skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: