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P09451 Detailed Design Review Agenda

KGCOE MSD of 45 P09451 Detailed Design Review

Meeting Purpose: To review and receive feedback on design details

Meeting Date: 2/13/09

Meeting Location: 09-1129

Meeting time: 12 – 2 PM

Agenda:

Time Topic of Review Team Member 12:00 - 12:10 Project Introduction & Overview Bryan 12:10 – 12:18 Action Items from System Review Andy 12:18 – 12:28 Data Collection & Time Studies Gabriela 12:28 – 12:45 Power Unit Design John 12:45 – 12:50 Mass Flow/Head Loss Calculations John 12:50 – 1:00 Piping Layout & Design Ken 1:00 – 1:15 Electrical System Design Jon 1:15 – 1:30 Peak Power Control System Ken 1:30 – 1:40 Labview User Interface Andy 1:40 – 1:50 Feasibility Analysis Gabriela 1:50 – 2:00 Action Items & Wrap-Up Bryan


Project Description

Project Background: This project is aimed at using thermoelectric (TE) devices to recover wasted energy exhaust from Dresser-Rand turbo machinery in the form of usable power. The goal of this project family is to acquire an improved understanding of the workings of thermoelectric devices. This knowledge will help bring RIT to the forefront of this emerging technology.

P09451, Thermoelectric Module for Large Scale Systems, is the fourth project in the TE family of projects that began in winter 2006 (2006-2). The first project, P07441 Thermo-Electric Module Test Stand, created a test stand and helped build an understanding of this technology through the characterization of commercially available TE modules. The second project, P07442 Thermo-Electric Demo Device, created a test bed to model automotive exhaust for future use in the exploration of TE heat exchangers used in waste heat recovery and power generation. P08451, Feasibility of Heat Recovery on Large Scale Systems developed a module to be used in conjunction with the test bed.

Problem Statement: In order to better understand thermoelectrics and their potential applications in industry for energy recovery, it is important to have a reliable and efficient test stand to validate models. The current test stand requires long set-up times, does not provide readily available data and has too many variables that compromise the reliability of the acquired data. It is also important that the stand allow for different configurations in order to have a wider range of data to draw conclusions from. These are areas that this project will be focused on improving in order to have a dependable test stand.

Objectives/Scope: 1. Implement air-cooling on cold side of thermo-

electric array in addition to the water-cooling system.

2. Be able to experimentally validate thermoelectric system models and enable more parameters to be explored.

3. Improve user interface and data acquisition to allow greater ease of use of the test stand.

4. Improve set-up and shut-down procedures to reduce assembly times.

Deliverables: • Upgraded thermoelectric test rig utilizing

interchangeable air-cooling on the cold side and capabilities for different configurations.

• Upgraded data acquisition system. • Documented data on module performance

for verification of theoretical results. • Feasibility Study for Dresser Rand.

Expected Project Benefits: • The ability to experimentally test valuable

aspects of thermoelectric modules. Core Team Members: • Bryan McCormick (ME) Project Manager • Andy Freedman (ME) Heat Transfer/Fluids

Analysis & Design • John Kreuder (ME) Thermal Modeler • Ken McLoud (ME) Structural Designer • Jon Holdsworth (EE) Electrical System

Designer • Gabriela Santa Cruz (IE) Engineering

Economics

Strategy & Approach

Assumptions & Constraints: 1. $7500 Budget 2. 22 week timeline 3. Max Temp 600 C 4. Module size constrained by test apparatus

Issues & Risks: • Thermal Expansion/Contraction • Adequate mass flow rate into TE module • Cost limiting technological potentials. • Finding a supplier for high-end

thermoelectric devices • Modeled results differ from experimental

results.

Project # Project Name Project Track Project Family

P09451 Thermoelectric Module for Large Scale Systems

Energy and Sustainable Systems

Sustainable Technologies for the Global Marketplace

Start Term Team Guide Project Sponsor Doc. Revision

2008-2 Dr. Robert Stevens Dresser-Rand 6


Main Action Items from System Review

• Add Specs for Customer Need 9 (CN9) and Customer Need (CN11)

� CN9 = Temp measurement (is what I'm measuring what I think I'm measuring)

� CN9 Spec: A. viii. Taking measurement does not affect measurement

� Yes or No � Yes, groove analysis refined and tested experimentally

� CN11 = Measure mean fluid temperature along power unit

� CN11 Spec: A. ix. Measure temperature between zones

� # of Measurements � # of zones plus 1


Refined Groove Analysis

Geometry Representation & Boundary Conditions (Figure 1)

-R2 represents the Thermoelectric Module (k=6W/mK)

-R1 represents the proposed groove filled with a thermal paste

(k=2.8 W/mK) [This represents a worse case scenario because the

thermocouple would actually be more beneficial to have modeled

than the paste. Due to the uncertainty of the actual effects of the

paste, the groove was assumed to be entirely filled with paste.]

-CO1 represents the upper portion of the aluminum test unit (k=160W/mK)

-Tc = 30°C or 303K

-Th = 200°C or 473K

-All other boundaries are insulated

Mesh (Figure 2)

-There are 30720 elements

Th = 200°C or 473K

Tc = 30°C or 303K


FEA Solution for Previous Model (Figure 3)

-As can be seen, significant issues existed due to the previous model’s

boundary conditions. These were changed to the current boundary

conditions. The new model also takes into account the actual dimensions

of our proposed system.


FEA Solution for Current Model (Figure 4)

KT

KT

KTA

dydxyxT

T

CO

R

R

L W

470

381

455

),(

1

2

1

0 0

===

=∫ ∫

-The average temperature within the groove is

455 Kelvin. This agrees with the average

boundary integral temperature from Figure 5

with only a minimal difference. Most of the

difference can be credited to the groove being

assumed to be entirely thermal paste rather

than the combination of the more thermally

conductive thermocouple and paste.


Boundary Integral (Figure 5)

KT

m

KmT

L

dxxT

T

L

45804.0

35015.18

)(0

=

⋅=

=∫

Solution – Closer Look (Figure 6)

- The absolute worse case temperature seen is the 438 Kelvin inside the groove. The average temperature throughout the groove is 455

Kelvin which agrees with the boundary integral value of 458 Kelvin meaning only a minimal difference. This being a worse case

scenario analysis allows us to believe that the temperature read inside the groove will read close or exactly the temperature that the hot

side of the thermoelectric module is seeing.

T=465K T=459K

T=458K T=457K T=438K


Experimental Results of Simple Test (Figure 7)

-Test stand was used to test individual thermoelectric module under three different scenarios

-Scenario 1: Thermocouple in the groove in the middle of the hole in the thermoelectric

-Scenario 2: Thermocouple in the groove offset from the hole in the thermoelectric by 9mm

-Scenario 3: No thermocouple and no groove

Results Pressure (psi) Th (degC) Tc (degC) Pmax (W) Scenario 1 130 200.06 29.77 4.55 Scenario 2 130 200.12 29.03 4.45 Scenario 3 130 200.16 ----- 4.54

-These results provide evidence that the groove has no known effect on the operation of the system.

Conclusions

-The groove is an acceptable design for taking measurements of temperature on both sides of the thermoelectric.

-More importantly, the groove will not significantly effect the transfer of heat through the aluminum plate to the hot side of the

thermoelectric. A relatively even distribution of heat allows for the temperature to be within measuring capabilities of expected values.


Data Collection The goal for performing tests on the current thermoelectric power unit was to gather enough data to perform statistical analysis to see how reliable and repeatable the data is. With this information we could get a better idea of where the system stands and where it needs improvement. Four tests were performed, the first two under the same exact conditions (i.e. same assembly, same temperature and flow), and for the second two the unit was disassembled and assembled one more time, to be able to measure the variability that is accountable for the assembly process. During both assemblies we also performed time studies to understand the process better and to be able to make design improvements to decrease assembly time. The results of the time studies, along with concepts to improve the times are displayed in the following chart: Test 1 Test 2 Improvement

Cleaning Cleaning 1:02 1:50 Applying Thermal Paste

to Modules: Three Modules, 1 side 4:12 3:46 Considered thermal pads, not feasible Three Modules, 2nd side 3:38 3:16 Considered thermal pads, not feasible

Placing Insulation 1:04 1:02 Marked Places for Modules for easy placement Setting on Bottom Plate 1:22 0:52 Bolts attached to bottom plate

Bottom Assembly

Sub Total 10:16 8:56 Applying Thermal Paste

to Modules: Three Modules, 1 side 3:51 3:04 Considered thermal pads, not feasible Three Modules, 2nd side 2:47 2:21 Considered thermal pads, not feasible

Placing Insulation 0:38 0:44 Marked Places for Modules for easy placement Setting Top Plate 0:28 0:59

Top Assembly

Sub Total 8:44 7:08 Placing Washers and Nuts 1:30 1:15 Tightening 3:01 4:21 Better Tool Clearance Outside Insulation 1:26 1:59 More space to insert, less pieces Thermocouples 6:50 5:54 Embeded in Power Unit

Final Assembly

Sub Total 12:47 13:29 Set on Jacks and Lift 1:47 2:51 Block with Appropriate Height in Test Stand Attaching to Stand 10:29 7:14 Better Tool Clearance Plugging into DAQ 6:54 6:01 Color Coding

Set-up in Stand

Sub Total 18:10 16:06

Total 49.57 45.39


Issues/Solutions on Data Processing The processing and cleaning up of the data that was collected presented many issues, which also gave place to ideas for improvement. The data is stored from LabView to a text file that is compatible with Microsoft Excel, but the data appears completely raw, with no labels or headers. This can make the cleanup process slow and allows for many user errors. To improve on this, the output will be formatted before storing into a file, so that the final result contains headers, valuable decimal points, etc. Another issue noted was that there is no control that prevents the system from taking invalid data, which is usually seen after the test is over, invalidating the stored data. To avoid this, data constrains will be implemented in LabView, with indicators when the data is out of range. Statistical Analysis Two tests were performed on the data to try to assess the variability of the different tests: a two-sample t-test and a z-test, using hypothesis testing. The two-sample t-test is one of the most commonly used hypothesis tests in Six Sigma work. It is applied to compare whether the average difference between two groups is really significant or if it is due instead to random chance. To perform this test, both samples must be normally distributed, so a plot of each measurement was performed. The p-value being less than 0.05 shows that the distributions are normal. The plots also showed a normal curve, as seen in the graph below:

191.28191.26191.24191.22191.20191.18191.16

Median

Mean

191.250191.245191.240191.235191.230191.225191.220

1st Q uartile 191.19

Median 191.23

3rd Q uartile 191.27

Maximum 191.29

191.22 191.24

191.22 191.25

0.04 0.05

A -Squared 3.14

P-V alue < 0.005

Mean 191.23

StDev 0.04

V ariance 0.00

Skewness -0.14661

Kurtosis -1.41878

N 100

Minimum 191.16

Anderson-Darling Normality Test

95% Confidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for S tDev95% Confidence Intervals

Summary for 101-1.1

The next step was to perform a t-test in MiniTab comparing the measurements made in the two different assemblies. The t-test outputs two important values, a t-statistic and a p-value. If the p-value is greater than 0.05 it is concluded that there is no difference in both samples. Unfortunately, none of the temperature measurements passed this test, and only the voltages and pressures passed it.


The hypothesis tested was Ho: x1-x2=0. (where x represents mean of the population) Namely stating that the mean of the first sample is equal to the mean of the second. To test whether this hypothesis is true, the t-test requires the finding of the t-statistic through the following formula:

where 2

)1()1(

21

222

2112

−+−+−

=nn

SnSnS p

The t-value must be compared with the critical value of the t distribution. The critical t-value marks the threshold that – if it is exceeded – leads to the conclusion that the difference between the observed sample mean and the hypothesized population mean is large enough to reject H0. The critical t-value equals the value whose probability of occurrence is less or equal to 5 percent. From the t-distribution tables, one can find that the critical value of t is +/- 2.093. The t-statistic confirmed that both measurements system are statistically different. This is clearly seen in a box-plot of one of the temperature measurements for both samples:

209_1209

29.9

29.8

29.7

29.6

29.5

29.4

29.3

29.2

Data

Boxplot of 209, 209_1

21

21

11

)(

nnS

xxT

p +

−=


Most aggressive fin:

Air flow (m/s) 1.0 2.0 3.0 4.1 5.1

Thermal resistance K/w 0.75 .50 .25 0

Rt = 0.39 K/w when cut to the right width and height 4”x1.939”. This is about 20% of the thermal resistance through the module itself, so about 2/3 of the temperature drop should be across the module.


Middle case:

Rt = 0.86 K/w at size 4” x 1.939” Wimpy case:

Rt = 1.06 K/w at size. About 1/3 of the temperature drop will be across the module. Also investigating custom folded fins:

For best case only. Cost, geometric compatibility and lead time will be the main considerations in final heat sink selection.


bernuolli eqn/conservation of energy

reduces to:

diameter0.166666667 ft0.050459179 m

current config.

Temp 20C/70F 200C/400Fmass flow(kg/s) 0.055 0.055air density (kg/m3) 1.2 0.7461volume flow m3/s 0.045833333 0.07371666velocity (m/s) 2.100845249 3.378922797viscosity m2/s 0.00001568 0.0000379Re 6760.645757 4498.619225e (cast iron) (um) 46 260e/d 0.000911628 0.000911628f 0.019 0.045e (pvc) (um) 5 2000 let D=1/6 fte/d 0.00009909 0.00009909f 0.035 0.07use these same f values for the new config.

air cool component additional number flow rate equiv length or length(ft) Kentrance Ventrance V/2 0exit Vexit V/2 090 deg bend 2 V 2.190 deg bend 4 V/2 4.245 deg bend V 045 deg bend V/2 0globe valve V 0finned length V/2 0finned length V 0tee (thru) 0tee (branch 90 deg) 3 V 6.3tee (branch 90 deg) 1 V/2 2.1length (major) V 8length (major) V/2 10Flow Meter VK of power unit V/2 5K of power unit V 0heater (reduced area) Vsum @ V 8 8.4sum @ V/2 10 11.3

lthgzvp

gzvp +++=++ 2

22

1

21

22 ρρ

lthp =ρ

1

∑+==i

ilt KV

D

VlfPh

2*2** 22

1

ρ


Piping Layout


Width = 45” Length = 98”

Height = 16”


Peak Power Impedance Matching &Automated Measurements

The current test stand requires manual measurements of Voc and IL.

Original Circuit with Manual Impedance Matching

3

By implementing a shunt resistor (RShunt) we can automate the measurement of the load

current.

Shunt

ShuntLoad R

VI = (Eq.1)


Impedance Matching Circuit for Peak Power

To complete the automation of the impedance matching, for peak power, a power MOSFET can

be implemented, where VariableR is the MOSFET resistance, controlled by the software control

system.

])[( DSTGSD VVVkI −= (Eq.2)

VariabletGSD

DS RVVkI

V=

−=

)(

1 (Eq.3)

ShuntVariableLoad RRR += (Eq.4)


Peak Power Control System 1) Plant model

0 5 10 15 20 25 300

0.5

1

1.5

2

2.5

3

3.5

4Power

Rload

pow

er

α = .05, ΔT=150, Rint = 4

A controller is needed to control Rload such that P is maximum for any value of α, ΔT, and

Rint.

The maxima occurs at Rload = Rint, however Rint changes with temperature and is not

well known.


So, we will take the derivative dP/dRload and design a controller to force it equal to zero.

0 5 10 15 20 25 30-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5dP/dRload

Rload

dP/d

Rlo

ad

This equation has no time dependence and therefore cannot be used as plant model for

a control system design. A damping term was added to account for the settling time of

the system

An optimization routine determined that a c value of .25 best corresponded to the open

loop settling time of 3 seconds


The control system:

In1 Out1

plant

In1 Out1

mosfet

dpdr

To Workspace1

time

To Workspace

In1 Out1

PID controller

0

Constant

Clock

the PID controller:

1

Out11s

Integrator

Kd

Gain2

Ki

Gain1

Kp

Gain

du/dt

Derivative

1

In1

The Mosfet converts the controller’s command voltage into a resistance:

Notice, this equation has a singularity at Vcommand = Vt

Vt ranges between 2 and 4 volts, which is well within our domain

This singularity causes the control system to seek the dp/dRload = 0 condition at R = ∞

Passing the PID output though the following function corrects the problem:


Optimized Response

Conditions: α = .05, ΔT=150, Rint = 4

Numerically optimized gains: Kp = 0.3.62, Ki = 16.21 Kd = -0.87

Initial condition = Rload = 1 ohm


Robustness

α, ΔT, and Rint are all highly variable, however the control gains can only be optimized at

one operating condition.

It is therefore important to understand how the gains optimized under the previous

operating condition behave under a different condition.

Conditions: α = .05, ΔT=75, Rint = 3

Same control gains

Initial condition = Rload = .5 ohms


max power = 1.17 watts

The system still reaches steady state in less than 2 seconds, with minimal oscillations

demonstrating that the gains can be optimized for a single operating point.


Data Acquisition

Needs Specification Priority Difficulty Metric Units Target Spec Marginal

B. Interface and Automation ---- ---- ---- ---- ---- CN6 i. Automated startup and shutdown 2 4 Actions # ≤2 ≤3 CN10 ii. Peak thermoelectric power indication 5 5 Binary yes/no yes yes CN16 iii. Steady state indication 3 1 Binary yes/no yes yes CN6 Time to reach steady state Time minutes ≤60 ≤90 CN7 iv. Graphs of calculated values 1 1 # of values displayed % 100 >75 CN7 v. Real time feedback 2 1 # of values displayed % 100 >75 CN7 vi. Automated post processing 2 1 Binary yes/no yes yes

CN4 vii Automate all measurements 4 4 Manual measurements # 0 <2

Need Number

Description

CN4 Automate electric data collection CN6 Reduce setup time CN7 Improve data display to assess problems during experiment CN10 Operate at peak power for duration of experiment CN16 Steady State Indication


Total Sensors (Inputs to DAQ) Thermocouples On Modules 48 Before/Inbetween/After Zones 5 Inlet/Outlet of Cold Exhaust 4 Heater Inlet 1 Mass Flows Air 1 Water 1 Compression Force Flexi-Force Sensors 8 Change in Air Pressure Pressure Transducers 3 Voltages Load Voltages 4 Shunt Voltages 4

79 Total

Air Configurations Sensor Needs Thermocouples On Modules 48 Before/Inbetween/After Zones 5 Inlet/Outlet of Cold Exhaust 4 Heater Inlet 1 Mass Flows Air 1 Compression Force Flexi-Force Sensors 8 Change in Air Pressure Pressure Transducers 3 Voltages Load Voltages 4 Shunt Voltages 4

78 Total

Water Configuration Sensor Needs Thermocouples On Modules 48 Before/Inbetween/After Zones 5 Heater Inlet 1 Mass Flows Air 1 Water 1 Compression Force Flexi-Force Sensors 8 Change in Air Pressure Pressure Transducers 1 Voltages Load Voltages 4 Shunt Voltages 4

73 Total


Current System User Interface


New System User Interface

Block Diagram


Interface


B. ii. Peak Thermoelectric Power Indication


B. iii. Steady State Indication


B. iv. Graphs of Calculated Values

Ls

s VR

VP =


B. v. Real Time Feedback

• Graphs

• Indicators

B. vi. Automated Post Processing

• All data will be output in text format, preferably excel, with column headers to save in post processing time

B. vii. Automate All Measurements

• Previously, IL and Voc were measured manually using a multimeter

• IL is now measured through the shunt resistor

• Voc is no longer needed due to new MPPT tracking system

Associated BOM

Description Approximate Unit Price Quantity Cost Supplier

Estimated Lead Time

Sensors and Modules Melcor TE modules $26.50 40 $1,060.00 Melcor 2-3 weeks Taihauxing TE modules $30.00 40 $1,200.00 Taihauxing 2-3 weeks K type Thermocouples (1/16") $22.00 72 $1,584.00 K type Thermocouples $22.00 3 $66.00 K type Thermocouples $22.00 4 $88.00 Thermocouple extension wire $138.00 1 $138.00 Omega Pressure Transducers $8.55 2 $17.10 Freescale Semiconductor Flexiforce sensors (sheet, 8 per sheet) $110.00 1 $110.00 Op-Amps $0.34 16 $5.44 Microchip


Feasibility Study Dresser-Rand follows an established format for gathering the required information that they need when assessing whether a new technology is feasible or not to be implemented. The template requires information on the goals, benefits, risks, possible markets, and other variables that are summarized in the following chart:

Project Charter

Project Name Thermoelectric Module for

Large Scale Systems Project Lead Bryan McCormick

Date, Revision # Project Sponsor Dresser-Rand: Paul Chilcott

Start Date 12/1/2008 SDT Leader

Completion Date 5/22/2009

Current Phase Detailed Design Idea Submitter Dr. Robert Stevens

Element Description Team Charter

Process Definition

The business process in which opportunity exists.

This project is aimed at acquiring an improved understanding of the workings of thermoelectric (TE) devices as a means to recovering wasted energy from Dresser-Rand

turbo machinery exhausts in the form of usable power.

Strategic Goal/ Business Case

Describe the opportunity as it relates to strategic business

goals.

A clear understanding of the workings, benefits and fallbacks of thermoelectric devices is important when deciding whether it is feasible to implement this technology in industry,

and whether it is the best choice among alternative technologies.

Problem Statement

State the significant issue(s) that needs to be addressed or opportunities to pursue.

To better understand thermoelectrics and their potential applications in industry for energy recovery, it is important to have a reliable, flexible and efficient test stand to validate models. The current test stand requires long set-up times, does not provide

readily available data and has too many variables that compromise the reliability of the acquired data. There are also different configurations that could be tested if the stand

allowed for more flexibility, which would produce a wider range of data to draw conclusions from.

What are the anticipated business results and when would

the results be realized?

• Upgraded thermoelectric test rig utilizing interchangeable air-cooling on the cold side and capabilities for different configurations.

• Upgraded data acquisition system. • Improved Power Unit.

•Documented data on module performance for verification of theoretical results. (All these deliverables are expected to be ready by 05/22/09)

Benefits Impact ($)

What is the preliminary budget estimate for the project cost?

This project counts with a budget of $7500

Scope/Boundaries Describe the project's scope and boundaries. Describe what is in

and outside the scope.

Activities within scope include: 1. Implement air-cooling on cold side of thermo-electric array in addition to the water-

cooling system. 2. Be able to experimentally validate thermoelectric system models and enable more

parameters to be explored. 4. Improve set-up and shut-down procedures to reduce assembly times.

3. Improve user interface and data acquisition to allow greater ease of use of the test stand.

The scope excludes: - Any study or design of the system that would eventually be implemented in industry.


Schedule/Milestones What are the start and

completion dates of the project?

Starting Date: 12/01/08 System Level Design:01/02/09

Detailed Design:01/30/09 Project Completion:5/22/09

Benefit to Customers

Who are the Customers, what benefit will they see and what

are their most critical requirements?

Dresser-Rand: The results of experimentally testing valuable aspects of thermoelectric modules will provide insights on the feasibility of implementing this technology to provide energy recovery capabilities to Dresser-Rand's customers, which would further increase

the value of the products, specially in applications in remote locations. RIT: This knowledge will help bring RIT to the forefront of this emerging technology.

Support Required (if any)

Will you need any special capabilities, hardware, etc.?

Key Stakeholders Who has been identified as a Key Stakeholder(s) for this

project?

Primary Stakeholder: Dresser-Rand Secondary Stakeholder: RIT

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