A port-Hamiltonian formulation of coupled heat transfer

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2022-02-27 DOI:10.1080/13873954.2022.2038637

Jens Jäschke, M. Ehrhardt, M. Günther, B. Jacob

引用次数: 4

Abstract

ABSTRACT Heat transfer and cooling solutions play an important role in the design of gas turbine blades. However, the underlying mathematical coupling structures have not been thoroughly investigated. In this work, the port-Hamiltonian formalism is applied to the conjugate heat transfer problem in gas turbine blades. A mathematical model based on common engineering simplifications is constructed and further simplified to reduce complexity and focus on the coupling structures of interest. The model is then cast as a port-Hamiltonian system and examined for stability and well posedness.

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耦合传热的端口哈密顿公式

传热与冷却方案在燃气轮机叶片设计中占有重要地位。然而，潜在的数学耦合结构尚未得到彻底的研究。本文将端口-哈密顿形式应用于燃气轮机叶片的共轭传热问题。建立了一个基于通用工程简化的数学模型，并对其进行了进一步简化，以降低复杂性并关注感兴趣的耦合结构。然后将该模型转换为一个波特-哈密顿系统，并检查其稳定性和适定性。

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

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.