A Virtual Engine Laboratory for Teaching Powertrain

Engineering

Burke, R.D.1, De Jonge, N.1, Avola, C.1, Forte, B.2

1. Powertrain and Vehicle Research Centre, Dept. Mechanical Engineering, University of Bath, Bath, UK

2. Dept. Electrical Engineering, University of Bath, Bath, UK

Abstract

A virtual engine laboratory application for use in automotive engineering education is proposed to allow

the practical teaching of powertrain calibration. The laboratory software is built as a flexible Matlab tool

that can easily be transferred for applications in other disciplines and promotes the link between

teaching and research.

Keywords: Virtual Laboratory, Automotive Engineering, Matlab, Design of Experiments, Diesel Engines

1 Introduction

Engineering education needs to incorporate many practical aspects that are key to the profession [1]. In

early years of engineering education, laboratory sessions can be simple to demonstrate the basic

physical principles. Small and inexpensive apparatus can be used to illustrate basic principles in in fields

such as thermodynamics, mechanics, or fluid mechanics. In later years of engineering education, for

example at master’s level, the taught concepts are more complex. The practical application of these

concepts requires larger, more sophisticated, and often expensive laboratories. A teaching example in

automotive engineering is the topic of engine controller calibration. This skill requires engineers to

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optimise the parameters of a control algorithm based on experimental data measured from an engine or

vehicle test facility. However, the use of such a test facility for education purposes is prohibitively

expensive and impractical for most universities. The result is that the education resorts to class based

activities which fail to stimulate higher levels of learning and, in the worst cases, only encourage

memorizing without promoting understanding [2].

The aim of this paper is to create a virtual laboratory application for use in automotive engineering

education and demonstrate how it can be used to improve teaching of the subject of engine calibration.

2 Background

2.1 Learning objectives in powertrain calibration

The topic of powertrain calibration is an example of engineering practice in the field of automotive

engineering. The task requires the use of design of experiments (DoE), experimental data capture and

processing, mathematical regression modelling, and optimisation techniques [3-5]. Students

participating in this course have a background in basic control theory, but for many this will represent

the first time they are exposed to its application to a real system. This application could be considered a

threshold concept that is difficult to teach without practical experience [6].

The motivation for implementing the virtual engine test laboratory stems from a historical analysis of

assessment formats and student feedback for a master’s level course. This course format was fully

lecture-based with the contents composed primarily of definitions and paper based worked examples.

Review of the course highlighted an emphasis on “remembering” as the major learning activities. This is

located in the knowledge or remembering level of Bloom’s Taxonomy and crucially misaligned with the

intended learning outcomes (ILOs) of this master’s level course which requires students to “analyse”.

With this format, it is not possible to achieve the higher levels of learning without allowing the students

put theory into practice [7, 8]. In fact, analysis of the preceding 5 years’ examination showed that 60-

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80% of marks were awarded for simply describing aspects introduced within the lecture material . To

create an environment where students can do this, the activities and assessments need to be aligned

with the application of the methods.

Ideally, each student would be able to apply the engineering theory on a real engine test facility,

spending many hours practicing developing their understanding. However, the cost of such facilities as

well as all the overhead knowledge in running a full powertrain test facility are prohibitive to this option.

A virtual laboratory approach was therefore chosen.

2.2 Review of virtual laboratories

Using virtual laboratories has been shown to be effective in all but the youngest of learners [9]. The key

shortfall is that some concepts need to be experienced to be fully accepted and understood. A good

example of this is the boiling of water at temperature below 100oC at lower pressures.

Studies of virtual laboratories have shown that if the experience is sufficiently realistic then the benefit

is like that of the equivalent real laboratory [7]. This is particularly the case if the virtual laboratory can

provide sufficient levels of realism [8] and avoid deterring students through unfriendly programming

environments [6].

Three categories of virtual laboratories can be found in the literature:

1. Virtual reality laboratories which emulate part or all the laboratory environment. These tools

can be used alone or in combination with real laboratory sessions and examples include the

LabSkills chemistry e-learning tools [1] and a geology based laboratory from the University of

Arizona [9].

2. Laboratories where the session is conducted using only part of the experimental equipment

with a computer simulation providing the rest. This approach is still conducted in a laboratory

setting, but reduces the overall equipment costs. Examples from the literature include an engine

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calibration lab at the University of Bradford [10] and a cruise control lab at the University of

Michigan [11]. Although these examples can be classified as virtual laboratories, they still

require a dedicated laboratory space and equipment which restricts student access to the

learning environment. Such laboratories can also be adapted into remote laboratories, where

the students can operate the equipment remotely via internet link.

3. Fully software based virtual labs which provide a computer based interaction with a simulation

model. Racing Academy [12] is an example of such a lab currently widely used, but is

constructed as a game and therefore does not give students the laboratory feel. A gas turbine

example can also be found in the literature [13].

The laboratory types 1 and 3 above have the advantage of being software based and as such have the

opportunity to be changed into fully online activities, subject to the computer overhead that they

require.

There are few examples of virtual engine laboratories in the literature, most probably because the

creation of the engine models required for these are themselves a topic of research or commercial tools

[14, 15]. However, the topic is gaining popularity and universities are needing to respond to a demand

from industry for expertise in this area [16].

The virtual laboratory from the University of Bradford [10] is a semi-virtual lab and still takes place in a

laboratory environment. A real engine controller is used, but linked to a specialist computer which hosts

the real-time engine model. The laboratory in fact represents some real installations at automotive

manufacturers who use hardware in the loop approaches to develop their control strategies [17]. The

advantage here is that the students are still in a laboratory environment and have to engage with some

degree of real hardware. However, the downside is that as a laboratory facility is still required and

student access is consequently limited.

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2.3 The suitability of a virtual laboratory for powertrain engineering

The experimental aspects of powertrain calibration are typically conducted on an engine test facility.

The test facility itself comprises of a test cell which includes the engine linked to a host computer system

that drives the test cell and records the data (see Figure 1). When the test cell is operational, the

engineer’s role is primarily interacting with the computer screen to set the operating conditions

according to a test plan and record data. On most facilities, there are safety systems in place that will

shut down the facility in the case of dangerous running conditions.

Figure 1: Typical layout of an internal combustion engine laboratory facility

In this work, it is recognised that there is an opportunity to recreate the engineer’s experience (the host

system interface) without the need for a real engine test facility. By replicating the computer based

user-interface and linking it to a mathematical model of the engine, the engineering tasks can be

performed with no cost on any computer. This will enable significant student access to such facilities to

put powertrain calibration theory into practice. The on-demand availability of a virtual laboratory will

encourage both independent and peer-supported learning [10, 13, 18]. Laboratory sessions will no

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longer be constrained to set timetabled periods and locations allowing both on- and off-campus

learning. The computer models required for this configuration are readily available within the research

groups providing the teaching and therefore this approach also creates a natural exchange platform

between research and teaching.

It is further recognised that this configuration of test facility host system is not unique to engine test

facilities. The user interface could be created in a flexible way to allow it to be applied to other

applications across different disciplines.

This work therefore aims to create a generic user interface that can be linked to mathematical models of

engineering and science systems to provide students with the experience of operating sophisticated

experimental equipment on any desktop computer.

3 Virtual lab description

3.1 Real engine laboratories

Test facilities are common in industry and universities to evaluate the performance of engine systems.

They are designed to measure the behaviour of the engine without the need for a full vehicle. This gives

more control over the testing but also allows engines to be developed concurrently with the vehicle.

A typical test facility is shown in Figure 2. The engine is used without the gearbox, drivetrain or vehicle

and its output shaft instead drives and dynamometer (motor/generator). The dynamometer is used to

brake the engine and thus replicate the resistance friction and inertia forces of a vehicle. The

dynamometer can be controlled to maintain a target rotational speed and will absorb or provide power

to maintain that speed. The amount of power the motor needs to absorb depends on how hard the

engine is working which is adjusted by actuating the accelerator pedal (just like when the engine is in a

car).

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The test rig is linked to a computer system known as the host system which controls the dynamometer

speed and accelerator pedal, but also:

- Controls the cooling fans and cooling water flows,

- Records data from instrumentation installed on the engine,

- Communicates with the engine’s controller to modify set-points such as the timing of

combustion, the opening of exhaust gas recirculation valves and the operating of the

turbocharger.

Figure 2: Engine test cell layout

It is the host system that is of key interest for the virtual laboratory as this is the interface between the

engineer and the test rig. The host system displays a graphical user interface (GUI) comprising the

following elements (an example is shown in Figure 3):

- Buttons to switch test bed systems on/off (dynamometer, fuel supply, cooling fans…);

- A live stream of measured values from the various sensors;

- Dials and gauges to monitor key engine operating conditions;

- Oscilloscopes for observing time history of selected data channels;

- Features for logging data to a data file;

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- Alarms that alert the user to certain conditions of the test cell (such as engine too hot…).

Figure 3: Typical user interface for the host system

3.2 Interface construction

The Mathworks Matlab was chosen as the environment to create the user interface. This was selected

because it is a universal tool used across disciplines and widely available within universities. In addition,

many of the graphical components already exist within Matlab such as buttons, graphs, and data

storage. Matlab is also a common environment for computational models of systems used for research

which are another key input to the virtual laboratory. By hosting the user interface in Matlab, this will

ease the linkage to the models. Finally, this will encourage students to engage with this universal tool to

develop their coding abilities and to make contributions to virtual lab.

To facilitate the use of the tool across disciplines, the user interface has been built as a library of

software components that can easily be arranged by an intermediate-level programmer to create new

interfaces for future virtual laboratory applications. The structure of the virtual laboratory environment

is illustrated in Figure 4 which has been constructed to promote future uses. The base interface

components are stored and documented as programming objects that can easily be personalised for

future applications. The actual application of the virtual engine laboratory is stored as a case study and

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application example to inspire future users. The core user interface and application were created by two

mechanical engineering undergraduate students with an interest in computer programming.

Figure 4: Programming Structure of the Virtual Laboratory Environment

Figure 5 shows some example screen from the user interface that have been designed to mimic the

screens from the test cell interface shown in Figure 3. The user interface interacts with the engine

simulation model which has minimal modifications compared to the research version. In fact, the model

will run without the user interface, allowing it to be updated independently to provide future features.

The model and its interaction with the GUI will be detailed in the following section.

The user interface exists as a script that the students must run in Matlab. Specific guidance for installing

and launching the script and once activated, the student work only with the GUI. In this way, the code is

openly available for students to explore without deterring student who have less interest in computer

programming.

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Cell Services Page Engine Control input page

Measurements page Oscilloscope page

Figure 5: Screenshots from the virtual laboratory

3.3 Engine Model

The engine model is issued from a number of sub-models which originate from research activities. The

full engine model is a combination of physics based and empirical models that capture different aspects

of engine operation. For the virtual laboratory application, the final choice of model type for each

component was a compromise between:

- Model availability: it must be available for open distribution to students and not be protected by

commercial restrictions. The model must also be able to run without costly software licenses on

all computers to allow full and unlimited access to the tool;

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- Computational effort: the model must be able to run faster than real-time on a standard

desktop computer;

- Accuracy: the absolute accuracy is of less importance than the model exhibiting correct trends.

This ensures that the model maintains a good level of realism and allows students to explore

topics taught across the automotive engineering degree, such as combustion effects.

An overview of the engine model is shown in Figure 6. This consists of the following sub-models which

have been combined mathematically to simulation the complete engine:

1. A semi-physical model of the turbocharger [19],

2. A mean value engine model describing the flow of air, burning of fuel and creation of torque in

the engine cylinders [20]. The mean value model is built as neural networks fitted to data issued

from a 1D gas dynamics model of the engine,

3. Dynamic polynomial or neural network models of the emissions formation in the cylinder [21,

22],

4. Physical models off the intake and exhaust manifolds as single control volumes [23].

5. Simple heat loss model of the intercooler.

Figure 6 also highlights the information that is exchanged between the different sub-models. The engine

model required simplification to reduce the calculation times such that is ran fast enough on a standard

desktop machine. This is a vital requirement for the students to have the perception of running a real

laboratory. The run time was improved by replacing differential equations describing the engine

operation for every degree of engine crank revolution with look-up tables describing the average

behaviour over two full revolutions. These look-up tables were constructed as neural networks. The

neural networks were fitted to data from a higher order mathematical model which was too slow for

this application and required specialist software licensing.

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Figure 6: Outline of the engine model showing the main parts.

The engine model uses look up tables to predict the mass flow in and out of the cylinders (meng ¿ based

on the intake pressure (P3) and temperature (T 3), exhaust pressure (P4), and engine rotational speed (

N eng). The engine model also predicts the exhaust temperature (T 4), torque and emissions based on the

mass flow and the combustion settings provided from the user interface. The emissions look-up

functions were derived and combined from a range of different engine models:

- Pilot combustion model from results presented by Tanka et al. [24],

- Soot model from Grahn et al. [22],

- Diesel injector characteristics from Dowell [25],

- NOx model in low speed/torque region [23] and high speed region [24].

The turbocharger model is composed of a compressor map, turbine map and shaft model. The shaft

model calculates the rotational speed of the turbocharger based on a power balance (equation 1).

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d NTC=PT−PCNTC

dt1

The compressor and turbine look-up tables (maps) contain mass flows (mC and mT) and isentropic

efficiencies (ηC and ηC) as a function of pressure ratio and turbocharger speed. These maps allow the

prediction of compressor and turbine power and compressor and turbine outlet temperatures (T 2 and

T 5 respectively). This is illustrated for the compressor through equations 2 and 3.

T 2=T 1(1+ (P2P1 )γ−1γ −1

ηC)

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PC=mC c p (T2−T 1 ) 3

The intercooler is a simple heat loss model based on a thermal energy balance to calculate the inlet

manifold temperature (T 3, see equation 4). The external heat loss (P IC¿is imposed by the user interface

and the pressure drop in the intercooler is neglected (meaning P3=P2).

T 3=T 2−PICmC c p

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The intake and exhaust manifolds act as control volumes which can accumulate mass based on the flows

into and out of them. Their temperature is imposed by the gas entering the manifold (T 3for inlet

manifold and T 4 for exhaust). The total mass accumulated in the manifolds is determined from equation

5 and 6. The pressures P3 and P4 are then calculated using the perfect gas law equation 7.

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d m¿=( mC−meng )dt 5

d mex=(meng−mT )dt 6

Pi=miRT iV i

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All the models are linked to the user interface to supply data to replicate measurements that would be

taken on the test rig. By combining these different models, the students can explore these mathematical

formulations which, although not the core topic of this laboratory, will be useful for them across their

degree programme. These mathematical formulations that can be used as templates for future work.

The simplification of the models ultimately resulted in a compromise of the model accuracy. However,

high precision of the outputs is not vital for a teaching environment, and the most important

requirement is that the model behaves in a realistic way. Figure 7 compares the virtual laboratory

prediction to published results from experimental work on Diesel engines operating at a similar

condition. Apart from Figure 7b, the virtual lab captures the trend of engine behaviour well, even if

there are clear differences in the absolute magnitude. For Figure 7b, although the shapes of the curves

are not comparable, the trend for increased specific fuel consumption with increased EGR rates is

maintained which is acceptable for this teaching application.

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(a) (b)

(c) (d)Figure 7: Model prediction of specific fuel consumption and specific NOx emissions compared to measured data for different injection timings and EGR rates. The injection timings are compared to data from a similar sized engine [26] and the data for EGR rate from a larger engine [27], both operating at similar speed and specific load points.

One of the major drawbacks of virtual laboratories is the lack of realism which can be off-putting for

students. Some of this realism can be addressed by the way the virtual laboratories are used within the

course and this will be addressed in the following section. However, some of the realism is inherent to

the software model and will be discussed here.

Most simulation models are deterministic, meaning that for a given set of initial and boundary

conditions, the model will calculate the same outcome every time the model is run. This is the case of

the models used in this application. However, experimental work always includes a degree of

randomness due to:

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- Random variation and error in the control and instrumentation equipment,

- Time based evolution of the test piece that is not typically captured by simulation models.

These variations are a key aspect of engineering education in the early years where much time is spent

teaching students that with experimental work there is no single, precise, and specific correct answer.

However, with a virtual laboratory, this exact answer may well seem to exist. To improve the realism,

random variation from the sensors was included into the virtual laboratory. This was included as an

addition to the model rather than in the user interface by mean of a random noise added to the model

signal. The amplitude of the noise was chosen to be of similar order of magnitude to the uncertainty of

the measurement equipment typically used in the real test cell. Uncertainty in the actuators was not

considered here, but could be introduced in a similar way to the model inputs.

4 Virtual model use in student assessment

The virtual engine laboratory was used as part of a coursework assessment as part of a master’s level

course. The students were given 6 weeks to complete the task in their own time, being able to access

the virtual laboratory at any time.

The coursework was aimed at teaching them the methodology of engine calibration. This important step

in engine development requires engineers to determine the optimal settings of the engine actuators to

meet fuel consumption, emissions, and performance targets. This engineering task is essentially an

optimisation problem of a complex, non-linear system with many input parameters and multiple targets

and constraints. It is typically conducted once engine hardware is available and the industry state-of-

the-art approach makes use of design of experiments methodology [2]. The optimisation process is

typically conducted according to the commonly called “Z-process” [2] which combines the following six

steps:

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1. Problem definition defining the targets and acceptable ranges of actuator settings based on

expert and prior knowledge,

2. Design of experimental test plan using specialist engineering software,

3. Experimental test campaign to collect data in the engine test facility,

4. Regression modelling to generate mathematical functions capturing the measured behaviour of

the engine (in specialist software),

5. Search of optimum controller configuration using optimisation algorithms and functions

generated in step 4,

6. Validation of optimal controller configuration on engine test cell.

The exercise created in this case study aims to allow students to put into practice steps 2-4 of this

process. In undertaking this task, the students are:

- Learn the underlying principles of DoE which is a useful statistical and experimental skill,

- Experience real engineering software for the application of DoE,

- Learn the functionality of engine test cells through the virtual laboratory.

The first of these points is achieved through the coursework design. Students were asked to undertake a

“one factor at a time” experiment, followed by a DoE approach. Through the same number of test

points, they will learn that the DoE approach gives them a far richer data set and much more

information on the behaviour of their system. Because they are required to collect the data from the

virtual laboratory, they will appreciate the time gain this approach offers.

The second is achieved by requiring the students to use an automotive calibration software tool to plan

their experiment and to build their regression models. It is not the aim of the course to teach any

particular software tool, however it is important to expose students to these tools as most available on

the market are similar in structure. This is analogous to teaching engineering drawing through computer

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aided design (CAD) software. A particular software package must be chosen, but the overall goal is to

teach the process.

The final step is achieved through use of the virtual laboratory and an accompanying session on engine

test cells delivered by a post-doctoral researcher. In addition to running the laboratory, the students are

also exposed to the post-processing of data and the conversion of measured quantities into physical

parameters. For example, exhaust emissions can only be measured as volumetric concentrations, and a

conversion process must be undertaken. This is an integral part of the exercise and students need to

create their own tools for doing this.

Figure 8 illustrates the intended workflow for the students’ assignment and clearly highlights the need

for the virtual engine laboratory. The engineering methods section are the key learning objectives of the

course, however without the virtual engine laboratory, it is not possible to complete the logical steps.

Any attempt to encourage students to undertake the tasks on the left-hand side of Figure 8 without the

virtual engine laboratory would require the provision of pre-recorded data and removes the feedback

from the test planning stage. The linking top the virtual engine lab allow the students to repeat and re-

try different approach.

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Figure 8: Outline procedure for students undertaking the coursework on calibration methods using the virtual laboratory environment

5 Reflection on the use of the virtual laboratory

5.1 Student attitude towards the virtual laboratory

The assignment deliverable was a short report capturing the student activities and a total of 58 students

participated and submitted a completed exercise.

The assignment asked students to compare the process of design of experiments with a simple one

factor at a time approach. The students were prescribed an experimental design and a regression model

structure and suggested they explore one additional design or model. As most student studied either

different experimental designs or regression models, it makes sense also to assess the total items

compared (nb. regression models + nb. experimental designs). Thus, the students were therefore asked

to compare 3 items. Figure 9a shows the number of students that compared different numbers of

experimental designs and regression models. Around 45% of student compared more than 3

models/designs.

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Whilst using the virtual laboratory, the students are exposed to the behaviour of the engines: they could

see the physical response of the engine to changes in set-points they were prescribing in the user

interface. For example, increasing the amount of fuel injected into the engine increases the exhaust

temperature and increases the speed of the turbocharger. This encourages the students to explore the

behaviour of the engine. In the activity, the students were asked to explore the trade-off between

engine fuel consumption and NOx emissions which is a key issue faced by engineers in the automotive

industry. Although the physical processes behind this trade-off are not the focus of this module, the

virtual lab intrigued many students to seek an understanding of this. Figure 9b shows the distribution of

students who cited relevant published material related to these physical processes. The figure also

shows the references explaining the statistical processes.

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(a)

(b)

Figure 9: Quantification of student work with respect to (a) design of experiments methodology and (b) the physical behaviour of internal combustion engines

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Both explaining the physical processes and comparing different experimental designs demonstrates that

the activity has stimulated the curiosity of many students to go beyond the core taught content. In

summary:

- 28% of students cited 3 or more references to explain engine processes or DoE processes

- 47% of students compared 4 or more regression models or experimental designs

- 57% of students did one of the above tasks and can be categorised as engaging in significant

self-learning because of the virtual laboratory.

5.2 Benefits to student learning

Section 5.1 showed how the virtual laboratory session allowed students to explore engine calibration

and statistical design in far more detail then previous cohorts taking the same course. It could be

postulated that the students who benefit from the virtual laboratory exercise would perform to a higher

level in a comparable examination situation relating to that topic. Figure 10 shows the normalised

examination scores from two cohorts, one having benefitted from the virtual laboratory session, the

other not. The distribution contains only information relating to the control and calibration part of the

examination. Although the examination question is different for each cohort, the question structure and

contents are comparable. Although in absolute terms there is an increase in examination performance,

historical analysis showed that this was within the year-to-year variability and therefore no direct

conclusion could be drawn.

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Figure 10: Normalised grades in examination of powertrain and calibration aspect of the course showing that there is a small but not significant improvement for students who have benefitted from the virtual laboratory.

The virtual laboratory coursework, which replaces part of the written examination, allows for the

assessment of the student’s application of theory and knowledge rather than their ability to remember.

This did show a marked impact on the performance of overseas students. Figure 11a shows the

normalised examination performance for students with a non-UK undergraduate education vs. those

with a UK-based undergraduate education. A marked difference can be seen between the two groups

with the overseas students underperforming compared to their UK-based counterparts. The

performance in the virtual laboratory coursework is shown in Figure 11b where the gap is significantly

less. Whilst the virtual laboratory is not the only method that could improve the inclusiveness of

teaching, the results do demonstrate the benefits of this learning technology. It is important to note that

without the virtual laboratory, only the written examination assessment would have been performed.

This also shows that the introduction of the virtual laboratory alone is not sufficient to improve the

performance in the examination: i.e. the undertaking of the virtual laboratory has not obviously

facilitated the learning of course contents. This reinforces that conclusion from the analysis of Figure 10

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which showed no significant difference in examination performance between the cohort having

benefitted from the virtual laboratory and those that did not.

(a)

(b)

Figure 11: Normalised grades of student whose undergraduate education was either in the UK or overseas for (a) the examination and (b) virtual laboratory assignment (Note: normalised grade shows the boundaries of marks achieved by students and does not indicate the pass/fail boundary. Both graphs a and b have the same x scale)

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5.3 Further skills development

Matlab was used as the platform for the user interface to encourage the development of generic

programming skills. It would have been possible to package the user interface as an independent

software tool for which access to the code and structure remains hidden for the user. Whilst this would

make the interface user-friendly, it is recognised that in engineering programming is becoming a key

generic skill and students should be encouraged to explore these tools. The open source nature of the

virtual laboratory empowers students to develop the ideas further incorporating new features such as

data analysis interfaces.

During the assessment period, most participants used the virtual laboratory as it was intended, i.e. by

using the GUI to drive the mathematical model. However, a small number of participants sought to

interact directly with the code. The primary motivation for this was to reduce the time required to spend

in front of the user interface during the data collection phases (Figure 8). Whilst at first glance this may

seem contrary to the objective of recreating the experience of operating a real engine laboratory, in

practice this approach was encouraged as requires direct interaction with the mathematical model of

the engine.

Students that engage with the mathematical model of the engine are in fact engaging with the research

work that has led to the creation of these models. This directly links teaching with research and has the

advantages:

- Encouraging the next generation of researchers in this community,

- Encouraging teaching to remain at the cutting edge of research.

5.4 Transferability of the virtual laboratory

The virtual laboratory interface was developed with an advisory board with membership from other

departments across the university (chemical engineering, health, electrical engineering, and physics).

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The involvement of academics from a range of disciplines was done to ensure that the interface library

would respond to their needs and be easily transferrable. Over the course of the virtual laboratory

development, three key applications were identified:

In chemistry/chemical engineering an application would enable students to experience the

control and monitoring of full scale chemical plants. This is important as students often have

difficulties in understanding the thermodynamic issues and the need to control reactions when

laboratories are scaled up to production,

In health education, distance learning students could benefit from the experience of monitoring

muscle activity of athletes breathing during exercise. These students have limited contact time

where they can visit the real laboratories: a virtual laboratory would allow them to experience

the data collection aspect from off-campus location,

In electrical engineering, the analysis of global positioning satellite (GPS) receiver technology

was identified to support learning in space science. This application has a similar motivation to

the virtual engine laboratory to provide students with practical experiences that cannot easily

be delivered in a real laboratory environment.

The user interface tool is hosted within an online repository, including full documentation and case

studies of the different applications.

6 Conclusions

A virtual laboratory for automotive applications is presented. The Virtual laboratory was built to

recreate the experience of the engineer when using an engine test cell, by replacing the real engine and

hardware with a mathematical model issued from research. The exercise has succeeded in changing the

learning experience of students by allowing them to put knowledge into practice. This has been

evidenced by:

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- The depth with which students have explored the topic of calibration,

- The engagement of student with research and external literature on engine physical processes,

- The improvement of performance of overseas students compared to assessments focussed on

remembering.

The open source nature of the user interface has successfully engaged students with the development

of the programming skills with a number of students exploring and modifying the application to suit

their needs. It is hoped that, along with the exposure to research, that this will encourage new young

researchers in this area in the future.

The virtual laboratory interface tool was built as a library of components that can easily be used to

create new interfaces for different applications. The project was undertaken with the advice from

academics from different disciplines to promote the transfer of the tool into other courses. This has

been a success as a second application is already underway in electrical engineering.

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