利用 CFD 多相混合模型对水-al2o3 纳米流体传热增强模拟进行数值不确定性量化的人工神经网络

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-02-27 DOI:10.1080/10407790.2024.2320719

Suaib Al Mahmud, Wazed Ibne Noor, Mazbahur Rahman Khan, Ahmad Faris Ismail

引用次数: 0

摘要

使用计算流体动力学（CFD）等数值方法模拟纳米流体传热增强是一种流行的做法。众所周知，误差在数值计算中起着重要作用。

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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Artificial neural network for numerical uncertainty quantification of water-al2o3 nanofluids heat transfer enhancement simulation using CFD multiphase mixture model

Simulating nanofluids heat transfer enhancement using numerical methods like Computational Fluid Dynamics (CFD) is a popular practice. Whereas it’s known that errors play an important role in numer...

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来源期刊

Numerical Heat Transfer Part B-Fundamentals 工程技术-力学

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

2.8 months

期刊介绍： Published 12 times per year, Numerical Heat Transfer, Part B: Fundamentals addresses all aspects of the methodology for the numerical solution of problems in heat and mass transfer as well as fluid flow. The journal’s scope also encompasses modeling of complex physical phenomena that serves as a foundation for attaining numerical solutions, and includes numerical or experimental results that support methodology development. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. The Editor reserves the right to reject without peer review any papers deemed unsuitable.