采用人工神经网络方法模拟滑移条件下浓度对优化水基混合纳米流体传热率的影响：回归分析

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-04-01 DOI:10.1080/10407790.2024.2333944

Subhajit Panda, Arun Prakash Baag, P.K Pattnaik, Rupa Baithalu, S.R Mishra

引用次数: 0

摘要

近代科学的发展依赖于若干工程应用中热传导率的高效表现，而纳米流体的作用对这些应用具有较大的影响。因此，...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Artificial neural network approach to simulate the impact of concentration in optimizing heat transfer rate on water-based hybrid nanofluid under slip conditions: A regression analysis

The recent era of science depends upon the efficient performance of the heat transfer rate in several engineering applications for which the role of nanofluids have a greater impact. Therefore, the...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.