Abstract A key challenge in the development of a practical thermal storage device (TSD) is the low thermal conductivity of common phase change materials (PCM). This low conductivity impedes both heat input and extraction. The most common solution is to use conductive metal fins to spread heat through the device. However, optimizing the effectiveness of the container and the fin arrangement is difficult due to the large number of potential design parameters. This paper develops a strategy to make simulation-based optimization process affordable and accurate. First, numerical techniques are designed to accurately and efficiently compute heat and mass transport in a variety of geometries without generating grids to conform to each geometry. This facilitates rapid prototyping and mitigates the expense of individual simulations. Second, a pre-screening process identifies the independent variables with the largest and most nonlinear effect on the objective function in the optimization process, thus narrowing the parameter space. Finally, a dynamic Kriging-based optimization approach constructs a multidimensional response surface using sparse input datasets; the response surface is then used to identify an optimal design. The combination of the above three strategies is shown to result in an approach that can aid in the design of optimal thermal storage devices that rely on a mixture of PCM and metal fins.

中文翻译：

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使用动态克里金法优化基于 PCM 的热存储设备的翅片设计

摘要 开发实用储热装置 (TSD) 的一个关键挑战是普通相变材料 (PCM) 的低热导率。这种低传导性阻碍了热量的输入和提取。最常见的解决方案是使用导电金属翅片通过设备散布热量。然而，由于大量潜在的设计参数，优化容器和翅片布置的效率是困难的。本文开发了一种策略，使基于仿真的优化过程经济实惠且准确。首先，数值技术旨在准确有效地计算各种几何形状中的热量和质量传输，而无需生成符合每种几何形状的网格。这有利于快速原型制作并减少单个模拟的费用。第二，预筛选过程在优化过程中识别对目标函数影响最大和非线性程度最大的自变量，从而缩小参数空间。最后，基于动态克里金法的优化方法使用稀疏输入数据集构建多维响应面；然后使用响应面来确定最佳设计。上述三种策略的组合显示出一种方法，可以帮助设计依赖于 PCM 和金属翅片的混合物的最佳热存储设备。基于动态克里金法的优化方法使用稀疏输入数据集构建多维响应面；然后使用响应面来确定最佳设计。上述三种策略的组合显示出一种方法，可以帮助设计依赖于 PCM 和金属翅片的混合物的最佳热存储设备。基于动态克里金法的优化方法使用稀疏输入数据集构建多维响应面；然后使用响应面来确定最佳设计。上述三种策略的组合显示出一种方法，可以帮助设计依赖于 PCM 和金属翅片的混合物的最佳热存储设备。