Dynamic Design Optimization for Thermal Management: A Case Study on Shell and Tube Heat Exchangers

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9212

Austin L. Nash, Neera Jain

引用次数: 1

Abstract

We present a new methodology for designing a heat exchanger that explicitly considers both static and transient performance characteristics. The proposed approach leverages 1) a highly detailed, albeit static model that captures the complex nonlinear relationship between heat exchanger geometry and heat transfer coefficients, and 2) a reduced-order dynamic model of the heat exchanger that approximates the geometry detailed in the static model. In order to optimize the component design for both static and transient performance metrics, pole locations of the corresponding linearized model are penalized in the cost function of the proposed optimization algorithm in order to move dominant poles further into the left half complex plane. Through a simulated case study for a shell and tube heat exchanger, we demonstrate how the proposed algorithm exploits the trade off between static design metrics, including mass and footprint, and the rate at which heat is removed from the primary fluid.

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热管理的动态优化设计:以管壳式换热器为例

我们提出了一种设计热交换器的新方法，明确地考虑了静态和瞬态性能特征。所提出的方法利用了1)一个非常详细的静态模型，该模型捕获了换热器几何形状和传热系数之间复杂的非线性关系，以及2)换热器的降阶动态模型，该模型近似于静态模型中详细的几何形状。为了优化静态和瞬态性能指标的组件设计，在所提出的优化算法的代价函数中对相应线性化模型的极点位置进行惩罚，以便将主导极点进一步移动到左半复平面。通过壳管式热交换器的模拟案例研究，我们展示了所提出的算法如何利用静态设计指标(包括质量和占地面积)与从一次流体中去除热量的速率之间的权衡。

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

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.