Title: Optimal Design of Thermoelectric Generators for Low Grade Heat Recovery Developed by Dr. HoSung Lee on 12/15/2014

Transcription

1 Title: Optimal Design of Thermoelectric Generators for Low Grade Heat Recovery Developed by Dr. HoSung Lee on 12/15/2014 Low-grade waste heat conversion to electricity has drawn much attention toward reduction of production cost (Matsuura, Rowe et al. 1992, Rowe 1995, Ebrahimi, Jones et al. 2014). Organic Rankine cycle has been recently proved as a feasible solution with about 7% thermal efficiency and initial cost of about $2/watt for a high temperature of 116 C and low temperature of 25 C (Imran, Park et al. 2014, Matsuda 2014, Minea 2014). Thermoelectric generators is thought to be an alternative solution. The barriers for thermoelectric generators are the low conversion efficiency of about 3% and high initial cost of about $20/watt (Rowe 2012, Ebrahimi, Jones et al. 2014). However, recent development of optimal design on thermoelectric generators indicates that significant improvement in both performance and cost could be achieved (Lee 2010, Lee 2013, Attar, Lee et al. 2014, Weera 2014). Together particularly with a small operating cost due to no moving parts, thermoelectric generators would be a good and reliable candidate for lowgrade waste heat recovery with the present optimal design. We consider low grade heat recovery from Steam Assisted Gravity Drainage (SAGD) oil sands production. It is understood that the oil-water mixtures at about 200 C be drawn to the ground surface from a deep underground oil reservoir while injecting steam to the mixtures including sands in the reservoir. The oil-water mixtures once at the surface are then cooled down to 80 C probably for separation of oil and water. After the completeness of cooling process, the hot water at 80 C is separated from the mixtures. It is conventionally difficult to recover the low grade heat of the separated water and they are wasted. We herein focus on optimal design to recover the waste heat using thermoelectric generators between the hot water at 80 C and roomtemperature water at 25 C. In addition, there is a possibility to recover the cooling energy of the mixtures from 200 C to 80 C using also thermoelectric generators if the cooling process allows applying thermoelectric generators for the oil-water mixtures. This possibility is briefly discussed later. Design of thermoelectric generator modules for various systems has been challenging: manufacturers provide empirical performance curves for their products based on the ideal (standard) condition which uses the constant high and low junction temperatures that assume no thermal resistances between the junction surfaces and medium-fluids. This is indeed unrealistic. Optimization with the ideal condition are often used erroneously in system design. Two additional problems are confronted, which are firstly that the material properties are not known (the manufacturers do not usually provide the properties due to their proprietary information) and secondly that the thermal electrical contact resistances between the thermoelectric elements and the conductors are not known and even difficult to handle in design. System designers may have great difficulties to select suitable modules for their system among many commercial thermoelectric modules available. A typical thick and long thermoelectric generator (TEG) module is shown in Figure 1, which is very similar over those of other manufacturers, indicating that no correct optimization is currently implemented (compared to the present optimal design). Intuitive long element length for intention to decrease the thermal conductivity also provokes a joule heating concurrently consuming the generated electricity turning into heat, so it is not clear whether the long element length in Figure 1 is beneficial or not until the optimization for a specific system is taken into account.

2 Figure 1. A commercial thermoelectric module We considered a realistic case (Lee, 2013) taking a fluid flow with a heat sink on each side of the hot and cold junctions, so that there exist two thermal resistances between the two junctions and the hot and cold fluids, forming a unit cell (similar to a TEG module). Then, we defined five independent dimensionless numbers, not conflicting one another, one of which called Nk which includes the most important combined geometric information such as number of thermocouples, dimensions of element (cross-sectional area and length), and thermal conductivity. The next important dimensionless number is called Rr which is the ratio of external electrical resistance to internal electrical resistance. The third dimensionless number is called Nh, the ratio of the hot convection to the cold convection. The fourth is the ratio of the hot fluid temperature to the cold fluid temperature. The fifth is called the dimensionless figure of merit ZT (which represent the quality of a material, the higher the better). In the following calculations, ZT = 1.4 at 80 C is used throughout, of which the material is bismuth telluride nanocomposites and practically feasible for the mass production (Poudel, Hao et al. 2008) although the currently available bulk bismuth telluride alloy in the market is at approximately ZT = 1. We were able to optimize the combined dimensionless number Nk along with other dimensionless numbers for a given condition of the hot and cold water temperatures and the material properties, which is shown in Figure 2 (a) and (b). The figures show dramatic thermal dynamics of power and efficiency as functions of Rr and Nk. At an optimal Rr = 1.6 (note that the ideal condition with constant junction temperatures gives an impractical value of Rr = 1), With decreasing Nk from the maximum power output, the power output decreases in Figure 2 (a) while the efficiency increases in Figure 2 (b). The optimal point can be compensated to have a high enough power and also a reasonable efficiency, which is solely a different aspect of design compared with the Rankin cycle. If the resource of hot water is abundant and free, the power output may be more important than the conversion efficiency. (a) (b)

3 Figure 2 (a) Power output (W) and (b) conversion efficiency (%) with respect to Rr and Nk. These figures used hot water temperature of 80 C and low water temperature of 25 C, ZT = 1.4, Nh = 10 and base area 5 cm x 5 cm. The water velocity of 0.5 m/s in both channels is used. One of important findings in this study (under publication) is that as mentioned earlier the performance of TEG module is a function of element length, as shown in Figure 3, which is qualitatively in excellent agreement with the analysis of the contact resistances since both are thermal resistances anyway. This indicates that the present thermal resistances between the junctions and fluids through the heat sinks have a potential to effectively take into account the intractable contact resistances between the junctions and electrical conductors. This greatly simplifies the analysis of the present optimal design. Figure 3 shows that the optimal element length is near 0.5 mm which contrasts with the commercial TEG module in Figure 1 (approximately 5 mm). This result indicates that the thermoelectric material (initial cost) could be significantly reduced with an even much higher power output. The 5-mm length commercial TEG module in Figure 1 actually claims a power output of about 1 watt at this temperature, which is approximately in agreement with this figure. Figure 3. Power output and efficiency vs. element length. These figures used hot water temperature of 80 C and low water temperature of 25 C, ZT = 1.4, Nh = 10 and base area 5 cm x 5 cm. This optimization of element length of 0.5 mm in Figure 3 is attained along with other optimal dimensionless parameters, mainly Rr, Nk and Nh. It is practically very difficult to achieve the optimization by a trial and error method without correct information of optimal Rr, definition of Nk, and system (fluid and heat sink) conditions. It is noted that the reduction of element length from 5 mm to 0.5 mm results in a fivefold increase in the power output from 1 watts to 5 watts with an acceptable decrease in the conversion efficiency from 3.2% to 2.5%. In fact, the similar phenomenon was surprisingly observed in the work of Matsuura and Rowe (Matsuura, 1992), where they thought that the increase of power output with decreasing the element length attributes solely to the contact resistances. However, the present study shows the thermal resistances virtually includes many features such as the fluids convection, the heat sinks and the contact resistances. Now, we can estimate the material cost per watt. According to the CRC Handbook (Rowe 2012), the cost per watt for typical TEG modules is about $20/watt, which

4 might be estimated to be $20 per 1 watts-commercial modules in this case. If we divide $20 by the present work of 5 watts instead of 1 watts, we may have $4/watt. Considering the reduction of element length from 5 mm to 0.5 mm would even reduce the material cost further probably to about $2/watt, which may be competitive to the cost $2/watt of the Rankine cycle. This calculation is very rough because the hot water temperature of 80 C used for the present work does not match the hot water temperature of 116 C used for the organic Rankine cycle. We just try to illustrate the effectiveness of our optimal design compared to the bench mark values. If the initial cost is competitive, TEG would be obviously a better choice when considering the negligible operating cost. The effects of ZT on performance are calculated using the present optimization method in Figure 4. The performance of TEG module can be improved with increasing ZT. ZT = 3 is expected in the near future based on the trend and development of material research-currently ZT = 2.4 with quantumdots superlattice material (Venkatasubramanian, Silvola et al. 2001, Dresselhaus, Chen et al. 2007) using nanotechnology, which may produce about 10 watts per the base area of 5 cm x 5 cm with 4.5 % efficiency as shown in Figure 4. Figure 4. Optimal power output and efficiency vs. the dimensionless figure of merit. This figure used hot water temperature of 80 C and low water temperature of 25 C, Nh = 10 and base area 5 cm x 5 cm. One of advantages using TEG for waste heat recovery is that there is no limit for the temperature difference between hot and cold water temperatures (although there may be a limit due to an economic reason between gain and cost) while the Rankine cycle may have a limited range for heat source water temperature for instance between 80 C and 60 C. It is then understood that the hot water at 80 C enters an evaporator and leaves at 60 C in the Rankine cycle, where the waste heat below 60 C is not used for recovery. The thermal efficiency for the Rankine cycle is calculated based on the power output generated by a turbine over the heat (input) absorbed in the evaporator, actually this calculation is brought from the work of Matsuda (Matsuda 2014). The optimal power output for TEG is calculated as a function of hot fluid temperature, which is shown in Figure 5, showing clearly that there will be a waste heat recovery (additional gain) from the heat source water below 60 C. On the other hand, there appears a progressive power output decreasing approximately from 6 watts to 3 watts as hot water passing through the channel of TEG modules from 80 C to 60 C. If we presume that the gain from the recovery below 60 C is the same as the loss from the power output decreasing from 80 C to 60 C, our simple calculation of power output at 80 C would be a good approximation. Now we try to estimate how many modules or arrays are required for a power output of 1000 kw if the power

5 output of 5 watts per base area of 25 cm^2 is used. Suppose that an array consists of 500 modules which has 1.25 m^2 in the surface area. A total number of modules is obtained by dividing 1000 kw by 5 W, which is 200,000 modules. Dividing 200,000 by 500 leads to total 400 arrays, which is shown in the first column of Table 1. The first column uses nanocomposite material of bismuth telluride alloy which has ZT = 1.4 developed by a MIT group (Poudel, 2008) being considered to exhibit the mass production in the near future. However, the similar calculations with currently available commercial bulk material of ZT = 1 are performed, which are shown in the second column of Table 1. All the three payback values in Table 1 are less than 5 years, which indicates that all are beneficial and feasible. Figure 5 Optimal power output as a function of hot fluid temperatures. This figure used low water temperature of 25 C, Nh = 10 and base area 5 cm x 5 cm. Table 1. Summary for three specific waste heat recovery cases. Waste heat sources Hot Water 80 C Cold Water 25 C Thermoelectric material used Bismuth telluride nanocomposites ZT = 1.4 at 80 C (Poudel, Hao et al. 2008) Performance of TEG Heat Recovery Units (Performance for maximum power output) Hot Water 80 C Cold Water 25 C Bismuth telluride Bulk ZT = 1.0 at 80 C (Poudel, Hao et al. 2008) Optimal calculations for one module Hot oil and water mixture 200 C Cold water 25 C Bismuth telluride nanocomposites ZT = 1.1 at 200 C (Poudel, Hao et al. 2008) Power output (W) ~ 5 W (6.5 W) ~ 3.5 W (4.8 W) ~ 35 W per module base area 25 cm^2 Total heat delivered (W) ~ 200 W ~ 194 W ~700 W Conversion efficiency (%) ~ 2.5 % (1.7 %) ~ 1.8 % (1.3 %) ~ 5 % Ideal efficiency* /Carnot 3.4 % / 15.5 % 2.5 % / 15.5 % 6.6 % / 31 % efficiency (%) Estimate cost $ per ~ $2/W ~ $3/W < $1/W watt** Element length (mm) 0.5 mm 0.6 mm 0.3 mm

6 Total # of modules (= 1000 kw / module power output) Volume of total arrays (= Volume of array # of array) Power density (= 1000 kw / volume of total arrays) Total hot water flow rate (kg/s) based on water velocity (m/s) in channel Calculations for 1000 kw power output 200, ,000 28, m^3 15 m^3 1.5 m^3 100 kw/m^3 70 kw/m^3 700 kw/m^3 ~ 230 kg/s 0.5 m/s ~ 230 kg/s 0.5 m/s ~ 215 kg/s 0.5 m/s Power generation (kw) 1000 kw 1000 kw 1,000 kw Total waste heat (kw) 40,000 kw 56,000 kw 20,000 kw Pump power (kw) for 80 kw 80 kw 80 kw hot & cold water flows Power generation efficiency (%) (1,000kW-80kW) / 40,000kW = 2.3 % (1,000kW-80kW) / 56,000 kw = 1.6 % (1,000kW-80kW) / 20,000 kw = 4.6 % Payback (years) with electricity price $0.1/kW and operating time at 7000 hours/year ~ 3 years =(1000kW*$2/W) / [(1000kW-80kW) 7000hr $0.1/kWh] ~ 4.5 years =(1000kW*$3/W) / [(1000kW-80kW) 7000hr $0.1/kWh] < 1.5 years =(1000kW*$1/W) / [(1000kW-80kW) 7000hr $0.1/kWh] *Ideal efficiency assumes constant hot and cold junction temperatures which deems unrealistic. **Prices were estimated with commercial product values. As mentioned earlier, there is a potential to recover the additional cooling energy of the oil-water mixtures from 200 C to 80 C once at the ground surface using also thermoelectric generators if the cooling process allows applying thermoelectric generators for the mixtures. As for the hot water at 80 C, this case is similarly calculated, which is shown in the third column of Table 1. The power density of 700 kw/m^3 is large compared to 100 kw/m^3 of the hot water at 80 C because of the higher temperature. Although the present calculations exhibit crudity at this time, they give a good picture of the systematic idea. For instance, what would be the core space occupancy, initial cost, and operating cost for a waste heat recovery of 1 MW of the cooling down process of oil-water mixtures in SAGD? The answer is approximately 1.5 m^3, at most 1 million dollars ($1/W x 1000kW), and a negligible operating cost as shown in Table 1. It is good to note that the TEG devices are very reliable and robust being used for two decades without problems in Mars (Minnich, 2009). It is seen in Table 1 that the three different cases yield different element lengths, which means that the geometry of the TEG module are custom designed for the specific systems. In fact the number of thermoelectric elements and the element s cross-sectional areas of the module also show different values although they are not shown in Table 1. This is indeed one of the most challenging parts of optimal design, which would not be achieved without the solid mathematical definitions of the present optimal design method. To my knowledge, this custom design of TEG module with the optimal geometry (number of thermoelements, element length and crosssectional area) for a particular system has not been found in the literature.

7 As mentioned before, the unit cell includes the hot and cold fluid convections, heat sinks, contact resistances, and thermoelectric module. Each unit cell in an array through a channel exhibits thermal isolation, but connected through a heat balance (energy in and out) across the unit cell, which implies that the junction temperatures of the unit cells along each channel reveal a stepchange rather than a continuous change. This allows mathematically the optimization tractable. The adequacy of the thermal isolation is well verified (Attar, 2014). Therefore, the concept of the unit cell in the present work is realistic. Likewise, we are able to optimize the geometry (number of thermo-element, cross-sectional area and length) of each unit cell from the entry to the exit in the channel for a specific system like this low grade heat recovery at 80 C or 200 C. There is still a remaining question from the earlier discussion how to determine the material properties such as the Seebeck coefficient, the electrical conductivity, and the thermal conductivity for the optimal design. This question is fundamental and also formidable even for manufacturers because the intrinsic material properties (provided by the material developer) will be changed depending on manufacturability due mainly to the contact resistances and does not usually show an agreement with the measurements of the performance (Huang, Weng et al. 2010). Therefore, we have developed a technique to determine the material properties using three maximum parameters of Tmax, Imax, and Qmax either from manufacturer s specification or direct measurements (Ahiska and Ahiska 2010, Weera 2014). We first formulated theoretically the maximum parameters and expressed the three material properties in terms of the three maximum parameters (Lee, Attar et al. 2014). When the material properties are determined by using the measured maximum parameters, the material properties become meaningful and realistic including all uncertainties such as the contact resistances, Thomson effect, degradation of materials, etc., which are called the effective material properties. One of my graduate students conducted experiments as shown in Figure 6 to verify the adequacy of the effective material properties developed. He compared the predictions using the effective material properties with the measurements and also the manufacture s data, showing all in good agreement, which is shown in Figures 7 (a) and (b). This result justifies that the effective material properties are good for the optimal design. It is important to note that the dimensionless figure of merit ZT using the effective material properties exhibits a little less than the intrinsic ZT, which result from all the uncertainties or losses imposed on the ZT.

8 Figure 6. Experimantal setup Figure 7 (a) Measured power output vs. hot side temperature for thermoelectric generator HZ-2, (b) measured cooling power vs. current for thermoelectric cooler RC12-04 References Ahiska, R. and K. Ahiska (2010). "New method for investigation of parameters of real thermoelectric modules." Energy Conversion and Management 51(2): Attar, A., et al. (2014). "Optimal Design of Automotive Thermoelectric Air Conditioner (TEAC)." Journal of Electronic Materials. Dresselhaus, M. S., et al. (2007). "New Directions for Low-Dimensional Thermoelectric Materials." Advanced Materials 19(8): Ebrahimi, K., et al. (2014). "A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities." Renewable and Sustainable Energy Reviews 31: Huang, H.-S., et al. (2010). "Thermoelecrtic water-cooling device applied to electronic equipment." International Communications in Heat Transfer 37: Imran, M., et al. (2014). "Thermo-economic optimization of refrigerative organic Rankine cycle for waste heat recovery applications." Energy Conversion and Management 87: Lee, H. (2010). Thermal Design; Heat Sink, Thermoelectrics, Heat Pipes, Compact Heat Exchangers, and Solar Cells. Hoboken, New Jersey, John Wiley & Sons.

9 Lee, H. (2013). "Optimal design of thermoelectric devices with dimensional analysis." Applied Energy 106: Lee, H., et al. (2014). "Performance Prediction of Commercial Thermoelectric Cooler Modules Using the Effective Material Properties." Submitted to Journal of Electronic Materials. Matsuda, K. (2014). "Low heat power generation system." Applied Thermal Engineering 70(2): Matsuura, K., et al. (1992). Design optimization for a large scale low temperature thermoelectric generator. Proceedings of the International Conference on Thermoelectrics. 11th: Minea, V. (2014). "Power generation with ORC machines using low-grade waste heat or renewable energy." Applied Thermal Engineering 69: Poudel, B., et al. (2008). "High-thermoelectric performance of nanostructured bismuth antimony telluride bulk alloys." Science 320(5876): Rowe, D. M. (1995). CRC handbook of thermoelectrics. Boca Raton London New York, CRC Press. Rowe, D. M. (2012). Modules, systems and application in thermoelectrics. Roca Raton, CRC Press, Taylor & Francis. Venkatasubramanian, R., et al. (2001). "Thin film thermoelectric devices with high room temperature figure of merit." Nature 413: Weera, s. (2014). Analytical performance evaluation of thermoelectric modules using effective material properties. Kalamazoo, Michigan, Western Michigan University. Master of Science in the Department of Mechanical and Aerospace Engineering: 143. Weera, S. L. L. (2014). Analytical performance evaluation of thermoelectric modules using effective material properties. Mechanical and Aerospace Engineering. Kalamazoo, Michigan, Western Michigan University. Master of Science. Evaluation Criteria 1. Proposed Technical Approach 2. Supporting Data - (Provide data to support your upgrade or conversion process; for conceptual approaches provide scientific rationale, where possible provide diagrams, performance predictions, measured data and cost data. Cost data may be in $/kw. Provide payback period.) 3. Technical Readiness (Describe the readiness of this idea to be executed in the field and the investment required.)

10 The present optimal design method with the currently available bulk material in the market (the second column of Table 1) shows a readiness of this idea providing a reasonable payback of less than 5 years for 1000 kw power output using low grade heat of water at 80 C and a power density of 70 kw/m^3. This allows to install the TEG units anywhere in a convenient place with a minimum space occupancy. 4. Plan for Execution (Provide the tasks, time and cost estimates to take this idea to the field.) The structure of TEG units with the current bulk material of ZT = 1 (column one in Table 1) is relatively simple compared to an organic Rankine cycle; an array has 500 modules (unit cells), each array unit having a thickness of 2 cm leading to a volume of 1.1 m x 1.1 m x 2 cm. There will be 572 array units leading to total volume of about 15 m^3 for 1000 kw. The present realistic optimal design method allows prompt calculations entering experimental verifications using a readily available two-flow-loop apparatus at WMU. Once the design is completed, the computer simulations will be performed to confirm the performance in the system. Meanwhile, a 32-TEG-module prototype unit is constructed and tested in my lab and compared with the computer model. Once the experimental results with the computer simulations are satisfactory. We finalize the design of an array unit and then start to construct/test a real prototype consisting of 500 modules array. After confirmation of the test results, we may order 572 array units which may be divided by a number of groups of arrays (called banks) for practical reasoning. It is anticipated to take five years in total for the above mentioned tasks. We believe some collaboration will be necessary for these tasks: potential companies are Marlow and Fraunhofer IPM for thermoelectric modules or elements fabrication, GenTherm for the array (or bank) units, and a company for piping and pumping design and installation. The payback for 1000 kw power output assuming electricity $0.1/kW may be estimated considering the TEG materials of about three million dollars (as shown in Table 1), the construction of array units, the piping and installation, and the pumping cost, which may lead to approximately the payback of 7 years (this is a very rough estimation which may change significantly). 5. Energy efficiency Calculations (Provide calculations to demonstrate that the energy input is less than the highest value or electricity produced, using one of the two equations below. Provide the usable heat temperature level that can be achieved and the waste heat temperatures.) Power generation E = (HVH EC)/Q Where: E = Efficiency HVH = High value in GJ (electricity produced) EC = Energy consumed in GJ Q = Total waste heat available in GJ Table 1. Summary for three specific waste heat recovery cases.

11 Waste heat sources Hot Water 80 C Cold Water 25 C Thermoelectric material used Bismuth telluride nanocomposites ZT = 1.4 at 80 C (Poudel, Hao et al. 2008) Performance of TEG Heat Recovery Units (Performance for maximum power output) Hot Water 80 C Cold Water 25 C Bismuth telluride Bulk ZT = 1.0 at 80 C (Poudel, Hao et al. 2008) Optimal calculations for one module Hot oil and water mixture 200 C Cold water 25 C Bismuth telluride nanocomposites ZT = 1.1 at 200 C (Poudel, Hao et al. 2008) Power output (W) ~ 5 W (6.5 W) ~ 3.5 W (4.8 W) ~ 35 W per module base area 25 cm^2 Total heat delivered (W) ~ 200 W ~ 194 W ~700 W Conversion efficiency (%) ~ 2.5 % (1.7 %) ~ 1.8 % (1.3 %) ~ 5 % Ideal efficiency* /Carnot 3.4 % / 15.5 % 2.5 % / 15.5 % 6.6 % / 31 % efficiency (%) Estimate cost $ per ~ $2/W ~ $3/W < $1/W watt** Element length (mm) 0.5 mm 0.6 mm 0.3 mm Total # of modules (= 1000 kw / module power output) Volume of total arrays (= Volume of array # of array) Power density (= 1000 kw / volume of total arrays) Total hot water flow rate (kg/s) based on water velocity (m/s) in channel Calculations for 1000 kw power output 200, ,000 28, m^3 15 m^3 1.5 m^3 101 kw/m^3 71 kw/m^3 700 kw/m^3 ~ 230 kg/s 0.5 m/s ~ 230 kg/s 0.5 m/s ~ 215 kg/s 0.5 m/s Power generation (kw) 1000 kw 1000 kw 1,000 kw Total waste heat (kw) 40,000 kw 56,000 kw 20,000 kw Pump power (kw) for 80 kw 80 kw 80 kw hot & cold water flows Power generation efficiency (%) (1,000kW-80kW) / 40,000kW = 2.3 % (1,000kW-80kW) / 56,000 kw = 1.6 % (1,000kW-80kW) / 20,000 kw = 4.6 % Payback (years) with electricity price $0.1/kW and operating time at 7000 hours/year ~ 3 years =(1000kW*$2/W) / [(1000kW-80kW) 7000hr $0.1/kWh] ~ 4.5 years =(1000kW*$3/W) / [(1000kW-80kW) 7000hr $0.1/kWh] < 1.5 years =(1000kW*$1/W) / [(1000kW-80kW) 7000hr $0.1/kWh] *Ideal efficiency assumes constant hot and cold junction temperatures which deems unrealistic. **Prices were estimated with commercial product values.

12 6. Experience (Discuss your experiences in low temperature waste heat recovery, including number of projects.) I have taught a course of Design of Thermal Systems at WMU since Meanwhile, I have developed a new graduate course of Advanced Thermal Design for 8 years, where thermoelectric generators is one of the topics. Low grade heat recovery is often taken by students as a term projects. This is the rationale for me to develop optimal design of thermoelectric devices. I wrote a textbook with a title of Thermal Design: Heat Sinks, Thermoelectrics, Heat Pipes, Compact Heat Exchangers and Solar Cells to help students for their thermal design projects. The optimal design developed is particularly good in nature for low grade heat recovery. The finding are under publications. 7. Expertise (Share the team expertise, discuss other work of a similar nature, highlight other capabilities or competencies that augment your ability perform the proposed project, provide the years in this field.) One of my students, Sean Weera, conducted experiments to verify the effective material properties which is one of important features of the optimal design methods with a good agreement compared to the theoretical equations (Weera, 2014). Another PhD student, Alaa Attar, developed automotive air conditioner using the optimal design method found a good way of design and proved the method is essential for his design (Attar, 2014). Low-grade waste heat conversion to electricity has drawn much attention toward reduction of production cost (Matsuura, Rowe et al. 1992, Rowe 1995, Ebrahimi, Jones et al. 2014). Organic Rankine cycle has been recently proved as a feasible solution with about 7% thermal efficiency and initial cost of about $2/watt for a high temperature of 116 C and low temperature of 25 C (Imran, Park et al. 2014, Matsuda 2014, Minea 2014). Thermoelectric generators is thought to be an alternative solution. The barriers for thermoelectric generators are the low conversion efficiency of about 3% and high initial cost of about $20/watt (Rowe 2012, Ebrahimi, Jones et al. 2014). However, recent development of optimal design on thermoelectric generators indicates that significant improvement in both performance and cost could be achieved (Lee 2010, Lee 2013, Attar, Lee et al. 2014, Weera 2014). Together particularly with a small operating cost due to no moving parts, thermoelectric generators turned out to be a good and reliable candidate for low-grade waste heat recovery with the present optimal design. I have been working in this field since I am a director of Advanced Thermal Science at Western Michigan University.