2026-09-18T22:56:24Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120592 2023-09-07 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Miljkovic, Nenad 2023-05 application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01 The student, Rizwan Khan, accepted the attached license on 2023-05-04 at 16:23. The student, Rizwan Khan, submitted this Thesis for approval on 2023-05-04 at 16:39. This Thesis was approved for publication on 2023-05-05 at 08:32. DSpace SAF Submission Ingestion Package generated from Vireo submission #19339 on 2023-09-01 at 17:22:24 Application of machine learning for predicting heat transfer coefficient in dropwise condensation of steam Khan, Rizwan Ali 2023-05-05 Dropwise Condensation 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. Text eng https://hdl.handle.net/2142/120592 Copyright 2023 Rizwan Khan M.S. Thesis Mechanical Engineering University of Illinois at Urbana-Champaign Mechanical Sci & Engineering