Optimization of a stirling engine by variable-step simplified conjugate-gradient method and neural network training algorithm

Chin Hsiang Cheng, Yu Ting Lin

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

The present study develops a novel optimization method for designing a Stirling engine by combining a variable-step simplified conjugate gradient method (VSCGM) and a neural network training algorithm. As compared with existing gradient-based methods, like the conjugate gradient method (CGM) and simplified conjugate gradient method (SCGM), the VSCGM method is a further modified version presented in this study which allows the convergence speed to be greatly accelerated while the form of the objective function can still be defined flexibly. Through the automatic adjustment of the variable step size, the optimal design is reached more efficiently and accurately. Therefore, the VSCGM appears to be a potential and alternative tool in a variety of engineering applications. In this study, optimization of a low-temperature-differential gamma-type Stirling engine was attempted as a test case. The optimizer was trained by the neural network algorithm based on the training data provided from three-dimensional computational fluid dynamic (CFD) computation. The optimal design of the influential parameters of the Stirling engine is yielded efficiently. Results show that the indicated work and thermal efficiency are increased with the present approach by 102.93% and 5.24%, respectively. Robustness of the VSCGM is tested by giving different sets of initial guesses.

原文English
文章編號5164
期刊Energies
13
發行號19
DOIs
出版狀態Published - 2020 10月

All Science Journal Classification (ASJC) codes

  • 可再生能源、永續發展與環境
  • 建築與營造
  • 燃料技術
  • 工程(雜項)
  • 能源工程與電力技術
  • 能源(雜項)
  • 控制和優化
  • 電氣與電子工程

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