[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118365-en":3,"doc-seo-118365-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118365,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Exploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement - Master’s thesis","This master’s thesis applies machine learning to the Hot Disk Transient Plane Source (TPS) method to improve the precision and efficiency of thermal conductivity prediction. The work is split into two parts: Part I focuses on low-density/high-insulation materials, while Part II addresses high-temperature measurements with noise. Four prediction algorithms are evaluated for accuracy. Training relies on TPS experimental data augmented with simulated data to address limited datasets. Results assess whether machine learning can predict thermal conductivity from transient curves, noting accuracy variability when reference points are absent or data is insufficient.","Exploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement  \nMaster’s thesis in Computer science and engineering  \nBITNOORI LEE  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2023  \nMaster’s thesis 2023  \nExploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement  \nBITNOORI LEE  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2023  \nExploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement  \nBITNOORI LEE  \n© BITNOORI LEE, 2023 .  \nSupervisor: Sebastianus Cornelis Jacobus Bruinsma, Ph.D, CSE  \nAdvisor: Besira Mihiretie, Ph.D, Hot Disk AB  \nExaminer: Pedro Petersen Moura Trancoso, CSE  \nMaster’s Thesis 2023  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nExploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement  \nBITNOORI LEE  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nThis study introduces the application of machine learning to the Hot Disk Transient Plane Source (TPS) method, aimed at enhancing the precision and efficiency of thermal conductivity prediction.  \nComprising two distinct parts, Part I, the research addresses the prediction of thermal conductivity in low-density/high-insulation materials. Part II is the thermal conductivity measurement under high-temperature conditions with noise. Four prediction algorithms were systematically applied and assessed for accuracy to predict thermal conductivities. Experimental data obtained through the TPS method served as the basis for machine learning training data, augmented with simulated data to make up for insufficient data.  \nThe outcomes of this study provide a conclusive response to a critical research question: Can machine learning accurately predict thermal conductivity from transient curves? In Part I, machine learning consistently and accurately predicts thermal conductivity for low-density/high-insulation materials devoid of CL values, underscoring its complementary utility. In Part II, machine learning demonstrates its proficiency in accurately predicting thermal conductivity, even in noisy transient curves at extreme temperatures. However, challenges stemming from insufficient data issues and the absence of reference points introduce variability in accuracy.  \nKeywords: Machine Learning, Supervised Learning, Predictive Modeling, TPS methods, Thermal conductivity, FEM simulation.  \nAcknowledgments  \nI express profound gratitude to the individuals and organizations whose steadfast support and expertise were instrumental in completing this research.  \nI sincerely appreciate Hot Disk AB’s invaluable contributions, generously sharing their extensive knowledge and 25 years’worth of data, which played an integral role in shaping this study. Heartfelt thanks are extended to CEO Mattias Gustavsson and the entire Hot Disk AB team for their unwavering support and indispensable contributions throughout this endeavor.  \nI am grateful to Besira Mihiretie, our esteemed company advisor, whose profound expertise and practical insights significantly enriched this research. His unwavering commitment to the project has been truly remarkable.  \nAdditionally, I appreciate the valuable guidance the examiner, Pedro Petersen Moura Trancoso, and the supervisor, Sebastianus Cornelis Jacobus Bruinsma, provided in computer science. Their devoted time and expertise have been invaluable.  \nLast but not least, I would like to express ","cbCaim0Jyb6L5l0G","https://ap.wps.com/l/cbCaim0Jyb6L5l0G","pdf",1091527,1,44,"English","en",105,"# Abstract\n# Contents\n## Introduction\n## Theory\n## Methods\n## Results","[{\"question\":\"What research problem does this thesis address?\",\"answer\":\"The thesis investigates whether machine learning can accurately predict thermal conductivity from transient curves produced by the Hot Disk Transient Plane Source (TPS) method.\"},{\"question\":\"How is the study organized into Part I and Part II?\",\"answer\":\"Part I targets low-density/high-insulation materials, while Part II covers thermal conductivity measurement under high-temperature conditions where noise is present.\"},{\"question\":\"Why are simulated data included alongside TPS experimental data?\",\"answer\":\"Simulated data are used to compensate for insufficient experimental data, supporting model training and improving evaluation robustness.\"}]","Exploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement - 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