利用参数化模型降阶法对电力电子模块热力学分析中温度相关特性进行参数化

S. Hassan, P. Rajaguru, S. Stoyanov, C. Bailey
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引用次数: 1

摘要

本文将ANSYS-FEM(有限元法)与参数化模型降阶(pMOR)技术相结合,对电力电子模块(PEM)进行了直接耦合热-力分析。与目前大多数模型降阶研究采用顺序耦合热-力学模型进行热-力学耦合分析不同,本研究采用直接耦合热-力学方法同时求解热模型和结构模型。通常,pMOR主要侧重于参数化模型参数(例如,材料属性,载荷),这些参数是常数。在这项研究中,在电子模块可靠性评估的背景下,已经证明了一种使用pMOR参数化温度相关特性的新方法,例如PEM结构中材料的热膨胀系数(CTE)。建立了PEM的二维有限元模型,并应用该模型研究了在热载荷作用下,铝(Al)合金的温度相关CTE效应对系统热力学响应的影响。基于Krylov子空间的PRIMA技术被用于模型降阶,矩阵插值的线性方法被用于pMOR的参数化。全阶状态空间模型有30,612个自由度,而pMOR实现的简化模型只有8个自由度。仿真运行表明,使用这种方法,可以实现计算时间的大幅减少,对于这个问题,在全阶模型和降阶模型之间减少了81%。在建模预测中,基于pmor的解决方案保留了结果的准确性。在这种情况下,与ANSYS-FEM模型(FOM)解决方案相比,应力结果的平均差异仅为0.43%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parametrising Temperature Dependent Properties in Thermal-Mechanical Analysis of Power Electronics Modules using Parametric Model Order Reduction
In this paper, a direct-coupled thermal-mechanical analysis of a Power Electronics Modules (PEM) using ANSYS-FEM (Finite Element Method) is integrated with a Parametric Model Order Reduction (pMOR) technique. Unlike most present studies on model order reduction, which perform the coupled thermal-mechanical analysis by sequential-coupled thermal-mechanical models, the direct-coupled thermal-mechanical approach deployed in this study solves the thermal and structural models simultaneously. Commonly, pMOR mainly focuses on parametrising model parameters (e.g., material properties, loads.) that are constants. In this investigation, a new approach to parametrise temperature-dependent properties using pMOR, such as the coefficient of thermal expansion (CTE) of the materials in PEM structures, has been demonstrated in the context of the reliability assessment of electronic modules. A two-dimensional finite element model of a PEM is developed and used to study the temperature-dependent CTE effects of the Aluminium (Al) alloy on the thermal-mechanical response of the system under thermal load. A Krylov subspace-based technique, PRIMA, has been used for the model order reduction and a linear approach of matrix interpolation for the parametrisation in the pMOR. The full-order state-space model has 30,612 degrees of freedom (DOFs), and the reduced model achieved by pMOR has just 8 DOFs. The simulation runs show that with this approach, a substantial reduction in computational time can be achieved, for this problem, by 81% between the full and the reduced order models. In modelling predictions, the pMOR-based solution has retained the accuracy of results. In this instance, the average difference in stress result, compared to the ANSYS-FEM model (FOM) solution, is only 0.43%.
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