Results for the heat transfer of a fin with exponential-law temperature-dependent thermal conductivity and power-law temperature-dependent heat transfer coefficients

IF 2.4 Q2 ENGINEERING, MECHANICAL Nonlinear Engineering - Modeling and Application Pub Date : 2022-01-01 DOI:10.1515/nleng-2022-0005
E. Shivanian, Leyla AhmadSoltani, F. Sohrabi
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Abstract

Abstract In this article, thermal behavior analysis of nonlinear fin problem with power-law heat transfer coefficient is studied to determine temperature distribution. This new supposition for the thermal conductivity, exponential-law temperature dependent, makes it to be nonlinear that is a general case in some sense. It is shown that the governing fin equation, that is, a nonlinear second-order differential equation, is exactly solvable with proper boundary conditions. To this purpose, the order of differential equation is reduced and then is converted into a total differential equation by multiplying a proper integration operant. An exact analytical solution is given to advance physical meaning, and the existence of unique solution for some specific values of the parameters of the model is demonstrated. The results are shown graphically. It is observed that fin efficiency is decreasing with respect to the power-law mode for heat transfer.
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具有指数律温度相关导热系数和幂律温度相关传热系数的翅片传热的结果
本文研究了具有幂律传热系数的非线性翅片问题的热行为分析,以确定温度分布。这个关于热导率的新假设,指数律温度依赖,使得它是非线性的,这在某种意义上是一般情况。结果表明,在适当的边界条件下,控制鳍方程即非线性二阶微分方程是精确可解的。为此,先将微分方程的阶数化简,再通过适当的积分运算变换为全微分方程。给出了精确解析解,提高了模型的物理意义,并证明了模型参数的某些特定值存在唯一解。结果用图形表示。观察到翅片效率相对于幂律传热模式呈下降趋势。
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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