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Title: Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems

Abstract

ABSTRACT Research work has investigated the impacts and effects of single- and multi-variable stochasticity on optimum thermoelectric (TE) system design for automotive and industrial energy recovery applications because many critical design and environmental parameters input to the design optimization process can be randomly variable. Analysis tools and techniques have been developed to investigate a variety of stochastic behaviors in critical input parameters, including Gaussian, Log-Normal, Weibull, Gamma, or any type of user-defined probability distribution. Recent accomplishments discussed in this work show that Gaussian input probability distributions can create non-Gaussian outcome distributions for optimum TE areas, required cold-side mass flow rates, and expected power generation; optimum deterministically-derived designs (TE areas and cold-side mass flow rates) should be significantly modified in response to stochastically variable inputs; and outcome parameter standard deviations can be quite significant and magnified relative to input parameter standard deviations. Multiple variable stochastic inputs tend to significantly increase the output design parameter variability (i.e., standard deviations). Coupled, interactive effects/impacts of multiple stochastic input parameters in this research have demonstrated that reductions of optimum TE areas by 9-10% relative to deterministic optimum values was warranted in key stochastic analyses cases studied. Reductions in required cold-side mass flow rates may alsomore » be justified. Optimum system power output also was characterized by relatively high standard deviations and variability as a result of stochastic input parameter effects on the TE design optimization process, this would be an important consideration when integrating the overall power system design with power management electronics and energy storage subsystems.« less

Authors:
;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
928623
Report Number(s):
PNNL-SA-53436
TRN: US200811%%429
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Energy Sustainability Conference, 323-331
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 30 DIRECT ENERGY CONVERSION; DESIGN; DISTRIBUTION; ENERGY RECOVERY; ENERGY STORAGE; FLOW RATE; MANAGEMENT; OPTIMIZATION; POWER GENERATION; POWER SYSTEMS; PROBABILITY; THERMOELECTRIC CONVERSION

Citation Formats

Hendricks, Terry J., and Karri, Naveen K. Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems. United States: N. p., 2007. Web.
Hendricks, Terry J., & Karri, Naveen K. Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems. United States.
Hendricks, Terry J., and Karri, Naveen K. Sun . "Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems". United States. doi:.
@article{osti_928623,
title = {Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems},
author = {Hendricks, Terry J. and Karri, Naveen K.},
abstractNote = {ABSTRACT Research work has investigated the impacts and effects of single- and multi-variable stochasticity on optimum thermoelectric (TE) system design for automotive and industrial energy recovery applications because many critical design and environmental parameters input to the design optimization process can be randomly variable. Analysis tools and techniques have been developed to investigate a variety of stochastic behaviors in critical input parameters, including Gaussian, Log-Normal, Weibull, Gamma, or any type of user-defined probability distribution. Recent accomplishments discussed in this work show that Gaussian input probability distributions can create non-Gaussian outcome distributions for optimum TE areas, required cold-side mass flow rates, and expected power generation; optimum deterministically-derived designs (TE areas and cold-side mass flow rates) should be significantly modified in response to stochastically variable inputs; and outcome parameter standard deviations can be quite significant and magnified relative to input parameter standard deviations. Multiple variable stochastic inputs tend to significantly increase the output design parameter variability (i.e., standard deviations). Coupled, interactive effects/impacts of multiple stochastic input parameters in this research have demonstrated that reductions of optimum TE areas by 9-10% relative to deterministic optimum values was warranted in key stochastic analyses cases studied. Reductions in required cold-side mass flow rates may also be justified. Optimum system power output also was characterized by relatively high standard deviations and variability as a result of stochastic input parameter effects on the TE design optimization process, this would be an important consideration when integrating the overall power system design with power management electronics and energy storage subsystems.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Apr 01 00:00:00 EDT 2007},
month = {Sun Apr 01 00:00:00 EDT 2007}
}

Conference:
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  • Advanced, direct thermal energy conversion technologies are receiving increased research attention in order to recover waste thermal energy in advanced vehicles and industrial processes. Advanced thermoelectric (TE) systems necessarily require integrated system-level analyses to establish accurate optimum system designs. Past system-level design and analysis has relied on well-defined deterministic input parameters even though many critically important environmental and system design parameters in the above mentioned applications are often randomly variable, sometimes according to complex relationships, rather than discrete, well-known deterministic variables. This work describes new research and development creating techniques and capabilities for probabilistic design and analysis of advanced TEmore » power generation systems to quantify the effects of randomly uncertain design inputs in determining more robust optimum TE system designs and expected outputs. Selected case studies involving stochastic TE .material properties demonstrate key stochastic material impacts on power, optimum TE area, specific power, and power flux in the TE design optimization process. Magnitudes and directions of these design modifications are quantified for selected TE system design analysis cases« less
  • Advanced, direct thermal energy conversion technologies are receiving increased research attention in order to recover waste thermal energy in advanced vehicles and industrial processes. Advanced thermoelectric (TE) systems necessarily require integrated system-level analyses to establish accurate optimum system designs. Past system-level design and analysis has relied on well-defined deterministic input parameters even though many critically important environmental and system design parameters in the above mentioned applications are often randomly variable, sometimes according to complex relationships, rather than discrete, well-known deterministic variables. This work describes new research and development creating techniques and capabilities for probabilistic design and analysis of advanced TEmore » power generation systems to quantify the effects of randomly uncertain design inputs in determining more robust optimum TE system designs and expected outputs. Selected case studies involving stochastic TE .material properties and coupled multi-variable stochasticity in key environmental and design parameters are presented and discussed to demonstrate key impacts from considering stochastic design inputs on the TE design optimization process. Critical findings show that: 1) stochastic Gaussian input distributions may produce Gaussian or non-Gaussian outcome probability distributions for critical TE design parameters, and 2) probabilistic input considerations can create design effects that warrant significant modifications to deterministically-derived optimum TE system designs. Magnitudes and directions of these design modifications are quantified for selected TE system design analysis cases.« less
  • 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,more » 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 more 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
  • Advanced, direct thermal energy conversion technologies are receiving increased research attention in order to recover waste thermal energy in advanced vehicles and industrial processes. Advanced thermoelectric (TE) systems necessarily require integrated system-level analyses to establish accurate optimum system designs. Past system-level design and analysis has relied on well-defined deterministic input parameters even though many critically important environmental and system design parameters in the above mentioned applications are often randomly variable, sometimes according to complex relationships, rather than discrete, well-known deterministic variables. This work describes new research and development creating techniques and capabilities for probabilistic design and analysis of advanced TEmore » power generation systems to quantify the effects of randomly uncertain design inputs in determining more robust optimum TE system designs and expected outputs. Selected case studies involving stochastic TE .material properties and coupled multi-variable stochasticity in key environmental and design parameters are presented and discussed to demonstrate key impacts from considering stochastic design inputs on the TE design optimization process. Critical findings show that: 1) stochastic Gaussian input distributions may produce Gaussian or non-Gaussian outcome probability distributions for critical TE design parameters, and 2) probabilistic input considerations can create design effects that warrant significant modifications to deterministically-derived optimum TE system designs. Magnitudes and directions of these design modifications are quantified for selected TE system design analysis cases.« less
  • The advanced neutron source (ANS) reactor being designed at Oak Ridge National Laboratory (ORNL) for the 1990s will be the worlds best source of low-energy neutrons for materials studies, physics research, transplutonium production, and radiation effects. Probabilistic risk analysis (PRA) is one of the tools being used for safety and operational optimization. The ANS is currently in preconceptual design. This paper presents an overview of the risk based on the initial preconceptual design, which is being modified, based in part on these PRA results. This iterative process of design and PRA evaluation will continue until optimized, after which the USmore » Department of Energy construction cycle may begin. PRA analysis is an important adjunct to the conventional reactor design process. It can provide an estimate of the fuel damage frequency being presented by a design, rank initiators and systems according to their contribution to risk, and identify areas where improvements would be most effective. The PRA is anticipated to continue providing risk-reduction guidance throughout design, construction, and operation.« less