Optimization of segmented thermoelectric generator using Taguchi and ANOVA techniques

Ravi Anant Kishore, Mohan Sanghadasa, Shashank Priya

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Recent studies have demonstrated that segmented thermoelectric generators (TEGs) can operate over large thermal gradient and thus provide better performance (reported efficiency up to 11%) as compared to traditional TEGs, comprising of single thermoelectric (TE) material. However, segmented TEGs are still in early stages of development due to the inherent complexity in their design optimization and manufacturability. In this study, we demonstrate physics based numerical techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing the performance of segmented TEGs. We have considered comprehensive set of design parameters, such as geometrical dimensions of p-n legs, height of segmentation, hot-side temperature, and load resistance, in order to optimize output power and efficiency of segmented TEGs. Using the state-of-the-art TE material properties and appropriate statistical tools, we provide near-optimum TEG configuration with only 25 experiments as compared to 3125 experiments needed by the conventional optimization methods. The effect of environmental factors on the optimization of segmented TEGs is also studied. Taguchi results are validated against the results obtained using traditional full factorial optimization technique and a TEG configuration for simultaneous optimization of power and efficiency is obtained.

Original languageEnglish (US)
Article number16746
JournalScientific reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017

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Analysis of Variance
Hot Temperature
Physics
Leg

All Science Journal Classification (ASJC) codes

  • General

Cite this

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Optimization of segmented thermoelectric generator using Taguchi and ANOVA techniques. / Kishore, Ravi Anant; Sanghadasa, Mohan; Priya, Shashank.

In: Scientific reports, Vol. 7, No. 1, 16746, 01.12.2017.

Research output: Contribution to journalArticle

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