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Mathematics > Numerical Analysis

arXiv:2403.03268v2 (math)
[Submitted on 5 Mar 2024 (v1) , last revised 25 Apr 2024 (this version, v2)]

Title: A Transient Thermal Model for Power Electronics Systems

Title: 用于电力电子系统的瞬态热模型

Authors:Neelakantan Padmanabhan
Abstract: An equation based reduced order model applicable to generalized heat equation and thermal simulations of power electronics systems developed in commercial CFD tools, is presented in this work. The model considers the physics of heat transfer between multiple objects in different mediums and presents a set of equations that can be applied to a wide range of heat transfer scenarios including conduction, natural and forced convection problems. A few case studies including heat transfer in a power electronic system are simulated in Ansys Icepak and the temperatures from the simulations are compared with the temperatures predicted by the models. The models are observed to be highly accurate when compared with the simulations. The predictive model described in this work reduces large complex simulations down to a few parameters which tremendously improves the computation speed, uses very low physical disk space and enables fast evaluation of thermal performance of the system for any changes in the input parameters.
Abstract: 基于方程的降阶模型,适用于广义热方程和商用计算流体力学工具中的电力电子系统热模拟,本工作进行了介绍。 该模型考虑了不同介质中多个物体之间的传热物理过程,并提供了一组可用于广泛传热场景的方程,包括导热、自然对流和强制对流问题。 一些案例研究,包括电力电子系统中的传热,在Ansys Icepak中进行了模拟,模拟得到的温度与模型预测的温度进行了比较。 与模拟结果相比,模型显示出高度的准确性。 本工作中描述的预测模型将大型复杂的模拟简化为几个参数,极大地提高了计算速度,使用非常低的物理磁盘空间,并能够快速评估系统在输入参数变化时的热性能。
Comments: Accepted for publication in IEEE Southeastcon 24
Subjects: Numerical Analysis (math.NA)
Cite as: arXiv:2403.03268 [math.NA]
  (or arXiv:2403.03268v2 [math.NA] for this version)
  https://doi.org/10.48550/arXiv.2403.03268
arXiv-issued DOI via DataCite
Related DOI: https://doi.org/10.1109/SoutheastCon52093.2024.10500091
DOI(s) linking to related resources

Submission history

From: Neelakantan Padmanabhan [view email]
[v1] Tue, 5 Mar 2024 19:05:27 UTC (1,232 KB)
[v2] Thu, 25 Apr 2024 15:03:33 UTC (1,232 KB)
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