Multi-parameter classification and quantification of R-134a condensation using machine learning-Reference-Cited by-同舟云学术

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Multi-parameter classification and quantification of R-134a condensation using machine learning

Published:2023-08 Issue: Volume:231 Page:120880
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Silva Junior Luiz H.,Barbosa Jader R.,da Silva Alexandre K.

Funder

Agência Nacional do Petróleo, Gás Natural e Biocombustíveis

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Energy Engineering and Power Technology,Fluid Flow and Transfer Processes,Mechanical Engineering

Reference33 articles.

1. Advanced Engineering Thermodynamics;Bejan,2006

2. Thermo-Fluid Dynamics of Two-Phase Flow;Ishii,2011

3. One-dimensional two-phase flow;Wallis,1969

4. Nucleate pool boiling simulations using the interface tracking method: Boiling regime from discrete bubble to vapor mushroom region;Sato;Int. J. Heat Mass Transf.,2017

5. Direct numerical simulation of nucleate pool boiling at large microscopic contact angle and moderate Jakob number;Huber;Int. J. Heat Mass Transf.,2017

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1. Image-based quantification of pool boiling heat flux on varied heating surfaces: Enhancing prediction performance with automated machine learning;Applied Thermal Engineering;2024-11

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