Autor/innen
Hanshu Chen, College of Mechanics and Engineering Science, Hohai University, Nanjing, Jiangsu, 211100, China; Yanzhao Gu, College of Mechanics and Engineering Science, Hohai University, Nanjing, Jiangsu, 211100, China; Zhuojia Fu, College of Mechanics and Engineering Science, Hohai University, Nanjing, Jiangsu, 211100, China; Guohai Chen, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116024, China; Dixiong Yang, Department of Engineering Mechanics, International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116024, China
Über dieses Buch
The randomness in structural parameters of printed circuit heat exchangers (PCHEs) is unavoidable and can significantly impact shakedown limit and reliability. However, studies on the influence of random structural parameters are limited due to the expensive computation burden. Therefore, this paper aims to achieve efficient stochastic shakedown and reliability analyses of PCHEs with multiple random structural parameters. First, the Linear Matching Method (LMM) is utilized to perform the shakedown analysis. Then, to account for the influence of random structural parameters, the Direct Probability Integral Method (DPIM), as an efficient non-intrusive stochastic analysis method, is introduced. By exploiting the advantages of LMM and DPIM, a novel LMM-DPIM is proposed for the uncertainty qualification analysis of PCHEs. Furthermore, the data-driven neural network model is implemented to improve the computational efficiency. As a result, an LMM-DPIM-based neural network (LMM-DPIM-NN) model is established. Finally, the high accuracy and efficiency of the proposed model are verified through comparisons with Monte Carlo simulation. Moreover, the results of uncertainty qualification analysis reveal the effects of different random structural parameters on the shakedown limit and reliability of PCHE. Particularly, the fillet radius at the corner of channels significantly influences the shakedown limit, leading to a huge reduction in reliability.
Veröffentlicht
21.08.2026
Copyright (c) 2026 Matthias Faes, Michael Beer, Julian Behrend, Marco Broccardo, Guohai Chen, Hanshu Chen, Jieyu Chen, Fang Cong, Henrik Ebel, Peter Eberhard, Andrea Franchini, Zhuojia Fu, Yanzhao Gu, Michael Hanss, Hui Huang, Md Maidul Islam, Jafar Jafari-Asl, Balsa Jovanovic, Tom Könecke, Olga Kosheleva, Vladik Kreinovich, Hao-Ming Li, Pei-Pei Li, Qinghe Li, Ruikun Li, Stefano Marelli, Rafi L. Muhanna, Robert L. Mullen, Chiara Nardin, Lukáš Novák, Shenghao Piao, Mario Rosenfelder, Styfen Schär, Jan Schneider, Wei Shi, Panagiotis Spyridis, Bruno Sudret, Ruben Van Coile, Bo-Yu Wang, Dixiong Yang, Xuan-Yi Zhang, Zi-Xin Zhang, Bo-Qi Zhao, Yan-Gang Zhao, Zhibao Zheng, Baihong Zhong, Zhaowei Zhou, Marcos A. Valdebenito, Chao Dang, Lan-Xin Li, Marius Bittner, Parth Tambat, Marco Behrendt, Zhao Liu, Ping Zhu, Zlatan Dimitrov, Petar O. Hristov, Meng-Ze Lyu, Yang-Yi Liu, Jian-Bing Chen, Qitian Lu, Miroslav Vořechovský, Ekaterina Auer, Lorenz Gillner, Julien Alexandre dit Sandretto, Binod K. Yadav, Ghifari Adam Faza, Hans Hallez, David Moens, Reza Naeimaei, Steffen Schön, Elisabeth Ötsch, Hans Neuner, Zhanhua Liang, Jingwen Song, Michael Desch, Mehdi Modares, Valentin Nikolov, Paulo Lucas Figueiredo, José Torres Farinha, Hugo Raposo, António J. Marques Cardoso, Alice do Carmo Duarte Rodrigues, Paula Gonçalves, Peihan Chen, Xinyu Zhu, Pengfei Wei, Kaiwen Li, Mansoureh Shahabi, Y. F. Cui, Johannes O. Royset, Salvatore Russotto, Mario Di Paola, Antonina Pirrotta, Til Lux, Johannes Sundheim, Tania Feiri, Udo Wiens, Marcus Ricker, F. Niklas Schietzold, Felix Harazin, Yoshi Diepelt, Wolfgang Graf, Michael Kaliske, Rongyao Song, Changcong Zhou, Xuanyi Zhao, Weiping Zhang, Juntao Hu, Yu Chen, Edoardo Patelli, Tingting Sun, Jianbing Chen, Bouwe Verkens, L. Bogaerts, Patrick Van Rymenant, Maximilian Schweizer, Marc Fina, Werner Wagner, Steffen Freitag, George Stefanou, Dimitros Savvas, Panagiotis Gavalas, Luigi Schiano, Razhan S. Ahmed, Oleksandr Al-Shboul, David Ringeloth, Vladislav Gudžulić, Stefanie Schoen, Jelena Bijeljić, Ernst Niederleithinger, Iurie Curoşu, Gerrit E. Neu, Jia-Hui Fu, Yi Luo, De-Cheng Feng, Xiao-Qiu Ai, Luyi Li, Hao Wang, Tairan Wang, Sifeng Bi, Emily Zeller (Kapitelautor/in)
Zitationsvorschlag
Neural network-driven uncertainty qualification analysis of printed circuit heat exchangers. (2026). In
REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World (pp. 507-515). TUDObooks.
https://doi.org/10.17877/tudobooks-11.151
