Application of machine learning for predicting heat transfer coefficient in dropwise condensation of steam

Dropwise Condensation (DWC) refers to a phase-change process, where condensation of vapors is manifested as distinct droplets on a non-wetting surface. Unlike filmwise condensation (FWC), DWC results in considerably higher heat transfer coefficients (HTC), which could lead to efficient heat transfer in many industrial applications and enable the design of smaller condensers. There have been, however, vast discrepancies among the results presented by researchers in this field since the 1960s, when the experimentation for characterizing DWC took pace. In this study, the effect of four different parameters on external DWC of steam is analyzed by applying machine learning algorithms to a vast amount of historical data spanning eight decades. Steam was chosen as a working fluid due to high latent heat of vaporization of water, its widespread and diverse industrial use, and the only fluid for which extensive experimental data is available.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Khan, Rizwan Ali

Contributors dc:contributor

• Miljkovic, Nenad

• Dropwise Condensation

Statement dc:rights

• Copyright 2023 Rizwan Khan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120592

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120592