时间分数阶非线性换热方程的预测校正数值模拟

IF 2.6 Q2 THERMODYNAMICS
Heat Transfer Pub Date : 2025-07-17 DOI:10.1002/htj.70017
Daasara Keshavamurthy Archana, Doddabhadrappla Gowda Prakasha
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引用次数: 0

摘要

本文利用变化比热系数来解决两种非线性传热问题。随着机械工程处于最前沿,高效的传热对许多工程系统的有效性、安全性和性能至关重要。热量的传递通过多种途径发生,包括传导、对流和辐射,每种途径在塑造现代技术的热景观中都起着至关重要的作用。我们提出了一种分数阶方法来降低研究的复杂性。在这里,使用卡普托分数阶导数(CFD),我们使用预测校正方法(PCM)检查和评估所考虑问题的解决方案。为了验证所考虑方法的可靠性,将所得结果与变分迭代法(VIM)、同伦摄动法(HPM)、厄米特小波法(HWM)、微分变换法(DTM)及精确解的结果进行了比较。PCM通常更适合实际的数值计算,特别是在需要实现的灵活性、稳定性和可访问性时。结果表明,PCM能较好地预测此类问题的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Numerical Simulation of Time Fractional Nonlinear Heat Transfer Equation With Predictor-Corrector Approach

Numerical Simulation of Time Fractional Nonlinear Heat Transfer Equation With Predictor-Corrector Approach

This article enables the changing specific heat coefficient to solve two nonlinear heat transfer problems. With mechanical engineering at the cutting edge, efficient heat transmission is essential to the effectiveness, safety, and performance of many engineering systems. The transfer of heat occurs through multiple pathways, including conduction, convection, and radiation, each playing a vital role in shaping the thermal landscape of modern technologies. We propose a fractional-order approach to reduce the complexity of the study. Here, using the Caputo fractional derivative (CFD), we examine and evaluate the solutions of the considered problems using the Predictor-Corrector method (PCM). To verify the reliability of the considered approach, the present findings are compared with those of the variational iteration method (VIM), homotopy perturbation method (HPM), hermite wavelet method (HWM), differential transformation method (DTM) and the accurate solutions. The PCM is typically more advantageous for actual numerical calculations, particularly when flexibility, stability, and accessibility of implementation are needed. The outcomes suggest that the PCM can forecast the solution of such problems with more appropriate results.

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来源期刊
Heat Transfer
Heat Transfer THERMODYNAMICS-
CiteScore
6.30
自引率
19.40%
发文量
342
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