4 Effect of Haulage Performance Parameters on Fuel Consumption

Kucera and Jamison (1965) measured the slip, rolling resistance and fuel consumption of a high horse power tractor under varying levels of drawbar loads. They investigated the operational characteristics with different type of ballasts under field conditions. They further studied the effect of larger tires on performance of high horsepower tractors on different surfaces.

Clark and Gillespie (1979) described a tractor performance meter to monitor the performance of farm tractor. The operator could adjust operating conditions to produce a high efficiency number. A fuel flow meter was connected in between the fuel tank and return line from the injector.

Anonymous (1982) reported a 10 percent change in tire rolling resistance would result in a 2 percent change in vehicle fuel consumption. The experimental approach consisted of the measurement of coefficient of rolling resistance of tires and the subsequent measurement of fuel consumption of vehicles tires. Tire effect on vehicle fuel consumption was expressed in terms of the change in tire rolling resistance.

Lyne et al. (1984) carried out a study to evaluate the variation in a specific fuel consumption resulting from changes in static load, inflation pressure and travel speed on a tractor during a field operation. Results showed that there is considerable potential for using automatic controls to improve specific fuel consumption. To ensure travel reduction at each drive wheel, the differential lock was kept engaged was kept engaged during operation. Static load and inflation pressure were the independent variables in the study. Four levels of static load ranging from 16 kN to 26 kN were used to each of four inflation pressure levels ranging from 62 kPa to 159 kPa. All 16 combinations of static load and inflation pressure were tested except when tire deflection and buckling became excessive and the operator felt that a particular condition was likely to cause the slippage on the rim.

Pang et al. (1985) gave an indirect method of measuring the fuel consumption rate of a diesel farm tractor. Exhaust gas temperature was measured in the exhaust pipe at a fixed distance from the engine. Tests showed that for a given throttle position, there was a linear relationship between fuel the fuel consumption and exhaust gas temperature.

Bheemsen (1991) carried out some experiments to optimize the operational parameters of tractor haulage performance using four wheeled trailer on tar macadam road. He studied the effects of various parameters like load, forward speed, hitch point height, inflation pressure of drive wheels and rear wheel ballasting on fuel consumption. It was observed that with the increase in load, the fuel consumption decreased to certain value and then increased. Similar trend was observed with inflation pressure, ballasting and hitch point height. He further observed that with that increase in travel speed, there was decrease in fuel consumption.

Kumar (1994) studied the effects of different levels of load, speed of operation, inflation pressure of drive wheels and ballasting of drive wheels on draft, fuel consumption and slip. He optimized the parameters such as load, travel speed, inflation pressure of drive wheels and ballasting of drive wheels for achieving maximum draft and minimum fuel consumption.

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Jenane et al. (1996) optimized tractive performance and specific fuel consumption for front wheel assist tractor. Test was conducted in field having heavy black crackling soil; the texture of soil was clay, for stubble, mould board and chisel plough surfaces. Tractor drawbar pull was measured with load cell, forward velocity and engine speed were measured by using magnetic pick up, and torque was measured by torque meter. Fuel flow meter was used to measure fuel consumption. Optimized results for the test were fuel consumption was at its optimum level when tractor was operated at its optimum level of tractive performance. Tractive efficiency was very near to 0.9, 0.8 and 0.7 respectively for stubble, mould board and chisel plough. For the specific fuel consumption dynamic traction ratio was 0.40. Minimum fuel consumption was found for slip values ranging from 10 % to 30 %.

Afonso et al. (2003) compared the fuel consumption for 89 kW tractor having FWD attached to a chisel plough with three types of tires, with and without tire ballasting and four forward speed. Investigation was carried out in a field with three replications. Fuel consumption was lowered with tire ballast, tractor equipped with radial tires and increase in forward speed.

Grisso et al. (2004) predicted the fuel consumption by developing a new fuel consumption equation using fuel consumption and power data from Nebraska tractor test laboratory. Research was carried out to optimize the fuel consumption for diesel engines, with full and part load and throttle condition reduced from full to part.

Jilek et al. (2006) optimized the fuel consumption with the effect of transport output (1/t-km) and transport performance (t/h) for tractor and trailer combination with the change in tractor engine power. For this study three tractors were considered attached with tipping trailers and run on a stretch of 2200 m. Trailer was filled with 5.2 and 10.2 tons of sand and pressure of trailer was 400 kPa, Following results were obtained from the study, lowest fuel consumption was estimated for a tractor having 50 kW rated engine power whereas highest fuel consumption was evaluated for a tractor having 114 kW rated engine power. The lowest tractor performance was found for tractor having 63 kW rated engine power and the highest tractor performance was found for tractor having 114 kW rated engine power.

Grisso et al. (2006) developed a method to predict the fuel consumption for a specific model by using NTTL and generalized model fuel consumption data for full as well as partial loads at reduced throttle condition. The equations developed for the fuel consumption can be used for different operating and loading conditions.

Ghalehjoghi and Loghavi (2007) Evaluated and compared the fuel consumption of Massey Ferguson and John Deere tractor attached to mould board for ploughing operations. Research was carried out with 2 levels of axle loads, three levels of plowing depth with 2000 and 2200 rpm for Massey and John Deere tractor respectively and a constant forward speed of 5 km/h. Results evaluated from the research that John Deere consumed more fuel as compared to Massey Ferguson and slippage was more for John Deere than Massey Ferguson.

Grisso et al. (2008) developed a method to compare fuel consumption under full and part loads with reduced throttle by using Nebraska test tractor report for a specific. Equations

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developed were used to predict fuel consumption under different operating and loading conditions. Method developed was compared with evaluated model and actual data.

Serrano et al. (2009) investigated on tractor-implement dynamics during field operations. Objective of the study was to find out the effect of liquid ballast and inflation pressure on tractor performance. Parameters considered for study was two levels of static ballasts and three levels of inflation pressure using four wheel drive 59 kW Massey Ferguson tractor. Result obtained from the test showed that liquid ballast decreased the work rate and increase the fuel consumption. Specifications of inflation pressure provided by tractor and tire manufactures were almost same.

2.5 Measurement of Slip, Draft and Speed

Zoz et al. (1970) developed a method for determining the expected drawbar pull, drawbar horse power, travel speed, and travel reduction. The performance of the tires under actual field conditions was the basis for his graphical analysis. They evaluated slip using the methods of fixed distance and fixed time as follows.

Slip=(N−n)∗100

NWhere,N = Number of wheel revolutions with loadn = Number of wheel revolutions without load

OR

Slip=(T−t )∗100

TWhere, T = Travel time with loadt = Travel time without load

Based on fixed time,

Slip=( D−d )∗100

DWhere,D = Distance travelled with loadd = Distance travelled without load

Lyne et al. (1977) designed slip meter for measuring recording and displaying instantaneous tractor wheel slip. Photo-electric transducers were used to monitor all the wheel speeds. The accuracy of the output of wheel slip displayed on an instrument panel meter was claimed to be adequate for the experimental use.

Haldar (1978) designed and fabricated a slip sensing device for tractors to display the output on a voltmeter. The wheel revolutions were converted into low voltage electrical pulses using a contact break mechanism transducer. These were then converted into voltage by means of a suitable amplifier based circuit. The comparison of forward and rear wheel voltage gave the slip values in terms of voltage.

Mohideen et al. (1979) used the slippage of a traction wheel to control the implement in order to improve the utilization of engine power. The slippage of the rear traction wheel

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with respect to the front wheel was determined by using resistor transducer. Analog output of slip activated a 12 V DC motor when the slip deviated from the desired set level.

Tolpadi (1985) used an ordinary bicycle dynamo to convert the wheel revolutions into voltage signals. The difference between the speeds of front and rear wheels was a function of the voltage obtained. The final slip value was displayed on a voltmeter in terms of voltage. Voltage generation was proportional to the speed but the cycle dynamo could not make a quick response, thus causing error in the slip measurement.

Behera (1989) made an attempt to measure instantaneous slip using photoelectric transducer and microprocessor kit. The slip was measured assuming the front wheel as a reference wheel. The system measured the slip through the front wheel as well as rear wheel accurately in the laboratory. In the field, nevertheless it did not give favorable results due to vibration of the tractor at high speeds. Further, there was difficulty in measuring slip in the daylight.

Thomson and Shinners (1989) developed an instrumentation system for pull type or three-point hitch implements which can measure speed and draft. Parameters considered for the measurement using this system were drive wheel speed, vertical and horizontal draft and true ground speed. System can calculate tractive power and slip and it can be used for any type of tractor. Force dynamometer used was design for tractors up to 80 kW having maximum draft capacity of 60 kN. They concluded from the investigation that load frame is portable and can be used precisely for draft forces between 1.5 and 35 kN.

Prasad (1990) designed and tested a microprocessor based slip sensing device. He improved the device of Behera by modifying the photo-transducer mounting bracket to reduce vibration. He also modified the software to measure the slip at high speeds.

Turner (1993) developed a portable system for measuring slip on various tractor using dual radar guns. Radar unit was used for measuring slip transmits certain radiations to the surface and in the same way receives reflected radiation from the surface. The difference in transmitted and reflected frequency gives the speed, and is also known as Doppler frequency. One radar gun is used to measure ground speed and other is used to measure wheel speed. Radar guns were installed 300 to 1200 mm away from the wheel. Radar guns used in these experiments were of 10 GHz and 24.125 GHz, having a nominal angle of 350

from horizontal surface, and produced Doppler frequencies of 26.41Hz/km/h and 26.11 Hz/km/h respectively. Tractor was operated on hard ground surface with zero pull with different velocity. Ratio of tire signal to ground speed signal was recorded to actual tire surface speed. Frequency voltage converter was used to convert frequency output to displayed voltage and then transferred to data acquisition system to calibrate.

Raheman and Jha (2006) developed a slip sensor for 2 WD tractor which is micro controller based indicating values of slip on-farm as well as tar macadam road surface. Sensor has four components: rpm, power supply, gear position, sensing of throttle position, processed data collection and visual unit. Wheel revolutions and throttle position was measured by proximity switches and potentiometer. Tractor was run on a tar macadam road surface in different gears and varying throttle position. In this investigation rear wheel as well as front wheel revolutions were measured with the help of sleep sensor. Slip sensor indicated the zero values on tar macadam road surface no load conditions whereas under

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loaded conditions the values for slip were around 2% while, on-field the slip values were 10 to 44 %.

Peca (2009) analyzed the effect of driving parameters such as forward speed, engine speed and overall efficiency. Experiments were conducted on farm and data obtained showed that increase in 10% to 20% overall power efficiency can be obtained by lowering throttle speed to 2200 rpm to 1750 rpm. Trailer type offset discs were used for the experiment; tractor throttle was adjusted to 2200/min, 1750/min and third one manual throttle. Inflation pressure of test tractor was kept 100 kPa and 70 kPa, having a static load of 52 kN. Test was conducted on 150 m and 80 m strip of soil and were replicated twice. Measured values of drawbar pull and ground speed gave the drawbar power and from drawbar power fuel consumption and overall power efficiency was calculated. Results showed that tractor running below the operated speed gave increase in overall efficiency by 10-20% and valid for 0.2 to 0.4 range of traction ratio of the tractor.

2.6 Methods of Ballasting

Ballasting is one of the most important parameters which affect the performance of a tractor. Ballast is a weight that is added to or removed form a tractor for optimization of tractor traction and stability. Mounted equipment may also be used as ballast. Ballast should be used to achieve just enough traction to transmit power to the ground without excessive slip.Ballasting operation consists of three separate processes:

1. Selection of the proper tire size.

2. Selection of the quantity of ballast weight to be used.

3. Adjustment of the ballast weight to achieve proper slip values.

Lyne et al. (1982) has conducted the field experiments at different static loads and tyre inflation pressure, keeping forward velocity constant. They reported that the values of specific fuel consumption could be attained at high levels of output power. Tractive efficiency can be optimized by selecting the appropriate dynamic load and inflation pressures. Optimization of overall tractor performance requires a control system for selecting the appropriate values for engine and traction parameters.

Nakra (1987) classified the method of ballasting into three categories. These include

(a) Draft Control

(b) Liquid Ballast

(c) Bolt-on-Weights

According to Nakra, draft control could be achieved by the draft control lever. This is used only when the implement is attached to a tractor. The depth at which the implement was to be operated could be adjusted by this lever. As the depth increased the weight transfer on to the rear wheels also increased.

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Nakra also observed that liquid ballasting of tires is a well known and widely used method of ballasting of a tractor. For this method of ballasting good and clean water should be used. This is especially true when there is no possibility of freezing, calcium chloride should be dissolved in water to form an anti – freeze solution. This should be used to prevent any damage. The commercial grade of 70 to 72 % Cacl2 is usually recommended.

Nakra further stated that the advantage of bolt-on-weights system. These include the possibility of easy removal of weights when not required, thus revealing the tractor of unnecessary weight. Over a period of long run this system could give better results in terms of fuel consumption than a tractor with liquid ballast, where weight cannot be easily adjusted. The weights used in this system usually consist of either single or double pieces of cast iron with various tapped and plan holes. These holes are meant for attaching weights to the wheel.

Clark and Vande Linde (1993) developed an automatic ballast system for a tractor, which was a micro computer based control system to fill or empty the tanks with fluid to provide appropriate ballast. The system senses weight on each tire and uses a traction model to calculate the required ballast based on soil conditions and the implements used with the tractors.

Tan et al. (1994) reported the improvement in the tractive efficiency of a 2 WD tractor using a dynamic ballasting system to control wheel slip. Weight was transferred between the front axle and rear axle by computer control transfer of water between front and rear mounted tanks. Four solenoid valves were used to control the water flow direction between the tanks. They found the dynamic ballasting clearly reduced the variation of wheel slip.

Giedra and Janulevicius (2005) investigated the interaction of tractor traction force, mass, trailer, tractor mass ratio and wheel slippage and analyzed the effect of ballast weight and wheel slip on fuel consumption on-field as well off field. Dependence of wheel slip on pull force, a weight utilization coefficient and ratio of the mass of a trailer and tractor were analyzed. Nomogram developed for the selection of ballast weight is divided into three parts i.e. lower part of Nomogram showed the effect of wheel slip on the ratio of weight of tractor and trailer, middle part of nomogram showed the dependence of tractor mass (m) made of the expression of wheel grip weight force utilization coefficient. Upper part of Nomogram shows the weight of tractor transferred to rear wheels of trailer weight.

2.7 Simulation of Haulage Performance

Crossley (1981) designed a computer program to predict vehicle performance and operational costs. In that program selected combination of number of mechanical, agricultural and economic factors concerned with the vehicle and terrain over which it was operated were considered. Three additional programs to predict vehicles performance limitations were also developed. The program results were found to be in close agreement with those of survey data obtained by the Transport and road Research Laboratory of Kenya.

Zoz (1987) predicted tractor performance of a 2 WD and 4 WD/MFWD using lotus templates on field using bias ply traction equations. To validate the software wheel slip, power, speed, pull and drawbar performance were determined.

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Agarwal (1993) simulated the tractor-implement-soil combination system to determine the performance of tractor implement combination. Theoretical models were developed with a 2 bottom mould board plough and an 11 Tyne cultivator in horizontal and vertical planes. A computer program was developed in FORTRAN 77 for tractor implement performance prediction.

Clark and Dahua (1995) developed a mathematical model to simulate tractive performance of a tractor attached with a subsoiler. Computer program in C was developed based on this mathematical model to predict dynamic load and efficiency of tractor, also the required ballast was determined for maximum tractive efficiency for each tire. Investigation results showed that tractive efficiency can be improved upto 20% by properly ballasting the tractor, by reducing the static weight and mounting the subsoiler in middle position to minimize the change in ballast.

Ravi (1998) optimized the design parameters of 2 WD tractor by computer simulation. Kinematics analysis in terms of tractor implement system parameters was developed to predict rear and front wheel reactions which could be used to determine the design parameters of tractor chassis and three-point linkage geometry. He developed a C program to find the optimum weight to power ratio for a 23 kW tractor and optimized the weight distribution on front and rear axles of tractor and also chassis dimensions.

Gupta (2000) developed software in C language for implement selection and ballast management of agricultural tractors. A field experiment was conducted with a 3-bottom 36 cm mould board plough and found that the difference between predicted and observed ballast was around 17 percent.

Al-Hamed et al. (2001) developed a computer based tractor performance program in visual C++ that predicts the performance of 2 WD and 4 WD/MFWD tractors for both bias ply and radial tires. The traction prediction equations proposed by Brixius (1987) have been used to develop a computer program for predicting the tractor performance. Tractor performance parameters such as tractive efficiency, dynamic weights on front and rear axles, motion resistance ratio, net traction ratio, wheel slip, actual speed, drawbar pull, drawbar power and dynamic traction ratio were predicted for a selected tractor. The program provides an intuitive user by linking databases such as tractor specifications, tire data (bias ply and radial), and traction equation coefficients to predict the performance of a selected tractor. The menus and object driven windows are vital in making the program relatively easy to learn and operate, compared to the program developed by using any software tool available prior to the visual programming tools. The user can access various object driven windows to edit or expand available databases.

Sahu (2001) developed a computer program in visual basic for predicting the haulage performance of agricultural tractors. The software was observed to over predict the draft (2.5 25 %) and transport productivity (10-30 %) of a 2 WD tractor when used with unbalanced and balanced trailers on different terrain surfaces. Gilandeh et al. (2007) developed a computer program in visual basic for predicting tractor field performance and fuel consumption on agricultural soil using bias or radial ply tires. Program consists of sections which have options like menu, run, select, further the section of program were divided into sub sections. Program also has options for changing the models of tractors, its parameters, and saving it in excel form. Program can be used by

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manufacturers, dealers to analyze the factors involved in tractor and implement performance on field as well as on road surface.

Pranav and Pandey (2008) developed a program in visual basic for ballast management on field through weight distribution and varying depth of operation. Tractor performance equations of ASAE and Brixius equations were used for calculating draft, traction, motion resistance ratio and slip. Based on the slip values, the program calculated the additional ballast and recommends to increase depth of operation or to increase speed of operation. Field experiments were conducted with 35 hp tractor and three different types of implement to validate the software. Instrumented tractor calculated implements draft, theoretical and actual speed and slip. Program developed in visual basic was used to optimize ballast weight distribution, forward velocity and slip in field conditions.

Sahu and Raheman (2008) predicted field performance for a 2 WD tractor and a tillage implement using decision support system and developed a program in visual basic. System provided intuitive user interface by database linking to the implement, tractor and tire specifications, soil and operating conditions, traction models constants to match the decision making and to predict the performance of the study. Research was validated by collecting data from field experiments with a 31 kW PTO power tractor and three tillage implements (mould board plough, offset disc harrow and cultivator) with different soil and operating conditions. Research evaluation found that draft and slip values were under predicted and actual field capacity was over predicted than those of observed values. However tractor with a 20 kW PTO power was properly loaded with mould board plough but over loaded with cultivator under normal conditions, whereas a 31 kW PTO power tractor was under loaded in normal soil conditions with three implements.

Catalan et al. (2008) modeled tractor performance in farm tractor design by developing a program in visual basic and written in C language, in which cone index is taken as a parameter for terrain representative. Software can predict slip in 2 WD and 4 WD tractors using any of the four models for prediction. Program uses database for operating is tractor specifications and working conditions and the result can be obtained in spread sheet format.

Kumar and Pandey (2009) developed a program in visual basic for predicting tractor drawbar performance for haulage as well as field operations for bias as well as radial ply tires. A software was developed and validated using data obtained from field experiments by using 28.8 kW PTO power tractor and different tillage implements like 2 bottom MB plough, 9 tyne cultivator and offset disc harrow in IIT Kharagpur, whereas similar data was collected for haulage performance on tar macadam road surface with a same tractor and unbalanced trailer. Wheel slip, forward velocity, draft and fuel consumption was measured by data acquisition system installed on tractor. The program developed was based on modified model predicting fuel consumption and tractive performance of the tractor for field as well as haulage performance.

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2.8 Types of Trailer Hitches

Anonymous (1963) classified the trailer hitches into seven types. He described each of the seven types as follows:

(a) Swinging drawbar: This has a high weight carrying capacity for the trailers or trailed implements. The drawbar is adjustable for height, length and offset. The clevis can be removed when using implements. For lateral adjustments five holes are provided in the drawbar frame. The hitch pin hole is adjustable outward from the PTO shaft also. Drawbar height is adjustable above the ground in four steps by inverting the clevis on the drawbar and by inverting the drawbar. This drawbar is of simple design and robust construction and fits under centre housing where it is held by four studs and nuts. It can also be left in position when the belt pulley is fitted.

(b) Extra pull hitch: This hitch allows surplus weight of heavy trailer or trailed implement to be added to the tractor rear wheels. This is further increased by transferring nearly the same weight from the front of the tractor also on the rear wheels. This improves traction, reduces tire wear and reduces fuel consumption. Operation of the extra pull hitch is done through the draft control lever.

(c) Automatic hitch: This is fitted to the drawbar frame. It gives, rigid close coupling for trailers. Very high weight transfer on to the rear wheels is achieved, for improved traction and greater output. The operator can attach or detach an implement or trailer without leaving his seat.

(d) Adjustable trailer hitch: This hitch height is adjustable and provides a variable attachment point for non – weight transferring two axle trailers. There can be as many as eight adjustable heights from ground level with this system.

(e) Telescope hitch: This multiple hitch can be used as a clevis drawbar with three length settings. It can be converted to an automatic hitch by removing the clevis and exposing the hook. The telescopic auto-hitch can also be used at varying lengths although maximum carrying capacity and weight transfer (and therefore traction) is achieved at the shortest position while in the extended position, a keeper plate must be used.

(f) Close turn hitch: Close turns in confined spaces are easily accomplished with this hitch which allows an angle of 850 between the tractor and trailer. An antifriction roller rides between the trailer drawbar and the hitch and allows easy turns between the tractor and trailer. This hitch is used with two wheel trailers which transfer weight onto the tractor rear wheels. This hitch also transfers 11% of the added weight to the tractor front wheels.

(g) Heavy duty Durban hitch for 8 and 10 ton trailers: This is of heavier construction than the standard trailer hitch and is suitable for use with heavy trailers. It is raised and lowered by the tractor hydraulic linkage and it pivots about a point under the clutch housing at which point it is bolted on – to the tractor. A mechanical transport lock is fitted which relieves the hydraulics on long hauls

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CHAPTER III

METHODOLOGY3.1 General

This chapter describes materials that were used and methodology followed to conduct experiments. Experiments were conducted in the campus of Asian Institute of Technology Bangkok, Thailand.

The present investigation is concerned with the optimization of some selected parameters related to haulage performance using a 2 Wheel Drive Tractor and a 2 wheel Trailer in visual basic. To achieve the objectives of the study, 11 experiments were carried out on a tar macadam road surface. Two different tractors were selected for this study. With the change in tractor for agricultural haulage operations there is change in hitch point height, inflation pressure, load carrying capacity etc. Hence the experiments were grouped under two major classifications according to haulage performance of tractor. Of the 11 experiments, 6 experiments relates to Kubota L 345 II DT tractor, of this 6 experiments again, the first five deals with the effect of an individual parameter on fuel consumption, keeping all the other variables constant. The 6th experiment deals with the combined effect of all selected parameters on fuel consumption. Rest of the 5 experiments was carried out in an identical way with the John Deere 2450 tractor. The procedure followed to carry out the experiments and to optimize the selected parameters in relation to minimum fuel consumption in terms of cc/ ton – km has been presented in this chapter.

3.2 Software Development and Validation

The software for calculating the tractor performance parameters was developed in Visual Basic by using C++ language. Slip was calculated using by iteration process included in the program for given tractor attached with trolley and surface condition. In order to validate the developed software, experiments were conducted on tar macadam road surface using a Kubota L 345 II DT and John Deere 2450 tractor attached with a two wheel trolley of different size. Tractor trolley was filled in with Sea sand as a load.

3.3 Selection of a Tractor and a Trailer

Investigation was carried out with two wheel drive tractor (Kubota L 345 II DT and John Deere 2450) and a two wheel trolley was selected. These are illustrated in table 3.1. The specifications of tractor and trailer have been given in Appendix A.

3.4 Selection of Tire and Inflation Pressure

The Experimental tractor Kubota L 345 DT and John Deere 2450 was fitted with 12.4 – 11/ 28 (Rear) and 7.50 – 16 (Front) and 18.6-15/30 (Rear) and 7.5 - 5/16 (Front) tires respectively. In addition other tractor was selected for study, as two tractors differ in their performance and specifications. Their detail specifications are given in appendix A. Traction and rolling resistance depend on the tire contact area with the ground surface. The two tractors were selected with a view to find out which tractor is more fuel efficient and gives maximum efficiency for haulage operation.

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3.5 Selection of other Variables and their Justification

3.5.1 Load Carried by the Trailer

For the haulage operation maximum benefit can be achieved when the trailer is full to its capacity. It is considered to have the maximum effect on fuel consumption. So, this is selected as one of the variables.

3.5.2 Inflation Pressure of Drive Wheels

Inflation pressure of drive wheels will have some effect on tractive efficiency, fuel consumption and rolling resistance. Tire effects on vehicle fuel consumption may be expressed in terms of the change in tire rolling resistance. According to a study a change of 0.001 in tire rolling coefficient resulted in a change of 1.5 cc / km fuel consumption over the high cycle. The rolling resistance is mainly due to the deformation of tire on road and dissipation of energy through impact. It depends chiefly on the nature of road surface, the nature of tires and the total weight of tractor plus the load carried in the trailer. From the consideration of safety it is essential that there should be an upper and lower limit set on tire operating pressure.It has been found out that other condition remaining the same, tire deformation on loose soil is 25 % less than of hard surface. Hence, during the exploitation of tires on road, their load carrying capacity can be increased by increasing the inflation pressure. But with the increase in inflation pressure, the contact area of tire with the road decreases. This results in the decrease of grip and increase in rolling resistance causing higher fuel consumption. When inflation pressure is reduced deformation will increase, but rolling resistance will be reduced resulting in considerable reduction in fuel consumption, So far as the inflation pressure is concerned there exists an optimum value at which the load carrying capacity will be more and fuel consumption will be less. To find out this, this was considered to be important parameter and hence included in the study.

3.5.3 Speed of Operation of Tractor

Engine rpm as well as gear position will have some effect on fuel consumption. Maximum engine rpm may not give minimum specific fuel consumption. In order to find out the optimum value of travel speed for minimum fuel consumption in cc/ton – km, this variable was in included in the study.

3.5.4 Drive Wheel Ballasting

Adding of extra weight to the rear wheels of a tractor to improve traction and reduce slip is known as ballasting. When less load is transmitted to the rear axle, rolling resistance will be more and necessary traction will not be produced for pulling the loaded trailer. In this situation the deformation being less, life of tire will be more. The reverse will happen when more load is coming on the rear axle. So, it is important to maintain the desired level of load coming on to the rear axle from the consideration of traction and low fuel consumption. This is also relevant from the consideration of the life of the tires. In view of the above, it was considered necessary to find the optimum level of extra weight to be added to the rear wheels. Accordingly ballasting was included as one of the variables.

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3.5.5 Hitch Point Height

The stability of a tractor – trailer combination mainly depends on hitch geometry during haulage operation. Variation in hitch point height cause a change in the amount of weight transferred to the rear wheels. This in turn affects the traction produced and fuel consumed. In order to avoid over turning of the tractor, it is desirable to keep the hitch point height of the trailer below the centre line of rear axle of tractor. Accordingly hitch point was considered as one of the variables that would influence the vehicle fuel consumption and was included in the study.

3.5.6 Slip

Slip is an important factor that effects the fuel consumption. As slip increase, the fuel consumption increases. For efficient operation of a tractor, the slip should be less than 10 %. So it was considered necessary to measure slip during each experiment.

3.5.7 Fuel Consumption

It was the most important dependent variable considered in the study. It was evaluated in terms of cc per ton – km. It was the main criterion for optimizing the levels of selected variables as mentioned under sections 3.4 and 3.5.1 to 3.5.53.6 Parameters Maintained Constant during the Investigation

Certain parameters were kept unchanged during research study to find out the effect of fuel consumption on them. These are given in Table 3.1.Table 3.1: List of parameters kept constant during the experiments and their relevant

particulars

Sr. No Description Particulars1 Type of Surface Tar macadam road2 Travel Distance (km) 0.7 3 Inflation pressure of front tires - Kubota L 345

II DT4.22 Kg/cm2

4 Inflation pressure of front tires – John Deere 2450

1.97 kg/cm2

5 Weight on front wheels (John Deere 2450) 239.4 kg6 Weight on Rear wheels (John Deere 2450) 180 kg7 Inflation pressure of trailer tires (Small) 2.11 kg/cm2

8 Inflation pressure of trailer tires (Big) 2.82 kg/cm2

9 Throttle Condition (Kubota L 345 II DT) Full10 Throttle Condition (John Deere 2450) Part (1500 rpm)

3.7 Experimental Set – Up

Experiment carried out for thesis work and developing a visual basic program comprises of 2 Wheel Drive tractor (Kubota L 345 II DT and John Deere 2450) and a two wheel trailers. Kubota L 345 II DT tractor was fitted with the tire size of 12.4 – 11/28 (Rear) and 7.50 – 16 (Front) and John Deere 2450 tractor was fitted with tire size 18.6-15/30 (Rear) and 7.5 - 5/16 (Front) tires. An adjustable height hitch was incorporated in the tractor (Fig. 3.2). It has distinctive feature of providing variable hitch heights. It consists of one fixed bracket

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over which one adjustable bracket moves vertically for providing variable hitch heights above the ground level. The fuel tank of tractor was topped up full. The PTO shaft of the tractor was fitted with its cover. The two wheel trailer was attached to the tractor by means of coupling the hitch beam of a trailer to hitch point through a hitch pin.

3.8 Effect of Load, Hitch Point Height, Travel Speed, Rear Wheel Ballast and Inflation Pressure on Fuel Consumption (Kubota L 345 II DT Tractor)

3.8.1 Equipment / Accessories / Facilities used

Table 3.2: List of equipments, accessories, facilities used during experiments

Kubota L 345 II DT tractorJohh Deere 2450 Tractor2 - Two wheel trailer Pressure gaugeBallasting weightsStop Watch Sea SandFront WeightsRear WeightsMeasuring cylinderReplications 2

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Figure 3.1: View of the test tractor Kubota L 345 II DT with loaded trailer

Figure 3.2: Arrangement of hitch point height for Kubota L 345 II DT Tractor (63, 53 and

43 cm)

Figure 3.3: Arrangement of hitch point height for Kubota L 345 II DT Tractor (33cm and

23 cm)

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3.8.2 Independent and Dependent Variables for Kubota L 345 II DT and John Deere Tractor

Table 3.3: Independent and dependent variables for Kubota L 345 II DT Tractor

Kubota L 345 II DT TractorDependent Variables Fuel Consumption (cc/ton-km) Slip (%)

Independent VariablesLoad

(tones)Hitch Point

Height (mm)Travel Speed (km/h)

Rear Wheel Ballast (kg)

Inflation Pressure (kg/cm2)

4.0438

430 High 4 114.1 1.2

3.53003.01562.50321.98781.47700.97300.4632

4.04

630

High 4 114.1 1.2530430330230

4.04 430

High 4

114.1 1.2High 3High 2High 1Low 4

4.04 430 High 4

391.4

1.2

338.8280.4220.3114.156.800

4.04 430 High 4 114.1

2.822.462.121.761.411.060.71

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Table 3.4: Independent and dependent variables for John Deere 2450 Tractor

John Deere 2450 TractorDependent Variables Fuel Consumption (cc/ton-km) Slip (%)Independent Variables

Load (tones)

Hitch Point Height (mm)

Travel Speed (km/h)

Rear Wheel Ballast

(kg)

Inflation Pressure (kg/cm2)

4.0000

490 High 4 180 1.26

3.48942.97782.51882.00221.49620.98560.4850

4.0000

655

High 4 180 1.26580490375274

4.0000 490

High 4

180 1.26High 3High 2High 1Low 4

4.0000 490 High 4 1801.411.060.71

3.8.3 Procedure

1) A tar macadam road was selected for the test. A stretch of 0.7 km was marked.

2) The tractor was thoroughly checked as per the standard procedure.

3) The hitch point height was adjusted to 430 mm above the ground level by means of the adjustable hitch height.

4) The trailer was loaded with 4.0438 tones of weight.

5) The trailer was hitched to the tractor.

6) The inflation pressure of drive wheels was adjusted to 1.2 kg / cm2.

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7) Ballasting weights of 114.1kg were fitted to the tractor rear wheels as shown in Figure 3.6.

As it is cumbersome and time consuming to follow liquid ballasting, the bolt – on – weight method of ballasting was followed.

8) The fuel tank was filled with diesel fuel up to the mark.

9) The tractor trailer combination was then run through the selected stretch of 0.7 km with H4 gear along the selected road surface.

10) The travel time was measured with a stop watch accurately.

11) The fuel tank was then carefully refilled up to the mark with diesel fuel with the help of a 1000 ml plastic measuring cylinder, having an accuracy of 0.05 ml.

12) The quantity of fuel consumed for the run was noted.

13) Speed of travel was calculated for the run following the standard method of dividing the distance by time.

14) Slip was calculated using the following formula :

For a fixed distance: slip(% )=(T−t )∗100

T Where,

T = Travel time with load, sec t = Travel time without load, sec

15) The same procedure was repeated with the remaining seven levels of loads for both the tractors.

16) Test was carried out using same procedure for hitch point height, travel speed, rear wheel ballast, inflation pressure for both the tractors (Kubota L 345 II DT and John Deere 2450).

3.9 Evaluation of Maximum Limit of Ballasting

In order to avoid the overloading of the tires the maximum limit of ballasting was determined. Details of theory of weight transfer on the rear wheels of the tractor are given in Appendix-D. Relevant figures were presented in section 3.9, (Figure No. 3.5 and 3.6). In that analysis, two equations were derived, One for the weight transfer due to the gradient and the other for the weight transfer due to hitching of a trailer. These are given below (Equation No. 3 and 4 of Appendix-D):

T=W V cos β (1 –BW )+ W V H 1 sin β

W−W V (1 –

BW )

T=wt (α+sin β ) H 2

W

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Substituting the values (Ref. Fig. 3.2 and Appendix-A)Wv = 1685 kg

β = sin-1 (15/100) = 8.6270

B = 893 mm W = 1945 mm

H1 = 880 mm In (3) we have, T = 152.22 kgPutting the values (Ref fig 3.3) wt = 6000 (maximum gross load capacity of trailer)

α = 0.025 (Bosch Hand book)

H2 = 630 mm (maximum hitch point height)In 4 we have, Ta = 310 kg

So the maximum weight transfer that can be expected when tractor - trailer combination is moving up a gradient is, Tt = T + Ta

= 152.22 + 310

= 462.22 kg

Weight coming on to rear wheels due to the tractor’s own weight = 1649 kg (Ref. Appendix A)Therefore, total weight on rear wheels = 462.22 + 1349

= 1811.22 kg

The load carrying capacity of two tires at 1.5 kg/cm2 is

1150 X 2 = 2300 kg

(The value of 1150 is taken from the data manual)

So the amount of tire capacity left for ballasting,

= (2300 - 2141.32)

= 488.78 kg

As a precautionary measure the maximum limit of ballasting was taken as 400 kg during the test

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Figure 3.4: Weight transfer on rear wheels when the tractor is on an upward slope (Source: Aitha Bheemsen (1991))

25

Figure 3.5: Weight transfer on rear wheels of a tractor with a loaded trailer when it is moving at an upward slope (Source: Aitha Bheemsen (1991))

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Figure 3.6: Ballasted rear wheel of Kubota L 345 II DT tractor

Figure 3.7: View of test tractor (John Deere 2450) with loaded trailer

Figure 3.8: Arrangement of hitch point height at rear of John Deere 2450 tractor (65.5 and

58 cm)

Figure 3.9: Arrangement of hitch point height at rear of John Deere 2450 tractor (49, 37.5

and 27.4 cm)

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3.10 Analysis of Data

The raw data obtained from the experiments is presented in Table B.1 to B.10 of Appendix-B. The results were statistically analyzed using sigma stat and are discussed in section 4.2 to 4.7 of chapter IV. ANOVA and coefficient of variance is also presented in Appendix-I.

3.11 Combined effect of load, hitch point height, travel speed, rear wheel ballasting and inflation pressure on fuel consumption (Kubota L 345 II DT and John Deere 2450)

The raw data obtained from this experiment were presented in table B.6 and B.11 of Appendix-B. The results were statistically analyzed and were presented in section 4.7 of chapter IV. ANOVA and coefficient of variance is discussed in Appendix I.

3.12 ANOVA for five selected Parameters

ANOVA for selected parameters for haulage performance was presented in Appendix I and results were discussed under section 4.2 to 4.7 of chapter IV.

3.13 Multiple Regression Analysis

The data was analyzed and mathematical models were developed. For fitting a multiple regression equation for the data obtained a visual basic program was written in C. The program and program windows are presented in Appendix C. The correlation coefficient and standard error of estimate for the full regression were also found. These are presented Appendix I. The general form of the equation as presented in Appendix I for two tractors are as follows:

FC = a0 + a1l + a2h + a3s + a4b + a5pFC = b0 + b1l + b2h + b3s + b5p

The order of the equation was selected based on the minimum standard error of estimate. In order to calculate the optimum values of the operational parameters fuel consumption was subjected to minima condition and the parameters were optimized using sigma stat software, the optimum values obtained were also presented in Appendix I

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Kubota L 345 II DT TractorJohn Deere 2450 Tractor

Slip (%)

Independent Variables

Dependent Variables

Simulation of Haulage Performance of 2 WD Agricultural Tractors in Visual Basic

Load (8 Levels)

Hitch Point Height

(5 Levels)

Travel Speed(5 Levels)

Rear Wheel Ballast

(7 Levels)

Inflation Pressure

(7 Levels)

Personal Computer

Fuel Consumption (CC/ton-km)

Visual Basic Programming (C) Statistical Analysis (Sigma Stat)

3.14 Flow Chart for Simulation of Haulage Performance

Figure 3.10: Flow Chart for Simulation of Haulage Performance

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CHAPTER IV

RESULTS AND DISCUSSION

4.1 General

Research objective was achieved by carrying all together 11 experiments. First 6 experiments were conducted to test the fuel consumption of Kubota L 345 II DT Tractor and remaining 5 experiments were conducted to test the fuel consumption of John Deere 2450 Tractor. Results of these 11 experiments are presented and discussed in this chapter.

4.2 Effect of Load on Fuel Consumption

The average values of fuel consumption and slip from two replications were presented in Tables B.1 and B.6 of Appendix B. The calculated values of percent slip; fuel consumption in cc/ton-km and travel speed in kmph was presented in Table 4.1 and 4.2 for two different types of tractors.

From the data of Table 4.1 and 4.2 two Graphs were plotted for load vs. fuel consumption for two different selected tractors. Figure 4.1 relates to the Kubota L 345 II DT tractor and Figure 4.2 relates to the John Deere 2450 tractor. The results were statistically analyzed and the ANOVA for result was presented in Appendix I. It appears from the ANOVA tables that the effect of load is significant at 1% level of significance for both cases.

From Figures 4.1 and 4.2 it appears that as load increases fuel consumption decreases to a certain values and then shows an increasing trend. This nature of the curves was in close agreement with that of the relevant performance characteristics of a compression ignition engine for these two variables. From the curve it is evident that there exists an optimum range of load from the consideration of fuel economy. This range of load is observed to vary from 3 tons to 4 tons.

An analysis for load vs. fuel consumption for two different tractors showed that John Deere 2450 tractor gives lower fuel consumption as compared to Kubota L 345 II DT with the same level of loads.

An equation was developed to show the relationship between load and fuel consumption for two different types of tractors. The coefficients of the two equations and the coefficient of determination were presented in Appendix-I for two types of tractors. The coefficient of determination was in the range of 0.98 and 0.97 indicating a very good fit. From the equation, it was found that bigger tire gives lower fuel consumption and is therefore, advantageous. This may be attributed to longer distance covered by the bigger tire of John Deere for same speed of operation.

30

Table 4.1: Calculated values of slip, fuel consumption and travel speed for different levels of load for Kubota L 345 II DT Tractor.

Sr. No

Load (tones)

Hitch point height(mm)

Travel speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 4.0438 430 10.34 114.1 1.2 106.08 58.242 3.530 430 10.35 114.1 1.2 103.19 38.763 3.0156 430 10.43 114.1 1.2 118.65 39.594 2.5032 430 10.40 114.1 1.2 131.42 39.435 1.9878 430 10.41 114.1 1.2 158.73 38.826 1.4770 430 10.44 114.1 1.2 247.81 39.277 0.9730 430 10.48 114.1 1.2 375.55 36.998 0.4632 430 10.51 114.1 1.2 667.70 40.60

Table 4.2: Calculated values of slip, fuel consumption and travel speed for different levels of load for John Deere 2450 Tractor

Sr. No

Load (tones)

Hitch point height (mm)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 4.0000 490 18.06 180 1.26 83.92 17.412 3.4894 490 18.58 180 1.26 90.31 14.163 2.9778 490 18.66 180 1.26 85.37 11.944 2.5188 490 18.87 180 1.26 115.25 9.055 2.0022 490 19.09 180 1.26 117.85 8.286 1.4962 490 19.17 180 1.26 184.56 5.207 0.9856 490 19.53 180 1.26 280.06 1.918 0.4850 490 19.44 180 1.26 572.91 1.91

0 0.5 1 1.5 2 2.5 3 3.5 4 4.50

100

200

300

400

500

600

700

800

f(x) = 334.15301882495 x^-0.916593143297286R² = 0.983695986831176

Load (tones)

FC

- (

cc/to

n-km

)

Figure 4.1: Effect of Load on Fuel consumption for Kubota L 345 II DT tractor

31

0 0.5 1 1.5 2 2.5 3 3.5 4 4.50

100

200

300

400

500

600

700

f(x) = 271.95918038309 x^-0.947072429467036R² = 0.976399071062729

Load (tones)

FC

- (

cc/to

n-km

)

Fig. 4.2: Effect of Load on Fuel consumption for John Deere 2450 Tractor

4.3 Effect of Hitch Point Height on Fuel Consumption

The average values of fuel consumption and slip from two replications were presented in Table B.2 and B.7 of Appendix-B for the two types of tractors. The calculated values of percent slip, fuel consumption in cc/ton-km and travel speed in km/hr were presented in Tables 4.3 and 4.4. With these data from tables 4.3 and 4.4 two graphs were plotted for hitch point height vs. fuel consumption. This is illustrated in Figures 4.3 and 4.4. Figure 4.3 relates to Kubota L 345 II DT tractor and Figure 4.4 relates to John Deere 2450 tractor. The results were statistically analyzed and two sets of ANOVA were presented in Appendix-I. It appears from the ANOVA tables that the effect of hitch point height is significant at 5 percent level of significance. It was evident from the graph that there exists a range when the fuel consumption is more economic. It was also observed from the two graphs that as hitch point height increases fuel consumption decreases to a certain value and then increases again.

An expression was developed for hitch point height vs. fuel consumption for each of the two types of tractors. The coefficients of the expression and the correlation coefficients were presented in Appendix-I. The coefficient of determination was in the order of 1 to 0.03.

Table 4.3: Calculated values of slip, fuel consumption and travel speed for different levels of hitch point height for Kubota L 345 II DT Tractor

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 630 4.038 18.27 114.4 1.2 77.79 19.852 530 4.038 18.34 114.4 1.2 79.56 19.693 430 4.038 18.34 114.4 1.2 77.79 22.214 330 4.038 18.34 114.4 1.2 70.72 19.225 230 4.038 18.42 114.4 1.2 72.48 20.83

32

Table 4.4: Calculated values of slip, fuel consumption and travel speed for different levels of hitch point height for John Deere 2450 Tractor

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 655 4 18.46 180 1.26 70.53 20.232 580 4 18.42 180 1.26 70.53 18.093 490 4 18.54 180 1.26 61.60 16.954 375 4 17.42 180 1.26 79.46 17.635 274 4 18.66 180 1.26 69.64 16.25

0102030405060708090

100

230 330 430 530 630Hitch Point Height (mm)

FC

(cc

/ton-

km)

Figure 4.3: Effect of hitch point height on fuel consumption for Kubota L 345 II DT tractor

0

20

40

60

80

100

120

274 375 490 580 655Hitch Point Height (mm)

FC

(cc

/ton-

km)

Figure 4.4: Effect of Hitch Point Height on Fuel consumption for John Deere 2450 Tractor

33

4.4 Effect of Travel Speed on Fuel Consumption

The average values of fuel consumption and slip were presented in table B.3 and B.8 of Appendix B for two types of tractors. The calculated values of percent slip, slip calculation in cc/ton-km and travel speed in km/hr are presented in Table 4.5 and 4.6. From the data of these two tables two graphs were plotted for speed vs. fuel consumption. These were presented in Figures 4.5 and 4.6. Figure 4.5 relates to Kubota L 345 II DT tractor and Figure 4.6 relates to John Deere 2450 tractor. It was observed that fuel consumption was higher for Kubota L 345 II DT tractor as compared to John Deere 2450 tractor.

The results were statistically analyzed and two sets of ANOVA are presented in Appendix-I for two types of tractor. It appears from both the graphs that as speed increases fuel consumption decreases. In this experiment the throttle position was maintained constant and the speed was varied by changing the gears. So the fuel injected into the engine cylinders would be nearly the same for different gear settings. Whereas the speed would differ because of different gear ratios, these factors could be attributed to lower fuel consumption at higher speeds.

An equation was developed between speed and the fuel consumption for both the tractors. Coefficient of determination for both tractors were presented in Appendix-I. It was observed that fuel consumption was higher for Kubota L 345 II DT tractor than for John Deere 2450 tractor; this is because of the fact that speed of John Deere tractor is more as compared to Kubota.

Table 4.5: Calculated values of slip, fuel consumption and travel speed for different levels of travel speed for Kubota L 345 II DT Tractor.

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 430 4.0438 5.10 114.4 1.2 72.48 15.522 430 4.0438 2.87 114.4 1.2 90.16 3.363 430 4.0438 2.38 114.4 1.2 104.31 0.944 430 4.0438 1.59 114.4 1.2 125.53 2.355 430 4.0438 1.08 114.4 1.2 173.26 2.96

Table 4.6: Calculated values of slip, fuel consumption and travel speed for different levels of travel speed for John Deere 2450 Tractor.

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 490 4 18.74 180 1.26 88.39 162 490 4 13.37 180 1.26 69.64 5.053 490 4 9.27 180 1.26 66.96 0.334 490 4 5.9 180 1.26 89.28 1.855 490 4 5.6 180 1.26 128.57 0.95

34

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50

20

40

60

80

100

120

140

160

180

200

f(x) = 9.5694591954092 x² − 82.289526021994 x + 243.97314100464R² = 0.96598188386246

Travel Speed (km/h)

FC

cc/

ton-

km)

Figure 4.5: Effect of Travel Speed on Fuel consumption for Kubota L 345 II DT Tractor

4 6 8 10 12 14 16 18 200

20

40

60

80

100

120

140

f(x) = 0.8721324772232 x² − 22.71090014328 x + 209.7913422062R² = 0.692639059148453

Travel Speed (km/h)

FC

cc/

ton-

km)

Figure 4.6: Effect of Travel Speed on Fuel consumption for John Deere 2450 Tractor

4.5 Effect of Rear Wheel Ballasting on Fuel Consumption

The average values of fuel consumption and slip were presented in table B.4 of Appendix B. The calculated values of percent slip, fuel consumption in cc/ton-km and travel speed in km/hr were presented in Table 4.7 and were plotted in Figure 4.4. The results were statistically analyzed and ANOVA is presented in Appendix I.

It appears from the graph that as ballasting increases fuel consumption decreases to a certain value and then increases. This may be due to the fact there is an optimum range of ballasting for optimum level of traction. When the tractor drive wheels are ballasted, the traction produced increases and there by fuel consumption decreases. But after a certain limit of ballasting besides providing necessary traction the tractor has to carry extra weight

35

with it, which is of no use in producing further traction. This extra weight may result in extra fuel consumption.

An expression was developed between ballasting and fuel consumption. The coefficients of determination and the correlation coefficients are presented in Appendix I.

Table 4.7: Calculated values of slip, fuel consumption and travel speed for different levels of drive wheel ballasting for Kubota L 345 II DT Tractor.

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 430 4.0438 18.1 391.4 1.2 76.9 19.282 430 4.0438 18.26 338.8 1.2 62.76 15.853 430 4.0438 18.22 280.4 1.2 91.93 18.134 430 4.0438 18.22 220.3 1.2 81.32 16.815 430 4.0438 18.22 114.1 1.2 80.44 17.176 430 4.0438 18.22 56.8 1.2 77.79 20.217 430 4.0438 17.83 0 1.2 79.59 30.00

0 50 100 150 200 250 30070

75

80

85

90

95

f(x) = 55.0652540660391 x^0.0821326256216275R² = 0.650926063510667f(x) = 6.91428236956172 ln(x) + 48.63078659179R² = 0.634911646854467

f(x) = 0.0003173608287 x² − 0.0513736436541 x + 80.012516733583R² = 0.883564142641573

Rear Wheel Ballast (kg)

FC

(cc

/ton-

km)

Figure 4.7: Effect of Rear wheel ballasting on Fuel consumption for Kubota L 345 II DT Tractor

36

4.6 Effect of Inflation Pressure on Fuel Consumption

The average values of fuel consumption and slip were presented in Table B.5 and B.9 of Appendix B for two types of tractors. The calculated values of percent slip, fuel consumption in cc/ton-km and travel speed in km/hr were presented in Table 4.8 and 4.9. Figure 4.8 relates to Kubota L 345 II DT tractor and Figure 4.9 relates to John Deere 2450 tractor.

It is evident from the curve that as inflation pressure increases fuel consumption decreases to a certain value and then increases again. With the increase in inflation pressure the rolling radius of tires will increase. This is likely to result in a slight increase in speed. Accordingly the fuel consumption per km will decrease. On the other hand, if the inflation pressure is increased beyond a certain limit within the working range of tires it would cause more slip and give less tractive efficiency, and thus resulting in more fuel consumption.

Two equations one for each of the two types of selected tractor were developed. The coefficient of determination and the correlation coefficient are presented in Appendix-I. The coefficient of determination is found to be in the range of 0.94 to 0.95 indicating a very good fit.

Table 4.8: Calculated values of slip, fuel consumption and travel speed for different levels of inflation pressure for Kubota L 345 II DT Tractor.

Sr. No

Hitch point height(mm)

Load (tones)

Travel Speed(km/h)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel consumption(cc/ton-km)

Slip(%)

1 430 4.0438 5.16 114.4 2.82 70.72 20.392 430 4.0438 5.16 114.4 2.46 63.64 20.393 430 4.0438 5.14 114.4 2.12 61.88 20.414 430 4.0438 5.1 114.4 1.76 76.02 20.665 430 4.0438 5.08 114.4 1.41 74.25 20.696 430 4.0438 5.03 114.4 1.06 74.25 20.947 430 4.0438 4.95 114.4 0.71 84.86 21.19

Table 4.9: Calculated values of slip, fuel consumption and travel speed for different levels of inflation pressure for John Deere 2450 tractor.

Sr. No

Hitch point height(mm)

Load (Tones)

Travel Speed(kmph)

Drive wheel

ballasting (kg)

Inflation pressure(kg/cm2)

Fuel Consumption(cc/ton-km)

Slip(%)

1 490 4 18.54 180 1.41 98.21 12.952 490 4 18.58 180 1.06 95.53 15.323 490 4 18.3 180 0.71 105.35 18.23

37

0.5 1 1.5 2 2.5 30

10

20

30

40

50

60

70

80

90

f(x) = 6.3377407139 x² − 30.086170483333 x + 102.44249891909R² = 0.706281149031666

Inflation Pressure kg/cm2

FC

(cc

/ton-

km)

Figure 4.8: Effect of Inflation Pressure on Fuel consumption for Kubota L 345 II DT Tractor

0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.590

92

94

96

98

100

102

104

106

108

f(x) = 51.020408163266 x² − 118.36326530612 x + 163.66853061225R² = 1

Inflation Pressure kg/cm2

FC

(cc

/ton-

km)

Figure 4.9: Effect of Inflation Pressure on Fuel consumption for John Deere 2450 Tractor

38

4.7 Combined Effect of Load, Hitch Point height, Travel Speed, Rear Wheel Ballast and Inflation Pressure on Fuel Consumption

The average values of the fuel consumption and slip were presented in Tables B.1 to B.9 of Appendix B. These data was analyzed using sigma stat software. The ANOVA obtained for two experiments and was presented in Appendix I for two types of tractors. From the ANOVA tables it is clear that load, hitch point height, travel speed, rear wheel ballasting and inflation pressure have significant effect on fuel consumption at 1 % to 5 % level of significance. But the interactions of any of the two parameters have no significant effect on fuel consumption.

A mathematical model was developed involving of all parameters by means of a visual basic program (Appendix C) for two types of tractors separately. The coefficient of models and coefficient of determination were presented in Appendix I. The parameters were then optimized for minimum fuel consumption using a package sigma stat. The optimized values of parameters for two types of tractors were also presented in Appendix I.

It appears from the values in Appendix I that optimum load to be carried in the trailer is nearly same for both the tested tractors. The optimum inflation pressure however was observed to be more for Kubota L 345 II DT tractor as compared to John Deere 2450 tractor. So far as hitch point height was concerned it is observed that the optimum values differ. For the Kubota L 345 II DT and John Deere 2450 tractor the values were 330 and 490 mm above the ground level respectively. The difference between the two values was 130 mm though the difference in diameters is only 30 mm. However, this value of 490 mm though high it was actually lower than the height of centre line of rear axle of the tractor. Hence no stability problem is envisaged, though it may cause little vibration in actual practice. The optimum level of travel speed was also more for John Deere 2450 tractor than Kubota L 345 II DT tractor. This may be due to the diameter factor. Fuel Consumption was less for large tire as compared to small tires as large tires helps in covering more distance as compared to the smaller ones when operated at the same gear.

4.8 Multiple Regression Analysis

The general form of the equation as presented in Appendix I for two tractors are as follows:

FC = a0 + a1l + a2h + a3s + a4b + a5pFC = b0 + b1l + b2h + b3s + b5p

The order of the equation was selected based on the minimum standard error of estimate. In order to calculate the optimum values of the operational parameters fuel consumption was subjected to minima condition. The values of the coefficients and their corresponding t values indicate that none of the parameters influence the performance to the greater extent; however for Kubota L 345 II DT tractor rear wheel ballasting is more dominant over other parameters and the parameter affecting next to rear wheel ballasting was hitch point height. For John Deere 2450 tractor travel speed is more dominant over other parameters and the parameter affecting next to travel speed was Inflation pressure.

39

Table 4.10: Multiple regression coefficients values for Kubota L 345 II DT and John Deere 2450 Tractor

Kubota L 345 II DT TractorIndependent Term

Coefficient t Value* Multiple correlation coefficient

Constant 512.956 4.889

0.872

Load (tones) -102.314 -8.788Inflation Pressure (kg/cm2)

-14.307 -0.491

Rear Wheel Ballasting (kg)

0.0309 0.193

Travel Speed (km/h)

-1.487 -0.762

Hitch Point Height (mm)

0.0190 0.0957

John Deere 2450 TractorConstant 569.046 2.460

0.825

Load (tones) -87.214 -5.669Inflation Pressure (kg/cm2)

-73.532 -0.592

Travel Speed (km/h)

-3.313 -0.826

Hitch Point Height (mm)

-0.0157 -0.0671

*Table t values at 5 % level – 2.015

40

CHAPTER V

CONCLUSIONS

Tractor is one of the major sources of transportation of commodities from one place to another in Thailand. The tractor production is also increasing at a steady rate in Thailand. The usage of the tractor is more for transportation than the other agricultural operations. The farmers can earn money by giving tractor and trailer on hire basis for transportation works. But the tractors are mainly designed for agricultural operations. So there is a great need to investigate whether the present design of tractors is adequate in terms of transportation operation. One of the possible approaches is to identify the related operational parameters and optimize them on the basis of minimum fuel consumption. Accordingly a project was undertaken with the following objectives:

1) To study the effects of haulage load, hitch point height, speed of operation, drive wheel ballasting and inflation pressure on fuel consumption.

2) To study the range of percent slip under the above conditions.

3) To determine the haulage performance parameters corresponding to minimum fuel consumption.

The field trials were carried out at Asian Institute of Technology; Bangkok, Thailand using a Kubota L-345 DT and John Deere 2450 tractor and a two wheel trailer, for test runs, a stretch of 0.7 km long tar macadam road was selected.

The experiments were done in four phases. In the first phase, the effect of each the above mentioned five independent variables on fuel consumption was evaluated using Kubota L 345 II DT tractor. In the second phase, the combined effect of all the five parameters on fuel consumption was evaluated using same tractor. In the third and fourth phases identical experiments were carried out as those of phases one and two respectively using John Deere 2450 tractor. Using the data of the results two mathematical models were developed according to the tractor used. From the models optimum values of the parameters for set of tractors for minimum fuel consumption can be ascertained.

On the basis of results of the experiments some broad conclusions are drawn. These are valid for the selected road surface and operating range of variables used.

The conclusions are as given below:

1) With the increase in load the fuel consumption (cc/ton-km) initially decreases to a certain minimum value and then shows an increasing trend

2) There is a range of hitch point height when the fuel consumption is relatively low.

3) As the speed of operation increases the fuel consumption decreases linearly.

41

4) With the increase in the level of ballasting of drive wheels, the fuel consumption initially decreases up to a certain level beyond which it increases again.

5) As the inflation pressure increases, the fuel consumption decreases to a certain value and then increases again.

6) From the mathematical models the optimum values of the parameters for the minimum fuel consumption with the Kubota L 345 II DT and John Deere 2450 tractor are evaluated as follows:

Kubota L 345 II DT Tractor:

a) Load (tones) – 4

b) Hitch point height (mm) – 330

c) Travel speed (km/h) – 5.4 (H4 Gear)

d) Ballasting (kg) – 338.8

e) Inflation pressure (kg/cm2) – 2.12

John Deere 2450 Tractor:

a) Load (tones) – 4

b) Hitch point height (mm) – 490

c) Travel speed (km/h) – 9.27 (H2 Gear)

d) Inflation pressure (kg/cm2) – 1.06

7) From the consideration of lower fuel consumption John Deere 2450 tractor is more advantageous than that of Kubota L 345 II DT tractor and is therefore recommended for use in haulage operations.

42

CHAPTER VI

RECOMMENDATIONS FOR FUTURE WORK

The present work was carried out with 2WD tractors (Kubota L-345 DT and John Deere 2450) and two wheel trailer on a tar macadam road for optimizing some selected parameters. Even though, with the present investigation encouraging results were obtained, there is enough scope for carrying out future work, these include:

1) To carry out experiments on different types of surfaces.

2) To carry out experiments on different levels of slopes.

3) To study the effect of some additional parameters like torques and pull.

4) To study the effect of air resistance and atmospheric temperature on fuel consumption.

5) To study the effect of different tire sizes on fuel consumption.

6) To find out the optimum values of the important operational parameters of different types of trailer from the consideration of fuel economy.

7) To carry out similar tests with a power tiller operated trailer.

8) To study vibration characteristics of tractor and operator.

43

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Mugucia, Steve. W., Ryo. Torisu and Junichi. Takeda (1987). Tractive performance of front wheel assist tractor on an asphalt surface. Journal of Faculty of Agriculture. Iwate University. 18(361 - 370).

Nakra, C. P. (1987). Farm Machines and Equipment. Dhanpat Rai and Sons publishing Company Ltd, New Delhi, 137-140.

Pang, S. N., C. G. Zoerrb and G. Wang (1985). Tractor monitor based on indirect fuel measurement. Transactions of ASAE, 28(4):994-998.

Peca, Jose. O., Joao M. Serrano., A. Pinheiro., M. Carvalho., M. Nunes., L. Ribeiro and F. Santos (2009). Speed advice for power efficient drawbar work. Journal of Terramechanics 47(55-61).

Pranav, P. K. and K. P. Pandey (2008). Computer simulation of ballast management for agricultural tractors. Journal of Terramechanics, 45(185–192).

46

Prasad, N. (1990). Design assembling and testing of microprocessor based slip meter. Agricultural and food engineering department, IIT, Kharagpur, (Unpublished M. Tech Thesis).

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47

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48

APPENDICES

APPENDIX AA.1 Specifications of the Tractor

Kubota L 345 II DT:

Make Kubota Model L-345 II DTType Four wheel, front wheel assist, rear wheel driven

Engine:

Make Kubota DieselType Four cylinder verticalCrank shaft Length wiseRated rpm 2800Bore and stroke 3.00” x 3.23 “ (76 mm x 82 mm)Compression ratio 21 to 1Displacement 90.7 cu in (1487 ml)Starting system 12 voltLubrication PressureAir Cleaner One paper elementFuel Filter One paper cartridgeMuffler VerticalCooling medium temperature control

One thermostat

Overall Dimensions:

Specification of Rear Tire Kubota L 345 II DT:

Tire RearSize 12.4/11-28, 6 plyMake Fire StoneNo of front and rear tires 4Wheel base (m) 1.98

49

Overall length 3375 mmOverall width 1595 mmOverall height 2100 mmTotal Weight 2456 kgWeight on front wheels 816 kgWeight on rear wheels 1649 kg

Section width (mm) 315Over all diameter (mm) 1260Rim Diameter (mm) 711.2Max Load Carrying Capacity (kg) 890 to 1510

Chassis:

Type Front wheel assistTread width 1) Rear – 46.1” (1170 mm) to 66.1 “ (1680

mm)2) Front 49.0” (1245 mm)

Wheel base 76.6 “ (1945 mm)Centre of gravity 1) Horizontal distance forward from centre

line of rear wheels – 35.2 “ (893 mm)2) Vertical distance above road way 34.6 “ (880 mm)3) Horizontal distance from centre of rear wheel tread 0” (0 mm) to the right/left

Hydraulic control system Direct engine driveTransmission Selected gear fixed ratio

Gear Speed (kmph)1 0.92 1.23 1.74 3.25 3.96 5.17 7.48 13.7

Reverse 1.6, 6.7Clutch Dual plate dry disc operated by foot pedalBrakes Wet disc operated by two foot pedals which

can be locked togetherSteering Power assistTurning radius 1) (On concrete surface with brake) Right

118 “ (3.00 m), Left 118 “ (3.00 m)2) (On Concrete surface without brake) right 150 “ (3.80 m), Left 150“ (3.80 m)

Turning space diameter 1) (On concrete surface with brake) Right 244” (6.20 m), Left 244 “ (6.20 m) 2) (On concrete surface without brake) Right 307 “ (7.80 m), Left 307 “ (7.80 m)

Power take off 540 rpm at 2532 engine rpmDraw bar height 17 in (43 cm)

50

Total Weight:

Tire ballast and weight With Ballast Without BallastRear tires No, Size, ply and psi (kpa)Ballast - Liquid -Cast iron

Two 12.4/11-28, 6, 16 (110)159 kg 213 kg

Two 12.4/11-28, 6, 16 (110)NoneNone

Front tiresNo, Size, ply and psi (kpa)Ballast- Liquid -Cast iron

Two 45 kg36 kg

TwoNone None

Static weight with operatorRearFrontTotal

1649 kg 905 kg816 kg 653 kg2465 kg 1558 kg

A.2 Specifications of the trailer used with Kubota L 345 II DT Small

Sr. No

Trailer Specifications

1 Type 2 Wheel2 Length (m) 3.363 Width (m) 1.7154 Height (m) 0.5855 Internal Length (m) 3.287 Internal width (m) 1.537 Internal Depth(m) 0.4658 Distance of Hitch Point from 2-W trailer axle (m) 3.689 Pay load capacity (tones) 510 Clearance beneath the axle (m) 0.3711 Height from floor to body (m) 0.8312 Base height when loaded (m) 0.5613 Tire Size 7.5-16, 6 ply

51

A.3 Specifications of Tractor

John Deere 2450:

Make John DeereType John Deere 2450, 4 Cylinder dieselType Farm Agricultural TractorEngine Power 69 hpWeight 3402 kg (7500 lbs)Rated rpm 2300Length (m) 3.6Width (m) 1.74Wheel Base (m) 2.22Three point hitch Cat I, IIFuel Tank (Liters) 84Forward 8

Gear Velocitym/s km/h

Low1234

0.610.921.402.02

2.213.315.067.27

High5678

2.213.305.047.19

7.9711.8818.1525.88

Bore /Stroke (Inch) 4.19x4.33 [106 x 110 mm]Displacement (ci) 239.2 [3.9 L]Cooling system Liquid Cooling systemTransmission Constant Mesh

Specifications of John Deere 2450 Tractor Rear tire

Tire Rear (Tubeless)Size 18.4/15.4-30, 8 plyMake Continental No of rear tires 2Wheel base (m) 2.22Section width (mm) 467.36Rim Diameter (mm) 762Max Load Carrying Capacity (kg) 890 to 1510

52

Specifications of John Deere 2450 Tractor Front tire

Tire Front (Tubeless)Size 7.5/5-16, 6 plyMake ContinentalNo of front tires 4Wheel base (m) 2.22Section width (mm) 190.5Rim Diameter (mm) 406.4Max Load Carrying Capacity (kg) 890 to 1510

A.4 Specifications of the trailer used with John Deere 2450 (Big)

Type Two WheelMake Week Trailer, UKLength (m) 3.79Width (m) 2.16Height (m) 0.71Internal Length (m) 3.55Internal width (m) 2.04Internal depth(m) 0.625Distance of Hitch Point from 2-W trailer axle (m)

0.37

Pay load capacity (tones) 8Clearance beneath the axle (m) 0.37Height from floor to body (m) 1.03Max Load imposed on draw bar (Tones) 2.2Max load on Rear axle (Tones) 5Type Two Wheel

A.5 Specifications of Loading Material

Material Sea Sand

Material Weight (kg) 4043.8Material Height (m) 0.65

53

APPENDIX B

A) Effect of Individual Operational Parameters on Fuel Consumption for Kubota L 345 II DT Tractor

B.1 Load vs. Fuel ConsumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting (kg)

Inflation pressure(kg/cm2)

Fuel Consumption(cc)

1 4.0438 430 243.6 114.1 1.2 3002 3.530 430 243.6 114.1 1.2 2553 3.0156 430 241.5 114.1 1.2 2504 2.5032 430 242.4 114.1 1.2 2305 1.9878 430 242.1 114.1 1.2 2206 1.477 430 241.2 114.1 1.2 2557 0.973 430 240.3 114.1 1.2 2558 0.4632 430 240 114.1 1.2 215

B.2 Hitch point height vs. Fuel ConsumptionSr. No

Gross Load(Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting

(kg)

Inflation pressure

(kg/sq.cm)

Fuel Consumption(cc)

1 4.0438 630 138 114.1 1.2 2202 4.0438 530 137.4 114.1 1.2 2253 4.0438 430 137.4 114.1 1.2 2204 4.0438 330 137.4 114.1 1.2 2005 4.0438 230 136.8 114.1 1.2 205

B.3 Travel Speed vs. Fuel ConsumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting (kg)

Inflation pressure

(kg/sq.cm)

Fuel Consumption(cc)

1 4.0438 430 137.4 114.1 1.2 2052 4.0438 430 244.2 114.1 1.2 2253 4.0438 430 330.9 114.1 1.2 2954 4.0438 430 442.5 114.1 1.2 3555 4.0438 430 648.3 114.1 1.2 490

B.4 Ballasting vs. Fuel ConsumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting (kg)

Inflation pressure

(kg/sq.cm)

Fuel Consumption(cc)

1 4.0438 430 139.2 391.4 1.2 217.52 4.0438 430 138 338.8 1.2 177.53 4.0438 430 138.3 280.4 1.2 2604 4.0438 430 138.3 220.3 1.2 2305 4.0438 430 138.6 114.1 1.2 227.56 4.0438 430 138.3 56.8 1.2 2207 4.0438 430 141.3 0 1.2 255

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B.5 Inflation Pressure vs. fuel consumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel Time for 0.7 km

(Sec)

Drive Wheel Ballasting

(kg)

Inflation Pressure (kg/cm2)

Fuel Consumption(cc)

1 4.0438 430 135.9 114.1 2.82 2002 4.0438 430 135.9 114.1 2.46 1803 4.0438 430 136.2 114.1 2.12 1754 4.0438 430 137.4 114.1 1.76 2155 4.0438 430 138 114.1 1.41 2106 4.0438 430 139.2 114.1 1.06 2107 4.0438 430 141.3 114.1 0.71 240

B.6 Combined Effect of Parameters vs. fuel consumptionFuel

Consumption (cc/ton-km)

Load (tones) Inflation Pressure (kg/cm2)

Rear Wheel Ballasting (kg)

Travel Speed (km/h)

Hitch point Height (mm)

106.08 4.0438 1.2 114.1 10.34 430103.19 3.53 1.2 114.1 10.35 430118.65 3.0156 1.2 114.1 10.43 430131.42 2.5032 1.2 114.1 10.4 430158.73 1.9878 1.2 114.1 10.41 430247.81 1.477 1.2 114.1 10.44 430375.55 0.973 1.2 114.1 10.48 430667.7 0.4632 1.2 114.1 10.51 43077.79 4.0438 1.2 114.1 18.27 63079.56 4.0438 1.2 114.1 18.34 53077.79 4.0438 1.2 114.1 18.34 43070.72 4.0438 1.2 114.1 18.34 33072.48 4.0438 1.2 114.1 18.42 23072.48 4.0438 1.2 114.1 5.1 43090.16 4.0438 1.2 114.1 2.87 430104.31 4.0438 1.2 114.1 2.38 430125.53 4.0438 1.2 114.1 1.59 430173.26 4.0438 1.2 114.1 1.08 43076.9 4.0438 1.2 391.4 18.1 43062.76 4.0438 1.2 338.8 18.26 43091.93 4.0438 1.2 280.4 18.22 43081.32 4.0438 1.2 220.3 18.22 43080.44 4.0438 1.2 114.1 18.22 43077.79 4.0438 1.2 56.8 18.22 43079.59 4.0438 1.2 0 17.83 43070.72 4.0438 2.82 114.1 5.16 43063.64 4.0438 2.46 114.1 5.16 43061.88 4.0438 2.12 114.1 5.14 43076.02 4.0438 1.76 114.1 5.1 43074.25 4.0438 1.41 114.1 5.08 43074.25 4.0438 1.06 114.1 5.03 43084.86 4.0438 0.71 114.1 4.95 430

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B) Effect of Individual Parameters on Fuel Consumption for John Deere 2450 Tractor

B.7 Load vs. Fuel ConsumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting (kg)

Inflation pressure(kg/cm2)

Fuel Consumption(cc)

1 4 490 139.5 180 1.26 2352 3.4894 490 135.6 180 1.26 2203 2.9778 490 135 180 1.26 177.54 2.5188 490 133.5 180 1.26 202.55 2.0022 490 132 180 1.26 1656 1.4962 490 131.4 180 1.26 192.57 0.9856 490 129 180 1.26 192.58 0.485 490 129.6 180 1.26 190

B.8 Hitch point height vs. Fuel ConsumptionSr. No

Gross Load(Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting

(kg)

Inflation pressure

(kg/sq.cm)

Fuel Consumption(cc)

1 4 655 136.5 180 1.26 197.52 4 580 136.8 180 1.26 197.53 4 490 135.9 180 1.26 172.54 4 375 144.6 180 1.26 222.55 4 274 135 180 1.26 195

B.9 Travel Speed vs. Fuel ConsumptionSr. No

Gross Load (Tones)

Hitch point height(mm)

Travel timefor 0.7 km

(sec)

Drive wheel ballasting (kg)

Inflation pressure

(kg/sq.cm)

Fuel Consumption(cc)

1 4 490 134.4 180 1.26 247.52 4 490 188.4 180 1.26 1953 4 490 271.8 180 1.26 187.54 4 490 426.6 180 1.26 2505 4 490 450 180 1.26 360

B.10 Inflation Pressure vs. Fuel ConsumptionSr. No

Gross Load

(Tones)

Hitch point height(mm)

Travel Time for 0.7 km (Sec)

Drive Wheel Ballasting

(kg)

Inflation Pressure (kg/cm2)

Fuel Consumption(cc)

1 4 490 135.9 180 1.26 2752 4 490 135.6 180 1.26 267.53 4 490 137.7 180 1.26 295

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B.11 Combined Effect of Parameters vs. Fuel ConsumptionFuel

Consumption (cc/ton-km)

Load (tones) Inflation Pressure (kg/cm2)

Rear Wheel Ballasting (kg)

Travel Speed (km/h)

Hitch point Height (mm)

83.92 4 1.26 180 18.06 49090.31 3.4894 1.26 180 18.58 49085.37 2.9778 1.26 180 18.66 490115.25 2.5188 1.26 180 18.87 490117.85 2.0022 1.26 180 19.09 490184.56 1.4962 1.26 180 19.17 490280.06 0.9856 1.26 180 19.53 490572.91 0.4850 1.26 180 19.44 49070.53 4.0000 1.26 180 18.46 65570.53 4.0000 1.26 180 18.42 58061.6 4.0000 1.26 180 18.54 49079.46 4.0000 1.26 180 17.42 37569.64 4.0000 1.26 180 18.66 27488.39 4.0000 1.26 180 18.74 49069.64 4.0000 1.26 180 13.37 49066.96 4.0000 1.26 180 9.27 49089.28 4.0000 1.26 180 5.90 490128.57 4.0000 1.26 180 5.60 49098.21 4.0000 1.41 180 18.54 49095.53 4.0000 1.06 180 18.58 490105.35 4.0000 0.71 180 18.3 490

57

APPENDIX C

Computer program in Visual Basic for Developing Mathematical Model

Main Code – FORM 1 – tractor information

Private Sub Cancel_Form_Click()Unload MeEnd SubPrivate Sub Combo1_Change()Combo1.AddItem (Text29.Text)End SubPrivate Sub Save_Next_Click()Dim oExcel As ObjectDim oBook As ObjectDim oSheet As ObjectDim oSaveAsFileNameDim strPath As String'Dim excelRange As Excel.Range'Dim intNewRow As Int32'Dim strNewCellAddress As String'Start a new workbook in ExcelSet oExcel = CreateObject("Excel.Application")Set oBook = oExcel.Workbooks.Add'Add data to cells of the first worksheet in the new workbookSet oSheet = oBook.Worksheets(1)oSheet.Range("A1").Value = "Model_Name"oSheet.Range("B1").Value = "Rated_PTO_Power"oSheet.Range("C1").Value = "Rated_Engine"oSheet.Range("D1").Value = "Wheel_Base"oSheet.Range("E1").Value = "Rear_Axle"oSheet.Range("F1").Value = "Front_Axle"oSheet.Range("G1").Value = "Hitch_Height_above_Ground"oSheet.Range("H1").Value = "Hitch_Distance_Rear_Axle"oSheet.Range("I1").Value = "CG_Height_Above_Ground"oSheet.Range("J1").Value = "CG_Distance_Rear_Axle"oSheet.Range("K1").Value = "No_of_Front_Tyres"oSheet.Range("L1").Value = "Sec_Width"oSheet.Range("M1").Value = "Rim_Dia"oSheet.Range("N1").Value = "Max_Load_Carry_Cap"oSheet.Range("O1").Value = "Overall_Dia"oSheet.Range("P1").Value = "No_of_Rear_Tyres"oSheet.Range("Q1").Value = "Sec_Width"oSheet.Range("R1").Value = "Rim_Dia"oSheet.Range("S1").Value = "Max_Load_Carry_Cap"oSheet.Range("T1").Value = "Overall_ Dia"oSheet.Range("U1").Value = "Theo_Speed"oSheet.Range("V1").Value = "Tract_Speed_1L"oSheet.Range("W1").Value = "Tract_Speed_2L"oSheet.Range("X1").Value = "Tract_Speed_3L"oSheet.Range("Y1").Value = "Tract_Speed_4L"oSheet.Range("Z1").Value = "Tract_Speed_1H"oSheet.Range("AA1").Value = "Tract_Speed_2H"oSheet.Range("AB1").Value = "Tract_Speed_3H"oSheet.Range("AC1").Value = "Tract_Speed_4H"oSheet.Range("A1:B1:C1:D1:E1:F1:G1:H1:I1:J1:K1:L1:M1:N1:O1:P1:Q1:R1:S1:T1:U1:V1:W1:X1:Y1:Z1:AA1:AB1:AC1").Font.Bold = True oSheet.Range("A2").Value = Text29.Text oSheet.Range("B2").Value = Text1.Text oSheet.Range("C2").Value = Text2.Text oSheet.Range("D2").Value = Text3.Text oSheet.Range("E2").Value = Text4.Text oSheet.Range("F2").Value = Text5.Text oSheet.Range("G2").Value = Text7.Text oSheet.Range("H2").Value = Text8.Text oSheet.Range("I2").Value = Text9.Text oSheet.Range("J2").Value = Text6.Text oSheet.Range("K2").Value = Text10.Text oSheet.Range("L2").Value = Text12.Text oSheet.Range("M2").Value = Text13.Text oSheet.Range("N2").Value = Text14.Text oSheet.Range("O2").Value = Text15.Text

58

oSheet.Range("P2").Value = Text16.Text oSheet.Range("Q2").Value = Text17.Text oSheet.Range("R2").Value = Text18.Text oSheet.Range("S2").Value = Text19.Text oSheet.Range("T2").Value = Text20.Text oSheet.Range("U2").Value = Text11.Text oSheet.Range("V2").Value = Text21.Text oSheet.Range("W2").Value = Text22.Text oSheet.Range("X2").Value = Text23.Text oSheet.Range("Y2").Value = Text24.Text oSheet.Range("Z2").Value = Text25.Text oSheet.Range("AA2").Value = Text26.Text oSheet.Range("AB2").Value = Text27.Text oSheet.Range("AC2").Value = Text28.Text 'Save the Workbook and Quit Excel oSaveAsFileName = App.Path & "\" & "TractorDetails.xlsx" 'oBook.SaveAs = App.Path & "\" & "TD.xlsx" 'strPath = "C:\Tractor.xls"'oBook = excelApp.Workbooks.Open(strPath, 0, False, 5, _'System.Reflection.Missing.Value, System.Reflection.Missing.Value, _'False, System.Reflection.Missing.Value, System.Reflection.Missing.Value, _'True, False, System.Reflection.Missing.Value, False)'excelRange = oSheet.UsedRange'excelRange.SpecialCells(Excel.XlCellType.xlCellTyp eLastCell).Activate()'intNewRow = excelApp.ActiveCell.Row + 1'strNewCellAddress = "A" & intNewRow'oSheet.Range(strNewCellAddress).Value = "this data goes in the first empty row"'oBook.Save'oBook.Close'oExcel.Quit' open form 2 for filling dataForm2.ShowUnload MeEnd Sub

Form 2 –trailer information

Private Sub Combo1_Change()'Combo1.List = Text29.TextEnd SubPrivate Sub Command1_Click()Form1.ShowUnload MeEnd SubPrivate Sub Command8_Click()Dim oExcel As Object Dim oBook As Object Dim oSheet As ObjectDim oSaveAsFileName 'Start a new workbook in Excel Set oExcel = CreateObject("Excel.Application") Set oBook = oExcel.Workbooks.Add 'Add data to cells of the first worksheet in the new workbook Set oSheet = oBook.Worksheets(1) oSheet.Range("A1").Value = "Trailer Model Name" oSheet.Range("B1").Value = "Trailer Length" oSheet.Range("C1").Value = "Trailer Width" oSheet.Range("D1").Value = "Trailer Height" oSheet.Range("E1").Value = "Trailer Base height" oSheet.Range("F1").Value = "Distance of hitch from 2W-trailer axle" oSheet.Range("G1").Value = "No of Tyres" oSheet.Range("H1").Value = "Section Width" oSheet.Range("I1").Value = "Rim Diameter" oSheet.Range("J1").Value = "overall Diameter" oSheet.Range("K1").Value = "Theoritical speed at rated engine speed" oSheet.Range("L1").Value = "Theoritical speed at part or full throttle" oSheet.Range("M1").Value = "loaded Material Weight" oSheet.Range("N1").Value = "loaded material type" oSheet.Range("O1").Value = "Loaded Material Height" oSheet.Range("A1:B1:C1:D1:E1:F1:G1:H1:I1:J1:K1:L1:M1:N1:O1").Font.Bold = True oSheet.Range("A2").Value = Text29.Text oSheet.Range("B2").Value = Text1.Text oSheet.Range("C2").Value = Text2.Text oSheet.Range("D2").Value = Text3.Text oSheet.Range("E2").Value = Text4.Text oSheet.Range("F2").Value = Text6.Text oSheet.Range("G2").Value = Text10.Text oSheet.Range("H2").Value = Text12.Text oSheet.Range("I2").Value = Text13.Text oSheet.Range("J2").Value = Text15.Text oSheet.Range("K2").Value = Text17.Text

59

oSheet.Range("L2").Value = Text18.Text oSheet.Range("M2").Value = Text5.Text oSheet.Range("N2").Value = Text8.Text oSheet.Range("O2").Value = Text7.Text 'Save the Workbook and Quit Excel oSaveAsFileName = App.Path & "\" & "TtrailerDetails.xlsx" 'oBook.SaveAs "C:\TD.xlsx" oExcel.QuitForm3.ShowUnload MeEnd SubPrivate Sub Command9_Click()Unload MeEnd Sub

Calculations

Option Strict OffOption Explicit OnFriend Class Form1

Inherits System.Windows.Forms.FormPrivate Sub Command1_Click(ByVal eventSender As System.Object, ByVal eventArgs As System.EventArgs) Handles

Command1.Click Dim Va, Vt As Object Dim Wp, Vf As Object Dim P, Q As Object Dim T, PTO As Object Va = Text1.Text Vt = Text2.Text Wp = Text3.Text Vf = Text4.Text P = Text5.Text Q = Text6.Text T = Label13.Text PTO = Text5.Text 'If Wh_speed.Checked = False Then 'MsgBox("Insert Values for Slip calculation!Select check box and click Calculate", vbExclamation, "Header") If Wh_speed.Checked = True Then Label1.Text = CStr(1 - (Va / Vt)) End If 'If Tp.Checked = False Then 'MsgBox("Insert Values for payload and velocity! Select check box and click Calculate", vbExclamation, "Header") If Tp.Checked = True Then Label13.Text = CStr(Wp * Vf) End If 'If TPE.Checked = False Then 'MsgBox("Insert Values for PTO Power! Select check box and click Calculate", vbExclamation, "Header") If TPE.Checked = True Then Label1.Text = CStr(T / PTO) End If 'If FEI.Checked = False Then 'MsgBox("Insert Values for Fuel Consumption! Select check box and click Calculate", vbExclamation, "Header") If FEI.Checked = True Then Label1.Text = CStr(Q \ T) End If End Sub Private Sub Command2_Click(ByVal eventSender As System.Object, ByVal eventArgs As System.EventArgs) Handles Command2.Click End End SubEnd Class

60

61

APPENDIX D

Amount of weight transfer on to the rear wheels of a tractor when it is moving at an upward slope with a loaded trailer

The maximum amount of load coming on to the rear wheels was calculated by taking into consideration the maximum load to be carried by the trailer, the type of surface as tar macadam road, the maximum permissible slope as 15 % and the coefficient of rolling resistance for the trailer as in a tar macadam road as 0.025.

a) Amount of weight transfer when tractor alone is ascending a gradient (Fig 3.4):

When the tractor is ascending a gradient the weight transfer occurs because of two effects. These are:

1) Increase in resistance due to down slope effect of its weight,2) Transfer of some weight from front to rear wheels.

Taking the moments about the front wheel contact point, the weight on rear wheel is,

QRW = WVCOSβ. (W-B) + WVH1sinβ i.e. QR = WV cos β. (1-B/W) + WV/W H1 sinβ-----------------------1

When the tractor is on a level ground, β = 0then, QR = WV/W (W-B)--------------------------------------------------2

So the weight transfer due to the gradient, T = (1) – (2)

i.e. T = WV cos β. (1-B/W) + WV/W H1 sinβ - WV(1-B/W)------3

b) Additional weight transfer when tractor is moving up a gradient with a loaded trailer (Fig 3.5)

Taking the moments about the tractor front wheel contact point, the additional weight transfer,

Ta is given by,

Ta w = (Wt α + Wt sin β ) H2

i.e. Ta = [Wt(α + sin β) H2] / W-------------------------------------------4

So the total weight transfer on to the tractor rear wheels,

= T + Ta

(Source: Aitha Bheemsen (1991))

62

APPENDIX E

A) One Way Analysis of Variance (ANOVA) – Kubota L 345 II DT

1) Load vs. fuel consumptionKruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Col 1 8 0 2.245 1.225 3.273Col 2 8 0 145.075 112.365 311.680

H = 11.294 with 1 degrees of freedom. P (est.) = <0.001 P (exact) = <0.001All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05Col 2 vs. Col 1 64.000 4.753 Yes

2) Hitch Point Height vs. Fuel ConsumptionKruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Hitch Point Height (mm) 5 0 430.000 305.000 555.000FC-cc/ton-km 5 0 77.790 72.040 78.233

H = 6.860 with 1 degrees of freedom. P (est.) = 0.009 P (exact) = 0.008All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05Hitch Point H vs. FC-cc/ton-km 25.000 3.693 Yes

3) Travel Speed vs. Fuel ConsumptionGroup N Missing Median 25% 75% Travel Speed (km/h) 5 0 2.380 1.463 3.427FC-cc/ton-km 5 0 104.310 85.740 137.463

H = 6.818 with 1 degrees of freedom. P (est.) = 0.009 P (exact) = 0.008All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05FC-cc/ton-km vs. Travel Speed 25.000 3.693 Yes

4) Rear Wheel Ballasting vs. Fuel ConsumptionKruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Rear Wheel Ballast (kg) 7 0 220.300 71.125 324.200FC-cc/ton-km 7 0 79.590 77.123 81.100

H = 1.800 with 1 degrees of freedom. P (est.) = 0.180 P (exact) = 0.209

5) Inflation Pressure vs. Fuel ConsumptionKruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Inflation Pressure (kg/sqr.cm) 7 0 1.760 1.148 2.375FC-cc/ton-km 7 0 74.250 65.410 75.578

H = 9.822 with 1 degrees of freedom. P (est.) = 0.002 P (exact) = <0.001All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05FC-cc/ton-km vs. Inflation Pre 49.000 4.427 Yes

63

B) One Way Analysis of Variance (ANOVA) – John Deere 2450

1) Load vs. Fuel ConsumptionKruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Load (Tones) 8 0 2.261 1.241 3.234FC-cc/ton-km 8 0 116.550 87.840 232.310

H = 11.294 with 1 degrees of freedom. P (est.) = <0.001 P (exact) = <0.001All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05FC-cc/ton-km vs. Load (Tones) 64.000 4.753 Yes

2) Hitch Point Height vs. Fuel Consumption:Kruskal-Wallis One Way Analysis of Variance on Ranks:Group N Missing Median 25% 75% Hitch Point Height (mm) 5 0 490.000 349.750 598.750FC-cc/ton-km 5 0 70.530 67.630 72.763

H = 6.860 with 1 degrees of freedom. P (est.) = 0.009 P (exact) = 0.008All Pair wise Multiple Comparison Procedures (Student-Newman-Keuls Method):

Comparison Diff of Ranks q P<0.05Hitch Point H vs. FC-cc/ton-km 25.000 3.693 Yes

3) Travel Speed vs. Fuel Consumption:Group Name N Missing Mean Std Dev SEMTravel Speed (km/h) 5 0 10.576 5.540 2.478FC-cc/ton-km 5 0 88.568 24.627 11.013

Source of Variation DF SS MS F P Between Groups 1 15206.880 15206.880 47.733 <0.001Residual 8 2548.662 318.583Total 9 17755.542

All Pair wise Multiple Comparison Procedures (Holm-Sidak method):Overall significance level = 0.05Comparisons for factor:

Comparison Diff of Means t Unadjusted P Critical Level Significant FC-cc/ton-km vs. Travel Speed 77.992 6.909 0.000123 0.050 Yes

4) Inflation Pressure vs. Fuel Consumption:Group Name N Missing Mean STD Dev SEMInflation Pressure (kg/sqr.cm) 3 0 1.060 0.350 0.202FC-cc/ton-km 3 0 99.697 5.076 2.931

Source of Variation DF SS MS F P Between Groups 1 14593.788 14593.788 1127.446 <0.001Residual 4 51.776 12.944Total 5 14645.564

All Pair wise Multiple Comparison Procedures (Holm-Sidak method):Overall significance level = 0.05Comparisons for factor:

Comparison Diff of Means t Unadjusted P Critical LevelFC-cc/ton-km vs. Inflation Pr 98.637 33.577 0.00000469 0.050Comparison Significant?FC-cc/ton-km vs. Inflation Pr Yes

64

APPENDIX F

A) Multiple Linear Regression Analysis – Kubota L 345 II DT

1) Load vs. Fuel Consumption:

Load (tones) = 3.404 - (0.00603 * FC-cc/ton-km) N = 8.000 R = 0.809Rsqr = 0.655 Adj Rsqr = 0.597

Standard Error of Estimate = 0.795Coefficient Std. Error t P VIF

Constant 3.404 0.443 7.684 <0.001FC-cc/ton-km -0.00603 0.00179 -3.373 0.015 1.000

Analysis of Variance: DF SS MS F P Regression 1 7.200 7.200 11.378 0.015Residual 6 3.796 0.633Total 7 10.996 1.571

The dependent variable Load (tones) can be predicted from a linear combination of the independent variables: P

FC-cc/ton-km 0.015

All independent variables appear to contribute to predicting Load (tones) (P < 0.05). Normality Test: Passed (P = 0.499)Constant Variance Test: Passed (P = 0.931)Power of performed test with alpha = 0.050: 0.710The power of the performed test (0.710) is below the desired power of 0.800.

2) Hitch Point vs. Fuel Consumption:

Hitch Point Height (mm) = -2074.273 + (33.096 * FC-cc/ton-km) N = 5.000 R = 0.803Rsqr = 0.644 Adj Rsqr = 0.525

Standard Error of Estimate = 108.928 Coefficient Std. Error t P VIFConstant -2074.273 1075.998 -1.928 0.149FC-cc/ton-km 33.096 14.205 2.330 0.102 1.000

Analysis of Variance: DF SS MS F P Regression 1 64403.903 64403.903 5.428 0.102Residual 3 35596.097 11865.366Total 4 100000.000 25000.000

The dependent variable Hitch Point Height (mm) can be predicted from a linear combination of the independent variables: pFC-cc/ton-km 0.102

The following appear to account for the ability to predict Hitch Point Height (mm) (P < 0.05): [None] Normality Test: Passed (P = 0.578)Constant Variance Test: Passed (P = 0.050)Power of performed test with alpha = 0.050: 0.346The power of the performed test (0.346) is below the desired power of 0.800.

3) Travel Speed vs. Fuel Consumption:

Travel Speed (km/h) = 6.543 - (0.0348 * FC-cc/ton-km) N = 5.000 R = 0.868Rsqr = 0.753 Adj Rsqr = 0.670

Standard Error of Estimate = 0.894 Coefficient Std. Error t P VIF

65

Constant 6.543 1.363 4.800 0.017FC-cc/ton-km -0.0348 0.0115 -3.023 0.057 1.000

Analysis of Variance: DF SS MS F P Regression 1 7.303 7.303 9.136 0.057Residual 3 2.398 0.799Total 4 9.702 2.425

The dependent variable Travel Speed (km/h) can be predicted from a linear combination of the independent variables: P

FC-cc/ton-km 0.057

The following appear to account for the ability to predict Travel Speed (km/h) (P < 0.05): [None] Normality Test: Passed (P = 0.094)Constant Variance Test: Passed (P = 0.050)Power of performed test with alpha = 0.050: 0.465The power of the performed test (0.465) is below the desired power of 0.800.

4) Rear Wheel Ballasting vs. Fuel Consumption:

Rear Wheel Ballast (kg) = 490.458 - (3.689 * FC-cc/ton-km) N = 7.000 R = 0.215Rsqr = 0.0462 Adj Rsqr = 0.000

Standard Error of Estimate = 157.874 Coefficient Std. Error t P VIFConstant 490.458 592.570 0.828 0.446FC-cc/ton-km -3.689 7.494 -0.492 0.643 1.000

Analysis of Variance: DF SS MS F P Regression 1 6039.012 6039.012 0.242 0.643Residual 5 124621.225 24924.245Total 6 130660.237 21776.706

The dependent variable Rear Wheel Ballast (kg) can be predicted from a linear combination of the independent variables: P FC-cc/ton-km 0.643

The following appear to account for the ability to predict Rear Wheel Ballast (kg) (P < 0.05): [None] Normality Test: Passed (P = 0.753)Constant Variance Test: Passed (P = 0.388)Power of performed test with alpha = 0.050: 0.064The power of the performed test (0.064) is below the desired power of 0.800.

5) Inflation Pressure vs. Fuel Consumption:

Inflation Pressure (kg/sqr.cm) = 7.048 - (0.0732 * FC-cc/ton-km) N = 7.000 R = 0.752Rsqr = 0.565 Adj Rsqr = 0.478

Standard Error of Estimate = 0.548 Coefficient Std. Error t P VIFConstant 7.048 2.083 3.384 0.020FC-cc/ton-km -0.0732 0.0287 -2.550 0.051 1.000

Analysis of Variance: DF SS MS F P Regression 1 1.955 1.955 6.504 0.051Residual 5 1.503 0.301Total 6 3.458 0.576

The dependent variable Inflation Pressure (kg/sqr.cm) can be predicted from a linear combination of the independent variables:

P

66

FC-cc/ton-km 0.051

The following appear to account for the ability to predict Inflation Pressure (kg/sqr.cm) (P < 0.05): [None] Normality Test: Passed (P = 0.677)Constant Variance Test: Passed (P = 0.602)Power of performed test with alpha = 0.050: 0.498The power of the performed test (0.498) is below the desired power of 0.800.

B) Multiple Linear Regression Analysis – John Deere 2450

1) Load vs. Fuel Consumption:

Load (tones) = 3.380 - (0.00593 * FC-cc/ton-km) N = 8.000 R = 0.813Rsqr = 0.660 Adj Rsqr = 0.604

Standard Error of Estimate = 0.773 Coefficient Std. Error t P VIF

Constant 3.380 0.430 7.854 <0.001FC-cc/ton-km -0.00593 0.00174 -3.415 0.014 1.000

Analysis of Variance: DF SS MS F P Regression 1 6.962 6.962 11.661 0.014Residual 6 3.582 0.597Total 7 10.544 1.506

The dependent variable Load (tones) can be predicted from a linear combination of the independent variables: P

FC-cc/ton-km 0.014

All independent variables appear to contribute to predicting Load (tones) (P < 0.05). Normality Test: Passed (P = 0.514)Constant Variance Test: Passed (P = 0.931)Power of performed test with alpha = 0.050: 0.718The power of the performed test (0.718) is below the desired power of 0.800.

2) Hitch Point Height vs. Fuel Consumption:

Hitch Point Height (mm) = 847.483 - (5.297 * FC-cc/ton-km) N = 5.000 R = 0.219Rsqr = 0.0478 Adj Rsqr = 0.000

Standard Error of Estimate = 172.779 Coefficient Std. Error t P VIFConstant 847.483 963.695 0.879 0.444FC-cc/ton-km -5.297 13.654 -0.388 0.724 1.000

Analysis of Variance: DF SS MS F P Regression 1 4493.455 4493.455 0.151 0.724Residual 3 89557.345 29852.448Total 4 94050.800 23512.700The dependent variable Hitch Point Height (mm) can be predicted from a linear combination of the independent variables:

P FC-cc/ton-km 0.724

The following appear to account for the ability to predict Hitch Point Height (mm) (P < 0.05): [None] Normality Test: Passed (P = 0.667)Constant Variance Test: Passed (P = 0.050)Power of performed test with alpha = 0.050: 0.050The power of the performed test (0.050) is below the desired power of 0.800.

3) Travel Speed vs. Fuel Consumption:

Travel Speed (km/h) = 18.918 - (0.0942 * FC-cc/ton-km)

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N = 5.000 R = 0.419Rsqr = 0.175 Adj Rsqr = 0.000

Standard Error of Estimate = 5.810 Coefficient Std. Error t P VIFConstant 18.918 10.766 1.757 0.177FC-cc/ton-km -0.0942 0.118 -0.799 0.483 1.000

Analysis of Variance: DF SS MS F P Regression 1 21.523 21.523 0.638 0.483Residual 3 101.266 33.755Total 4 122.789 30.697

The dependent variable Travel Speed (km/h) can be predicted from a linear combination of the independent variables: P

FC-cc/ton-km 0.483

The following appear to account for the ability to predict Travel Speed (km/h) (P < 0.05): [None] Normality Test: Passed (P = 0.445)Constant Variance Test: Passed (P = 0.050)Power of performed test with alpha = 0.050: 0.092The power of the performed test (0.092) is below the desired power of 0.800.

4) Inflation Pressure vs. Fuel Consumption:

Inflation Pressure (kg/sqr.cm) = 5.895 - (0.0485 * FC-cc/ton-km) N = 3.000 R = 0.703Rsqr = 0.495 Adj Rsqr = 0.000

Standard Error of Estimate = 0.352 Coefficient Std. Error t P VIFConstant 5.895 4.891 1.205 0.441FC-cc/ton-km -0.0485 0.0490 -0.989 0.503 1.000

Analysis of Variance: DF SS MS F P Regression 1 0.121 0.121 0.979 0.503Residual 1 0.124 0.124Total 2 0.245 0.122

The dependent variable Inflation Pressure (kg/sqr.cm) can be predicted from a linear combination of the independent variables:

P FC-cc/ton-km 0.503

The following appear to account for the ability to predict Inflation Pressure (kg/sqr.cm) (P < 0.05): [None] Normality Test: Passed (P = 0.378)Constant Variance Test: Failed (P = <0.001)Power of performed test with alpha = 0.050: <0.001The power of the performed test (<0.001) is below the desired power of 0.800.

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APPENDIX G

A) Polynomial Regression Analysis – Kubota L 345 II DT

1) Load vs. Fuel Consumption:

Order 0 Load (Tones) = 2.249 Order 1 Load (Tones) = 3.532 - (0.00538 * FC-cc/ton-km) Order 2 Load (Tones) = 5.045 - (0.0183 * FC-cc/ton-km) + (0.0000173 * FC-cc/ton-km^2) Order 3 Load (Tones) = 8.067 - (0.0572 * FC-cc/ton-km) + (0.000147 * FC-cc/ton-km^2) - (0.000000117 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 1.571 1.5711 0.526 1.0452 0.256 0.2703 0.149 0.107Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.713 14.894 0.0082 0.171 7.311 0.0433 0.0624 4.602 0.098Regression Results: OverallOrder Rsqr F P 0 0.0001 0.713 14.894 0.0082 0.883 18.936 0.0073 0.946 23.253 0.009Assumption Testing:Order Normality (P) Constant Variance (P)0 0.796 0.1821 0.600 0.7052 0.542 0.2333 0.180 0.460

2) Hitch Point Height vs. Fuel Consumption:

Order 0 Hitch Point Height (mm) = 430.000 Order 1 Hitch Point Height (mm) = -2074.273 + (33.096 * FC-cc/ton-km) Order 2 Hitch Point Height (mm) = 11098.505 - (318.817 * FC-cc/ton-km) + (2.345 * FC-cc/ton-km^2) Order 3 Hitch Point Height (mm) = 1169376.908 - (46524.921 * FC-cc/ton-km) + (616.187 * FC-cc/ton-km^2) - (2.716 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 25000.000 25000.0001 11865.366 13134.6342 17291.085 -5425.7203 20000.000 -2708.915Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.644 5.428 0.1022 0.0101 0.0586 0.8313 0.146 0.729 0.550

69

Regression Results: OverallOrder Rsqr F P 0 0.0001 0.644 5.428 0.1022 0.654 1.892 0.3033 0.800 1.333 0.454Assumption Testing:Order Normality (P) Constant Variance (P)0 0.746 0.05001 0.578 0.05002 0.265 0.05003 0.149 0.0500

3) Travel Speed vs. Fuel Consumption:

Order 0 Travel Speed (km/h) = 2.604 Order 1 Travel Speed (km/h) = 6.543 - (0.0348 * FC-cc/ton-km) Order 2 Travel Speed (km/h) = 15.193 - (0.187 * FC-cc/ton-km) + (0.000609 * FC-cc/ton-km^2) Order 3 Travel Speed (km/h) = 31.094 - (0.626 * FC-cc/ton-km) + (0.00445 * FC-cc/ton-km^2) - (0.0000106 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 2.425 2.4251 0.799 1.6262 0.156 0.6443 0.0809 0.0746Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.753 9.136 0.0572 0.215 13.420 0.0673 0.0237 2.844 0.341Regression Results: OverallOrder Rsqr F P 0 0.0001 0.753 9.136 0.0572 0.968 30.189 0.0323 0.992 39.630 0.100Assumption Testing:Order Normality (P) Constant Variance (P)0 0.439 0.05001 0.0936 0.05002 0.239 0.05003 0.295 0.0500

4) Rear Wheel Ballasting vs. Fuel Consumption:

Order 0 Rear Wheel Ballast (kg) = 200.257 Order 1 Rear Wheel Ballast (kg) = 490.458 - (3.689 * FC-cc/ton-km) Order 2 Rear Wheel Ballast (kg) = 4643.391 - (113.113 * FC-cc/ton-km) + (0.713 * FC-cc/ton-km^2) Order 3 Rear Wheel Ballast (kg) = -61793.737 + (2507.818 * FC-cc/ton-km) - (33.328 * FC-cc/ton-km^2) + (0.146 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 21776.706 21776.7061 24924.245 -3147.539

70

2 23467.384 1456.8613 27383.816 -3916.432Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.0462 0.242 0.6432 0.235 1.310 0.3163 0.0897 0.428 0.560Regression Results: OverallOrder Rsqr F P 0 0.0001 0.0462 0.242 0.6432 0.282 0.784 0.4263 0.371 0.590 0.498Assumption Testing:Order Normality (P) Constant Variance (P)0 0.745 0.2171 0.753 0.3882 0.491 0.1813 0.364 0.602

5) Inflation Pressure vs. Fuel Consumption:

Order 0 Inflation Pressure (kg/sqr.cm) = 1.763 Order 1 Inflation Pressure (kg/sqr.cm) = 7.048 - (0.0732 * FC-cc/ton-km) Order 2 Inflation Pressure (kg/sqr.cm) = -5.420 + (0.273 * FC-cc/ton-km) - (0.00238 * FC-cc/ton-km^2) Order 3 Inflation Pressure (kg/sqr.cm) = -539.083 + (22.334 * FC-cc/ton-km) - (0.304 * FC-cc/ton-km^2) + (0.00137 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 0.576 0.5761 0.301 0.2762 0.343 -0.04223 0.283 0.0600Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.565 6.504 0.0512 0.0382 0.385 0.5693 0.151 1.849 0.267Regression Results: OverallOrder Rsqr F P 0 0.0001 0.565 6.504 0.0512 0.604 3.044 0.1563 0.755 3.077 0.178Assumption Testing:Order Normality (P) Constant Variance (P)0 0.778 0.2171 0.677 0.6022 0.254 0.4383 0.580 0.905

B) Polynomial Regression Analysis – John Deere 2450

1) Load vs. Fuel Consumption:

Order 0 Load (tones) = 2.244 Order 1 Load (tones) = 3.380 - (0.00593 * FC-c/ton-km) Order 2

71

Load (tones) = 5.039 - (0.0232 * FC-c/ton-km) + (0.0000267 * FC-c/ton-km^2) Order 3 Load (tones) = 7.979 - (0.0705 * FC-c/ton-km) + (0.000224 * FC-c/ton-km^2) - (0.000000216 * FC-c/ton-km^3)

Regression Results:Order MSres MSincr0 1.506 1.5061 0.597 0.9092 0.269 0.3283 0.191 0.0780Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.660 11.661 0.0142 0.212 8.295 0.0353 0.0552 3.038 0.156Regression Results: OverallOrder Rsqr F P 0 0.0001 0.660 11.661 0.0142 0.872 17.067 0.0093 0.927 17.029 0.015Assumption Testing:Order Normality (P) Constant Variance (P)0 0.793 0.1821 0.514 0.9312 0.604 0.1603 0.511 0.0287

2) Hitch Point Height vs. Fuel Consumption:

Order 0 Hitch Point Height (mm) = 474.800 Order 1 Hitch Point Height (mm) = 847.483 - (5.297 * FC-cc/ton-km) Order 2 Hitch Point Height (mm) = -3572.112 + (120.684 * FC-cc/ton-km) - (0.892 * FC-cc/ton-km^2) Order 3 Hitch Point Height (mm) = 1696269.095 - (73063.931 * FC-cc/ton-km) + (1043.745 * FC-cc/ton-km^2) - (4.944 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 23512.700 23512.7001 29852.448 -6339.7482 41764.541 -11912.0933 2812.500 38952.041Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.0478 0.151 0.7242 0.0641 0.144 0.7413 0.858 28.699 0.117Regression Results: OverallOrder Rsqr F P 0 0.0001 0.0478 0.151 0.7242 0.112 0.126 0.7573 0.970 10.813 0.188Assumption Testing:Order Normality (P) Constant Variance (P)0 0.738 0.05001 0.667 0.05002 0.219 0.05003 0.149 0.0500

72

3) Travel Speed vs. Fuel Consumption:

Order 0 Travel Speed (km/h) = 10.576 Order 1 Travel Speed (km/h) = 18.918 - (0.0942 * FC-cc/ton-km) Order 2 Travel Speed (km/h) = -16.037 + (0.663 * FC-cc/ton-km) - (0.00385 * FC-cc/ton-km^2) Order 3 Travel Speed (km/h) = -1513.645 + (51.206 * FC-cc/ton-km) - (0.555 * FC-cc/ton-km^2) + (0.00193 * FC-cc/ton-km^3)

Regression Results:Order MSres MSincr0 30.697 30.6971 33.755 -3.0582 44.602 -10.8473 68.287 -23.684Regression Results: IncrementalOrder Rsqr F P 0 0.0001 0.175 0.638 0.4832 0.0982 0.270 0.6553 0.170 0.306 0.678Regression Results: OverallOrder Rsqr F P 0 0.0001 0.175 0.638 0.4832 0.274 0.376 0.6023 0.444 0.266 0.697Assumption Testing:Order Normality (P) Constant Variance (P)0 0.594 0.05001 0.445 0.05002 0.623 0.05003 0.339 0.0500

73

APPENDIX H

A) Multiple Linear Regression for combined effect of all parameters – Kubota L 345 II DT

Fuel Consumption (cc/ton-km) = 512.956 - (102.314 * Load (tones)) - (14.307 * Inflation Pressure (kg/cm2)) + (0.0309 * Rear Wheel Ballasting (kg)) - (1.487 * Travel Speed (km/h)) + (0.0190 * Hitch Point Height (mm))

N = 32.000 R = 0.872 Rsqr = 0.760 Adj Rsqr = 0.714

Standard Error of Estimate = 62.847

Coefficient Std. Error t P VIFConstant 512.956 104.925 4.889 <0.001Load (tones) -102.314 11.642 -8.788 <0.001 1.040Inflation Pressure (kg/cm2) -14.307 29.144 -0.491 0.628 1.116Rear Wheel Ballasting (kg) 0.0309 0.160 0.193 0.849 1.115Travel Speed (km/h) -1.487 1.951 -0.762 0.453 1.193Hitch Point Height (mm) 0.0190 0.199 0.0957 0.925 1.000Analysis of Variance: DF SS MS F P Regression 5 325764.592 65152.918 16.496 <0.001Residual 26 102692.505 3949.712Total 31 428457.096 13821.197

Column SSIncr SSMargLoad (tones) 323098.631 305059.326Inflation Pressure (kg/cm2) 330.685 951.837Rear Wheel Ballasting (kg) 2.642 146.647Travel Speed (km/h) 2296.482 2294.765Hitch Point Height (mm) 36.152 36.152

The dependent variable Fuel Consumption (cc/ton-km) can be predicted from a linear combination of the independent variables: P Load (tones) <0.001Inflation Pressure (kg/cm2) 0.628Rear Wheel Ballasting (kg) 0.849Travel Speed (km/h) 0.453Hitch Point Height (mm) 0.925

The following appear to account for the ability to predict Fuel Consumption (cc/ton-km) (P < 0.05): Load (tones) Normality Test: Failed (P = <0.001)Constant Variance Test: Failed (P = <0.001)Power of performed test with alpha = 0.050: 1.000

B) Multiple Linear Regression for combined effect of all parameters – John Deere 2450

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Fuel Consumption (cc/ton-km) = 569.046 - (87.214 * Load (tones)) - (73.532 * Inflation Pressure (kg/cm2)) - (3.313 * Travel Speed (km/h)) - (0.0157 * Hitch Point Height (mm))

N = 21.000 R = 0.825 Rsqr = 0.681 Adj Rsqr = 0.601

Standard Error of Estimate = 71.849

Coefficient Std. Error t P VIFConstant 569.046 231.301 2.460 0.026Load (tones) -87.214 15.385 -5.669 <0.001 1.183Inflation Pressure (kg/cm2) -73.532 124.223 -0.592 0.562 1.040Travel Speed (km/h) -3.313 4.013 -0.826 0.421 1.169Hitch Point Height (mm) -0.0157 0.233 -0.0671 0.947 1.002

Analysis of Variance: DF SS MS F P Regression 4 176352.894 44088.224 8.541 <0.001Residual 16 82595.877 5162.242Total 20 258948.771 12947.439

Column SSIncr SSMargLoad (tones) 171644.503 165895.815Inflation Pressure (kg/cm2) 1179.537 1808.792Travel Speed (km/h) 3505.589 3517.918Hitch Point Height (mm) 23.264 23.264

The dependent variable Fuel Consumption (cc/ton-km) can be predicted from a linear combination of the independent variables: P Load (tones) <0.001Inflation Pressure (kg/cm2) 0.562Travel Speed (km/h) 0.421Hitch Point Height (mm) 0.947

The following appear to account for the ability to predict Fuel Consumption (cc/ton-km) (P < 0.05): Load (tones) Normality Test: Failed (P = 0.003)Constant Variance Test: Failed (P = 0.021)Power of performed test with alpha = 0.050: 0.999

APPENDIX I

A) Coefficient of Expression and ANOVA tables for Kubota L 345 II DT Tractor

1) Load vs. Fuel Consumption

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Variable CoefficientConstant 5.045Load -0.0183Load2 0.0000173

Source of variation DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 7.200 7.200 11.378 0.015 5%Residual 6 3.796 0.633

Total 7 10.996 1.571

2) Hitch Point Height vs. Fuel ConsumptionVariable CoefficientConstant 11098.505Hitch Height -318.817Hitch Height2 2.345

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 64403.903 64403.903 5.428 0.102 5 %Residual 3 35596.097 11865.366

Total 4 100000.000 25000.000

3) Travel Speed vs. Fuel ConsumptionVariable CoefficientConstant 15.193Speed -0.187Speed2 0.000609

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 7.303 7.303 9.136 0.057 5 %Residual 3 2.398 0.799

Total 4 9.702 2.425

4) Rear Wheel Ballasting vs. Fuel ConsumptionVariable CoefficientConstant 4643.391Ballasting -113.113Ballasting2 0.713

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 6039.012 6039.012 0.242 0.643 5 %Residual 5 124621.225 24924.245

Total 6 130660.237 21776.706

5) Inflation Pressure vs. Fuel ConsumptionVariable CoefficientConstant -5.420Pressure 0.273Pressure2 -0.00238

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 1.955 1.955 6.504 0.051 5 %Residual 5 1.503 0.301

Total 6 3.458 0.576

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B) Coefficient of Expression and ANOVA tables for John Deere 2450 Tractor

1) Load vs. Fuel ConsumptionVariable CoefficientConstant 5.039Load -0.0232Load2 0.0000267

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 6.962 6.962 11.661 0.014 5 %Residual 6 3.582 0.597

Total 7 10.544 1.506

2) Hitch Point Height vs. Fuel ConsumptionVariable CoefficientConstant -3572.112Hitch Height 120.684Hitch Height2 -0.892

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 4493.455 4493.455 0.151 0.724 5 %Residual 3 89557.345 29852.448

Total 4 94050.800 23512.700

3) Travel Speed vs. Fuel ConsumptionVariable CoefficientConstant -16.037Speed 0.663Speed2 -0.00385

Source of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 21.523 21.523 0.638 0.483 5 %Residual 3 101.266 33.755

Total 4 122.789 30.697

4) Inflation Pressure vs. Fuel ConsumptionSource of variation

DF Sum of squares Mean sum of squares

F-Ratio P Significant level

Regression 1 0.121 0.121 0.979 0.503 5 %Residual 1 0.124 0.124

Total 2 0.245 0.122

C) Model Fitting Results for Combine Effect of all Parameters on Fuel Consumption

1) Combined Effect and ANOVA for Kubota L 345 II DT TractorVariable Coefficient Std Error T-Value P VIF

Constant 512.956 104.925 4.889 <0.001Load (tones) -102.314 11.642 -8.788 <0.001 1.040

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Inflation Pressure (kg/cm2)

-14.307 29.144 -0.491 0.628 1.116

Rear Wheel Ballasting (kg)

0.0309 0.160 0.193 0.849 1.115

Travel Speed (km/h) -1.487 1.951 -0.762 0.453 1.193Hitch Point Height (mm)

0.0190 0.199 0.0957 0.925 1.000

Source Sum of Squares DF Mean sum of squares

F-ratio P

Regression 5 325764.592 65152.918 16.496 <0.001Residual 26 102692.505 3949.712Total 31 428457.096 13821.197

2) Combined Effect and ANOVA for John Deere TractorVariable Coefficient Std Error T-Value P VIF

Constant 569.046 231.301 2.460 0.026Load (tones) -87.214 15.385 -5.669 <0.001 1.183Inflation Pressure (kg/cm2)

-73.532 124.223 -0.592 0.562 1.040

Travel Speed (km/h) -3.313 4.013 -0.826 0.421 1.169Hitch Point Height (mm)

-0.0157 0.233 -0.0671 0.947 1.002

Source Sum of Squares DF Mean sum of squares

F-ratio P

Regression 4 176352.894 44088.224 8.541 <0.001Residual 16 82595.877 5162.242Total 20 258948.771 12947.439

3) Optimal Solution for Kubota L 345 II TractorVariable Optimum Value

Load (tones) 4Hitch point height (mm) 330Travel speed (kmph) 5.4 (H4 Gear)Ballasting (Kg) 338.8Pressure (kg/cm2) 2.12

4) Optimal Solution for John Deere 2450 TractorVariable Optimum Value

Load (tones) 4Hitch point height (mm) 490Travel speed (kmph) 9.27 (H2 Gear)Pressure (kg/cm2) 1.06

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