Probabilistic Modeling Approach to Thermoelectric Systems Design Optimization

Abstract

Recent studies on thermoelectric (TE) systems indicate that the existence of high figure of merit (ZT) materials alone is not sufficient for superior system performance and an integrated system level analysis is necessary to attain such performance. This is because there are numerous design parameters at various levels of the system that are randomly variable in nature that could affect the overall system performance. In this work the effect of stochasticity in design variables at various levels of a TE system has been studied and analyzed to attain optimal design solutions. Starting with stochasticity in one of the environmental variables, a progression was made towards studying the coupled effects of stochasticity in multiple variables at environmental and heat exchanger levels of a thermoelectric generator (TEG) system. Research and analysis tools were developed to incorporate stochasticities in single or multiple variables individually or simultaneously to study both the individual and coupled affects of input design variable stochasticities (probabilities) on output performance variables. Results indicate that normal or Gaussian distribution in input design parameters may not produce Gaussian output parameters. Also when the stochasticities in multiple variables are coupled, the standard deviations in performance parameters are magnified, and their means/averages deviate moremore » from the deterministic values. Although more studies are required to quantify the parameters for design modifications, the studies presented in this paper affirm that incorporating stochastic variability not only aids in understanding the effects of system design variable randomness on expected output performance, but also serves to guide design decisions for optimal TE system design solutions that provide more robust system designs with improved reliability and performance across a range of off-nominal conditions.« less