Application of machine learning for predicting heat transfer coefficient in dropwise condensation of steam
Khan, Rizwan Ali
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https://hdl.handle.net/2142/120592
Description
Title
Application of machine learning for predicting heat transfer coefficient in dropwise condensation of steam
Author(s)
Khan, Rizwan Ali
Issue Date
2023-05-05
Director of Research (if dissertation) or Advisor (if thesis)
Miljkovic, Nenad
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Dropwise Condensation
Abstract
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.
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