生活热水储罐排放过程的近似求解

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
F. Müller
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引用次数: 0

摘要

摘要:本文提出了一种新的生活热水储罐排放过程温度的近似计算方法。这个过程的主要思想是对出现在储罐内的边界层的动态行为进行数学描述。这使得储罐内部温度分布的近似解结构简单,但在整个排料过程中精度很高。为了验证该近似解与利用广义傅里叶系数构造的精确解以及用半离散方法得到的数值解进行了比较。对该方法进行调整,还可以得到充电过程的近似过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximating the solution of the discharging process in a domestic hot water storage tank
ABSTRACT In this article a new approximation procedure for the temperature during the discharging process in a domestic hot water storage tank is developed. The main idea of this procedure is the mathematical description of the dynamic behaviour of a boundary layer that appears inside the storage tank. This leads to an approximate solution of a simple structure but a high accuracy throughout the whole discharging process for the temperature distribution inside the storage tank. For validation the approximate solution is compared with the exact solution that is constructed with help of generalized Fourier coefficients and a numerical solution that is obtained by a semi-discretization method. Adapting the method leads also to an approximation procedure for charging processes.
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
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.
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