Applicability of machine learning techniques in predicting specific heat capacity of complex nanofluids

IF 1.7 4区工程技术 Q3 THERMODYNAMICS

Heat Transfer Research Pub Date : 2023-01-01 DOI:10.1615/heattransres.2023049494

YoungSuk Oh, Zhixiong Guo

引用次数: 0

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机器学习技术在预测复杂纳米流体比热容中的适用性

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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来源期刊

Heat Transfer Research 工程技术-热力学

CiteScore

3.10

自引率

23.50%

发文量

102

审稿时长

13.2 months

期刊介绍： Heat Transfer Research (ISSN1064-2285) presents archived theoretical, applied, and experimental papers selected globally. Selected papers from technical conference proceedings and academic laboratory reports are also published. Papers are selected and reviewed by a group of expert associate editors, guided by a distinguished advisory board, and represent the best of current work in the field. Heat Transfer Research is published under an exclusive license to Begell House, Inc., in full compliance with the International Copyright Convention. Subjects covered in Heat Transfer Research encompass the entire field of heat transfer and relevant areas of fluid dynamics, including conduction, convection and radiation, phase change phenomena including boiling and solidification, heat exchanger design and testing, heat transfer in nuclear reactors, mass transfer, geothermal heat recovery, multi-scale heat transfer, heat and mass transfer in alternative energy systems, and thermophysical properties of materials.