Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

Montañez Barrera, J.A.; Barroso Maldonado, Juan Manuel; Bedoya Santa Cruz, A.F.; Mota Babiloni, Adrián

doi:https://doi.org/10.1016/j.ijheatmasstransfer.2022.123017

0 Downloads0 Views0% Trend

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

Montañez Barrera, J.A., Barroso Maldonado, Juan Manuel, Bedoya Santa Cruz, A.F., Mota Babiloni, Adrián

Summary

Abstract

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pr...

Sponsorship

Science Direct

Keywords: Two-phase flow Pressure drop Zeotropic mixtures Machine learning Transfer learning ANN Micro-channels

DOI

https://doi.org/10.1016/j.ijheatmasstransfer.2022.123017

URI

https://repositorio.cetys.mx/handle/60000/1446

Creative Commons license

Except where otherwised noted, this item's license is described as Atribución-NoComercial-CompartirIgual 2.5 México

Usage statistics

Full item page

Date

Type

Pages

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

Summary

Abstract

Description

Advisor

Journal Title

Volume Title

Sponsorship

Citation

Endorsement

Review

Supplemented By

Referenced By

DOI

URI

Publisher

Journal ISSN

Creative Commons license

Usage statistics

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

Summary

Abstract

Description

Advisor

Journal Title

Volume Title

Sponsorship

Citation

Endorsement

Review

Supplemented By

Referenced By

DOI

URI

Publisher

Journal ISSN

Creative Commons license

Usage statistics

Related publications

Other author title(s)