{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127310"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127310","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An intelligent strategy for phase change heat and mass transfer: Application of machine learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Khodakarami, Siavash"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Miljkovic, Nenad","Jacobi, Anthony","Feng, Jie","Stillwell, Andrew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08-01","date_published":"2024-08-01","updated_at":"2026-07-22T22:25:03Z","subjects":["Condensation","Machine Learning, Infrared","Thermography","Generative Adversarial Network"],"languages":["en","eng"],"rights":["Copyright 2024 Siavash Khodakarami"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127310","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Miljkovic, Nenad","Jacobi, Anthony","Feng, Jie","Stillwell, Andrew"]},{"key":"dc:creator","label":"Author","values":["Khodakarami, Siavash"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-08-01","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Condensation","Machine Learning, Infrared","Thermography","Generative Adversarial Network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Siavash Khodakarami"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127310"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Siavash Khodakarami, accepted the attached license on 2024-07-30 at 09:14.","The student, Siavash Khodakarami, submitted this Dissertation for approval on 2024-07-30 at 09:30.","This Dissertation was approved for publication on 2024-08-01 at 10:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21174 on 2025-03-28 at 14:42:24","Reliable and cost-effective measurement and characterization of phase change processes have always been challenging and expensive. Likewise, due to the complex nature of these processes, the fundamental understanding of processes such as boiling, and condensation remains limited. Therefore, a need exists in the phase change heat and mass transfer research community to develop new techniques which can achieve both more accurate and simpler heat transfer measurements. Furthermore, a need exists to develop a better understanding of the relevant physical mechanisms governing these processes. Conventional methods for measuring and characterizing phase change heat transfer are often complex and lead to high measurement uncertainty, and their use is limited to narrow conditions. However, in the past decade, the field of engineering has seen a surge in the application of machine learning and computer vision techniques in various areas such as material science, biomedical, manufacturing, and autonomous driving. Recently, these techniques have shown promising results in the field of thermofluidic sciences. This study aims to propose new methodologies based on imaging data and deep learning to predict heat transfer related parameters in vapor-to-liquid phase change processes. This study is intended to provide a strong argument for the need for new characterization techniques in phase change processes and why machine learning has the potential to augment or replace other methods. The new condensation characterization frameworks developed in this work allow for a more reliable, easier, cost-effective, and more generalizable condensation heat transfer measurement techniques and also opens new capabilities for studying the fundamentals of condensation processes including droplet shedding dynamics, droplet shedding frequency, droplet residence time, and transient temperature distribution during condensation on hydrophilic, hydrophobic, and superhydrophobic surfaces. This work provides an insight into the application of imaging data including high-speed imaging, regular optical imaging, and infrared imaging data along with deep learning based computer vision models to develop new predictive and generative models in the domain of phase change heat transfer. Specifically, three different characterization frameworks based on deep learning were developed. The first framework predicts condensation heat flux based on optical imaging data at low frequency of 30 frames per second. Application of this model for local heat flux measurement is demonstrated. The second framework consists of a self-supervised model for detection of shedding droplets during condensation without any manual data annotation step based on high-speed imaging data and a supervised data-driven model to predict shedding droplet dynamics based on tube size, droplet contact angle, and condensation heat flux. The third framework includes high-speed infrared thermography of tube surfaces during dropwise condensation to study temperature distribution at high spatial and temporal resolutions. The infrared imaging data are then used to develop a physics-informed generative model based on conditional generative adversarial network to map 2D grayscale images into temperature maps during condensation. The methodologies and results of this work may be used for better understanding of complex phase change processes as well as developing more accurate design criteria for more durable and efficient engineered surfaces for enhanced vapor-to-liquid phase change heat and mass transfer."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An intelligent strategy for phase change heat and mass transfer: Application of machine learning"]}]}],"canonical_facts":{"dc:contributor":["Miljkovic, Nenad","Jacobi, Anthony","Feng, Jie","Stillwell, Andrew"],"dc:creator":["Khodakarami, Siavash"],"dc:date":["2024-08-01","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Siavash Khodakarami, accepted the attached license on 2024-07-30 at 09:14.","The student, Siavash Khodakarami, submitted this Dissertation for approval on 2024-07-30 at 09:30.","This Dissertation was approved for publication on 2024-08-01 at 10:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21174 on 2025-03-28 at 14:42:24","Reliable and cost-effective measurement and characterization of phase change processes have always been challenging and expensive. Likewise, due to the complex nature of these processes, the fundamental understanding of processes such as boiling, and condensation remains limited. Therefore, a need exists in the phase change heat and mass transfer research community to develop new techniques which can achieve both more accurate and simpler heat transfer measurements. Furthermore, a need exists to develop a better understanding of the relevant physical mechanisms governing these processes. Conventional methods for measuring and characterizing phase change heat transfer are often complex and lead to high measurement uncertainty, and their use is limited to narrow conditions. However, in the past decade, the field of engineering has seen a surge in the application of machine learning and computer vision techniques in various areas such as material science, biomedical, manufacturing, and autonomous driving. Recently, these techniques have shown promising results in the field of thermofluidic sciences. This study aims to propose new methodologies based on imaging data and deep learning to predict heat transfer related parameters in vapor-to-liquid phase change processes. This study is intended to provide a strong argument for the need for new characterization techniques in phase change processes and why machine learning has the potential to augment or replace other methods. The new condensation characterization frameworks developed in this work allow for a more reliable, easier, cost-effective, and more generalizable condensation heat transfer measurement techniques and also opens new capabilities for studying the fundamentals of condensation processes including droplet shedding dynamics, droplet shedding frequency, droplet residence time, and transient temperature distribution during condensation on hydrophilic, hydrophobic, and superhydrophobic surfaces. This work provides an insight into the application of imaging data including high-speed imaging, regular optical imaging, and infrared imaging data along with deep learning based computer vision models to develop new predictive and generative models in the domain of phase change heat transfer. Specifically, three different characterization frameworks based on deep learning were developed. The first framework predicts condensation heat flux based on optical imaging data at low frequency of 30 frames per second. Application of this model for local heat flux measurement is demonstrated. The second framework consists of a self-supervised model for detection of shedding droplets during condensation without any manual data annotation step based on high-speed imaging data and a supervised data-driven model to predict shedding droplet dynamics based on tube size, droplet contact angle, and condensation heat flux. The third framework includes high-speed infrared thermography of tube surfaces during dropwise condensation to study temperature distribution at high spatial and temporal resolutions. The infrared imaging data are then used to develop a physics-informed generative model based on conditional generative adversarial network to map 2D grayscale images into temperature maps during condensation. The methodologies and results of this work may be used for better understanding of complex phase change processes as well as developing more accurate design criteria for more durable and efficient engineered surfaces for enhanced vapor-to-liquid phase change heat and mass transfer."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127310"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Siavash Khodakarami"],"dc:subject":["Condensation","Machine Learning, Infrared","Thermography","Generative Adversarial Network"],"dc:title":["An intelligent strategy for phase change heat and mass transfer: Application of machine learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}