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Optimization of the volume-to-point heat conduction problem with automatic differentiation based approach

机译：基于自动分化的方法优化体积点热传导问题

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摘要

Heat conduction optimization problems, such as the volume-to-point problem, have always been core issues in industry and electronics cooling. Existing approaches either limit the solution space by using priori structures, or use manual operation rules to adjust the solutions gradually. And there does not exist a general way to deal with arbitrary objectives. The present study develops a novel method by using automatic differentiation technique. The heat conduction process is converted into a recurrent convolu-tional neural network equivalently, and the automatic differentiation technique is utilized to calculate the gradient of the final objective with respect to the thermal conductivity field directly. Based on these techniques, the thermal conductivity field is optimized directly to minimize the hot spot temperature. Cases with different heat sinks are introduced to test the proposed optimization method, respectively. Results demonstrate that the average temperature and hot spot temperature are both reduced remarkably after using the proposed method. Furthermore, compared with the previous iteration methods based on entropy generation minimization or entransy dissipation minimization, the proposed method produces similar results when minimizing the average temperature, and reduces the hot spot temperature rising by 17% ~ 33% when minimizing the hot spot temperature. The optimization method based on automatic differentiation technique exhibits the potential capability of handling with various types of objectives.
机译：热传导优化问题，如体积点问题，始终是工业和电子冷却的核心问题。现有方法通过使用先验结构来限制解决方案空间，或者使用手动操作规则逐渐调整解决方案。并且不存在处理任意目标的一般方法。本研究通过使用自动分化技术开发一种新方法。导热过程等效地转换成经常性卷积神经网络，并且使用自动分化技术直接计算最终目标的梯度。基于这些技术，导热场直接优化以使热点温度最小化。引入不同散热器的案例分别测试所提出的优化方法。结果表明，使用该方法后，平均温度和热点温度均显着降低。此外，与基于熵产生最小化或延长耗散最小化的先前的迭代方法相比，当最小化平均温度时，所提出的方法产生类似的结果，并在最小化热点温度时降低17％〜33％的热点温度。基于自动分化技术的优化方法表现出用各种类型的目标处理的潜在能力。

著录项

  • 来源
    《International Journal of Heat and Mass Transfer》 |2021年第10期|121552.1-121552.11|共11页
  • 作者

    Mengxuan Song; Kai Chen; Xing Zhang;

  • 作者单位

    Department of Control Science and Engineering Tongji University Shanghai 201804 P. R. China;

    Key Laboratory of Enhanced Heat Transfer and Energy Conservation of the Ministry of Education School of Chemistry and Chemical Engineering South China University of Technology Guangzhou 510640 Guangdong P. R. China;

    Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering Mechanics Tsinghua University Beijing 100084 P. R. China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Heat conduction optimization; Automatic differentiation; VP Problem; Hot spot temperature;

    机译：导热优化;自动分化;vp问题;热点温度;

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