Machine and deep learning driven models for the design of heat exchangers with micro-finned tubes-Reference-Cited by-同舟云学术

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Machine and deep learning driven models for the design of heat exchangers with micro-finned tubes

Published:2024-05 Issue: Volume:16 Page:100370
ISSN:2666-5468
Container-title:Energy and AI
language:en
Short-container-title:Energy and AI

Author:

Efatinasab Emad,Irannezhad Nima,Rampazzo Mirco,Diani Andrea^ORCID

Publisher

Elsevier BV

Reference52 articles.

1. Condensation in horizontal smooth tubes: A new heat transfer model for heat exchanger design;Cavallini;Heat Transf Eng,2006

2. Universal approach to predicting heat transfer coefficient for condensing mini/micro-channel flow;Kim;Int J Heat Mass Transfer,2013

3. Condensation of pure and near-azeotropic refrigerants in microfin tubes: A new computational procedure;Cavallini;Int J Refrig,2009

4. An improved and extended general correlation for heat transfer during condensation in plain tubes;Shah;HVAC R Res,2009

5. Development of the general correlation for condensation heat transfer and pressure drop inside horizontal 4 mm small-diameter smooth and microfin tubes;Hirose;Int J Refrig,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Novel dimensionless predictive flow pattern map for HFOs inside microscale enhanced tubes;International Journal of Thermofluids;2024-11

2. Comprehensive study of flow boiling modeling inside helical micro-finned tubes: Empirical, non-convex optimization and deep learning predictive models;International Journal of Heat and Mass Transfer;2024-10

3. Prediction of thermo-hydraulic properties of flow in an innovative plate heat exchanger using machine learning algorithms;Physica Scripta;2024-09-04

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