A fast methodology for identifying thermal parameters based on improved POD and particle swarm optimization and its applications

IF 4.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Analysis with Boundary Elements Pub Date : 2024-10-19 DOI:10.1016/j.enganabound.2024.106001

Zhenkun Cao , Chengbao Sun , Miao Cui , Ling Zhou , Kun Liu

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引用次数: 0

Abstract

The identification method based on the traditional Proper Orthogonal Decomposition (POD) reduced-order model has the problem of low efficiency, due to the large amount of both data and computation, when dealing with complicated problems with a large number of spatially distributed nodes. To deal with this issue, an improved POD reduced-order model is proposed in this work. The improved POD reduced-order model only requires the establishment of a three-dimensional (3D) database of training samples varied with both time and measurement locations. Therefore, the amount of data and computation is independent of the total number of spatially distributed nodes, which enables the amount of data and computation to be greatly reduced. To identify thermal parameters in heat conduction problems, a database of transient temperature field is constructed with different training parameters and space nodes by using polygonal boundary element method, and a set of POD basis vectors is obtained by the POD reduced-order model. Then, a surrogate model combined with the improved particle swarm optimization (PSO) is employed for the identification of thermal parameters. Three different inverse heat conduction problems are designed and analyzed to verify the performance of the improved methodology. The results show that the efficiency of the modified method is superior to the traditional POD method with comparable accuracy. The more of the number of spatially distributed nodes, the more obvious advantages of the efficiency. Furthermore, this method has been tested on noisy data, proving its reliability in dealing with problems arising from measurement errors.

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基于改进型 POD 和粒子群优化的热参数快速识别方法及其应用

在处理具有大量空间分布节点的复杂问题时，基于传统的适当正交分解（POD）降阶模型的识别方法存在数据量和计算量都很大、效率低的问题。为解决这一问题，本文提出了一种改进的 POD 降阶模型。改进后的 POD 降阶模型只需要建立一个三维（3D）数据库，其中包含随时间和测量位置而变化的训练样本。因此，数据量和计算量与空间分布节点的总数无关，从而大大减少了数据量和计算量。为了识别热传导问题中的热参数，利用多边形边界元法构建了具有不同训练参数和空间节点的瞬态温度场数据库，并通过 POD 降阶模型获得了一组 POD 基向量。然后，代用模型与改进的粒子群优化（PSO）相结合，用于识别热参数。设计并分析了三个不同的反热传导问题，以验证改进方法的性能。结果表明，改进方法的效率优于传统的 POD 方法，且精度相当。空间分布节点数越多，效率优势越明显。此外，该方法还在噪声数据上进行了测试，证明了其在处理测量误差引起的问题方面的可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Analysis with Boundary Elements 工程技术-工程：综合

CiteScore

5.50

自引率

18.20%

发文量

368

审稿时长

56 days

期刊介绍： This journal is specifically dedicated to the dissemination of the latest developments of new engineering analysis techniques using boundary elements and other mesh reduction methods. Boundary element (BEM) and mesh reduction methods (MRM) are very active areas of research with the techniques being applied to solve increasingly complex problems. The journal stresses the importance of these applications as well as their computational aspects, reliability and robustness. The main criteria for publication will be the originality of the work being reported, its potential usefulness and applications of the methods to new fields. In addition to regular issues, the journal publishes a series of special issues dealing with specific areas of current research. The journal has, for many years, provided a channel of communication between academics and industrial researchers working in mesh reduction methods Fields Covered: • Boundary Element Methods (BEM) • Mesh Reduction Methods (MRM) • Meshless Methods • Integral Equations • Applications of BEM/MRM in Engineering • Numerical Methods related to BEM/MRM • Computational Techniques • Combination of Different Methods • Advanced Formulations.