Data-Driven Machine Learning Model for Aircraft Icing Severity Evaluation

No AccessTechnical NotesData-Driven Machine Learning Model for Aircraft Icing Severity EvaluationSibo Li, Jingkun Qin and Roberto PaoliSibo LiUniversity of Illinois at Chicago, Chicago, Illinois 60607*Ph.D. Candidate, Department of Mechanical and Industrial Engineering.Search for more papers by this author, Jingkun QinChina Literature Limited, 100105 Beijing, People’s Republic of China†Data Scientist.Search for more papers by this author and Roberto Paoli https://orcid.org/0000-0003-2158-8870University of Illinois at Chicago, Chicago, Illinois 60607‡Research Assistant Professor, Department of Mechanical and Industrial Engineering; also Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439. Senior Member AIAA.Search for more papers by this authorPublished Online:14 Jul 2021https://doi.org/10.2514/1.I010978SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Mclean J., “Determining the Effects of Weather in Aircraft Accident Investigations,” 24th Aerospace Sciences Meeting, AIAA Paper 1986-0323, 1986. https://doi.org/10.2514/6.1986-323 LinkGoogle Scholar[2] Bourgault Y., Boutanios Z. and Habashi W. 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All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 2327-3097 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. AcknowledgmentsThis work was supported by Argonne National Laboratory through grant number ANL 4J-30361-0030A, titled “Multiscale Modeling of Complex Flows,” and by National Science Foundation through grant number 1854815, titled “High-Performance Computing and Data-Driven Modeling of Aircraft Contrails,” awarded to R. Paoli.PDF Received12 February 2021Accepted3 June 2021Published online14 July 2021