A segmented thermoelectric generator optimization method based on interpretable XGBoost and deep generative model

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy Pub Date : 2025-09-23 DOI:10.1016/j.anucene.2025.111891

Tianbao Fang , Weiliang Jin , Yinzhou Feng , Liangliang Lv , Guohan Gao , Jieqiong Luo , Gongping Li , Nannan Jia

{"title":"A segmented thermoelectric generator optimization method based on interpretable XGBoost and deep generative model","authors":"Tianbao Fang , Weiliang Jin , Yinzhou Feng , Liangliang Lv , Guohan Gao , Jieqiong Luo , Gongping Li , Nannan Jia","doi":"10.1016/j.anucene.2025.111891","DOIUrl":null,"url":null,"abstract":"<div><div>The implementation of segmented thermoelectric generators (STEGs) can enhance the energy conversion efficiency of radioisotope thermoelectric generators (RTGs). This study proposes a method integrating interpretable XGBoost and Conditional Variational Autoencoder (CVAE) to optimize STEG. An XGBoost regression model trained on validated finite element data predicts electrical performance while Shapley Additive Explanations (SHAP) analysis quantifies parameter impacts. Simultaneously, CVAE was implemented to construct mappings between design parameters and temperature field images to provide visual feedback during the design process. The results demonstrate the XGBoost model achieves exceptional regression performance, and enabling rapid prediction and multi-objective optimization. Based on the optimal design parameters, the CVAE predicts the temperature field image within 2 s, with a structural similarity index (SSIM) of 0.9676. SHAP-based interpretation reveals the key factors affecting electrical performance and provides decision support for optimized parameter selection.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"226 ","pages":"Article 111891"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Nuclear Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030645492500708X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

The implementation of segmented thermoelectric generators (STEGs) can enhance the energy conversion efficiency of radioisotope thermoelectric generators (RTGs). This study proposes a method integrating interpretable XGBoost and Conditional Variational Autoencoder (CVAE) to optimize STEG. An XGBoost regression model trained on validated finite element data predicts electrical performance while Shapley Additive Explanations (SHAP) analysis quantifies parameter impacts. Simultaneously, CVAE was implemented to construct mappings between design parameters and temperature field images to provide visual feedback during the design process. The results demonstrate the XGBoost model achieves exceptional regression performance, and enabling rapid prediction and multi-objective optimization. Based on the optimal design parameters, the CVAE predicts the temperature field image within 2 s, with a structural similarity index (SSIM) of 0.9676. SHAP-based interpretation reveals the key factors affecting electrical performance and provides decision support for optimized parameter selection.

查看原文本刊更多论文

基于可解释XGBoost和深度生成模型的分段热电发电机优化方法

分段式热电发生器（steg）的实现可以提高放射性同位素热电发生器（rtg）的能量转换效率。本文提出了一种集成可解释XGBoost和条件变分自编码器（CVAE）的方法来优化STEG。经过验证的有限元数据训练的XGBoost回归模型可以预测电气性能，而Shapley加性解释（SHAP）分析可以量化参数影响。同时，利用CVAE构建设计参数与温度场图像之间的映射关系，为设计过程提供视觉反馈。结果表明，XGBoost模型具有良好的回归性能，能够实现快速预测和多目标优化。基于最优设计参数，CVAE在2 s内预测出温度场图像，结构相似指数（SSIM）为0.9676。基于shap的解释揭示了影响电气性能的关键因素，并为优化参数选择提供决策支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Annals of Nuclear Energy 工程技术-核科学技术

CiteScore

4.30

自引率

21.10%

发文量

632

审稿时长

7.3 months

期刊介绍： Annals of Nuclear Energy provides an international medium for the communication of original research, ideas and developments in all areas of the field of nuclear energy science and technology. Its scope embraces nuclear fuel reserves, fuel cycles and cost, materials, processing, system and component technology (fission only), design and optimization, direct conversion of nuclear energy sources, environmental control, reactor physics, heat transfer and fluid dynamics, structural analysis, fuel management, future developments, nuclear fuel and safety, nuclear aerosol, neutron physics, computer technology (both software and hardware), risk assessment, radioactive waste disposal and reactor thermal hydraulics. Papers submitted to Annals need to demonstrate a clear link to nuclear power generation/nuclear engineering. Papers which deal with pure nuclear physics, pure health physics, imaging, or attenuation and shielding properties of concretes and various geological materials are not within the scope of the journal. Also, papers that deal with policy or economics are not within the scope of the journal.