{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132750"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132750","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient development and uncertainty propagation of aviation fuel chemical kinetic models","abstract":"This work presents a comprehensive methodology for the rapid development of reduced chemical kinetic mechanisms for jet fuels, with a focus on sustainable aviation fuels (SAFs). The framework is designed to balance model accuracy, computational efficiency, and experimental requirements for quick development times and application in complex environments like computational fluid dynamics (CFD). The method uses experimental ignition delay data from a shock tube and a rapid compression machine as primary validation targets, which are then used for data-driven optimization of chemical kinetic mechanisms. To efficiently propagate and reduce model uncertainty, a hybrid response surface network and stochastic gradient descent ensemble (HRSN-SGDE) technique is developed, enabling the rapid generation and analysis of a large distribution of optimized mechanisms. An approach is then introduced to generate a base mechanism from fuel composition data and a detailed surrogate mechanism library, which estimates averaged molecular properties and pyrolysis products for a lumped fuel decomposition mechanism structure. The methodology is demonstrated on four diverse fuels, and the uncertainty is propagated through a series of simulation outputs, including ignition delay, flame speed, and species concentrations. Finally, this uncertainty is propagated into a diffusion flame simulation to show uncertainty effects in more complex environments. The results show that while ignition delay measurements effectively constrain the models to match global performance, significant uncertainty can persist in other outputs, such as species concentration profiles, highlighting key areas for future improvements to enhance the reliability of chemical kinetic models for next-generation aviation fuels.","abstract_html":"This work presents a comprehensive methodology for the rapid development of reduced chemical kinetic mechanisms for jet fuels, with a focus on sustainable aviation fuels (SAFs). The framework is designed to balance model accuracy, computational efficiency, and experimental requirements for quick development times and application in complex environments like computational fluid dynamics (CFD). The method uses experimental ignition delay data from a shock tube and a rapid compression machine as primary validation targets, which are then used for data-driven optimization of chemical kinetic mechanisms. To efficiently propagate and reduce model uncertainty, a hybrid response surface network and stochastic gradient descent ensemble (HRSN-SGDE) technique is developed, enabling the rapid generation and analysis of a large distribution of optimized mechanisms. An approach is then introduced to generate a base mechanism from fuel composition data and a detailed surrogate mechanism library, which estimates averaged molecular properties and pyrolysis products for a lumped fuel decomposition mechanism structure. The methodology is demonstrated on four diverse fuels, and the uncertainty is propagated through a series of simulation outputs, including ignition delay, flame speed, and species concentrations. Finally, this uncertainty is propagated into a diffusion flame simulation to show uncertainty effects in more complex environments. The results show that while ignition delay measurements effectively constrain the models to match global performance, significant uncertainty can persist in other outputs, such as species concentration profiles, highlighting key areas for future improvements to enhance the reliability of chemical kinetic models for next-generation aviation fuels.","abstract_has_math":false,"creators":["Wiersema, Paxton"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Lee, Tonghun","Mayhew, Eric","Glumac, Nick","Ansell, Phillip"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["combustion, chemical kinetics, SAF"],"languages":["en"],"rights":["Copyright 2025 Paxton Wiersema"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132750","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lee, Tonghun","Mayhew, Eric","Glumac, Nick","Ansell, Phillip"]},{"key":"dc:creator","label":"Author","values":["Wiersema, Paxton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-10-29"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["combustion, chemical kinetics, SAF"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Paxton Wiersema"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132750"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This work presents a comprehensive methodology for the rapid development of reduced chemical kinetic mechanisms for jet fuels, with a focus on sustainable aviation fuels (SAFs). The framework is designed to balance model accuracy, computational efficiency, and experimental requirements for quick development times and application in complex environments like computational fluid dynamics (CFD). The method uses experimental ignition delay data from a shock tube and a rapid compression machine as primary validation targets, which are then used for data-driven optimization of chemical kinetic mechanisms. To efficiently propagate and reduce model uncertainty, a hybrid response surface network and stochastic gradient descent ensemble (HRSN-SGDE) technique is developed, enabling the rapid generation and analysis of a large distribution of optimized mechanisms. An approach is then introduced to generate a base mechanism from fuel composition data and a detailed surrogate mechanism library, which estimates averaged molecular properties and pyrolysis products for a lumped fuel decomposition mechanism structure. The methodology is demonstrated on four diverse fuels, and the uncertainty is propagated through a series of simulation outputs, including ignition delay, flame speed, and species concentrations. Finally, this uncertainty is propagated into a diffusion flame simulation to show uncertainty effects in more complex environments. The results show that while ignition delay measurements effectively constrain the models to match global performance, significant uncertainty can persist in other outputs, such as species concentration profiles, highlighting key areas for future improvements to enhance the reliability of chemical kinetic models for next-generation aviation fuels.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Paxton Wiersema, accepted the attached license on 2025-10-28 at 15:43.","The student, Paxton Wiersema, submitted this Dissertation for approval on 2025-10-28 at 15:48.","This Dissertation was approved for publication on 2025-10-29 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22838 on 2026-02-19 at 20:08:35"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Efficient development and uncertainty propagation of aviation fuel chemical kinetic models"]}]}],"canonical_facts":{"dc:contributor":["Lee, Tonghun","Mayhew, Eric","Glumac, Nick","Ansell, Phillip"],"dc:creator":["Wiersema, Paxton"],"dc:date":["2025-12","2025-10-29"],"dc:description":["This work presents a comprehensive methodology for the rapid development of reduced chemical kinetic mechanisms for jet fuels, with a focus on sustainable aviation fuels (SAFs). The framework is designed to balance model accuracy, computational efficiency, and experimental requirements for quick development times and application in complex environments like computational fluid dynamics (CFD). The method uses experimental ignition delay data from a shock tube and a rapid compression machine as primary validation targets, which are then used for data-driven optimization of chemical kinetic mechanisms. To efficiently propagate and reduce model uncertainty, a hybrid response surface network and stochastic gradient descent ensemble (HRSN-SGDE) technique is developed, enabling the rapid generation and analysis of a large distribution of optimized mechanisms. An approach is then introduced to generate a base mechanism from fuel composition data and a detailed surrogate mechanism library, which estimates averaged molecular properties and pyrolysis products for a lumped fuel decomposition mechanism structure. The methodology is demonstrated on four diverse fuels, and the uncertainty is propagated through a series of simulation outputs, including ignition delay, flame speed, and species concentrations. Finally, this uncertainty is propagated into a diffusion flame simulation to show uncertainty effects in more complex environments. The results show that while ignition delay measurements effectively constrain the models to match global performance, significant uncertainty can persist in other outputs, such as species concentration profiles, highlighting key areas for future improvements to enhance the reliability of chemical kinetic models for next-generation aviation fuels.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Paxton Wiersema, accepted the attached license on 2025-10-28 at 15:43.","The student, Paxton Wiersema, submitted this Dissertation for approval on 2025-10-28 at 15:48.","This Dissertation was approved for publication on 2025-10-29 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22838 on 2026-02-19 at 20:08:35"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132750"],"dc:language":["en"],"dc:rights":["Copyright 2025 Paxton Wiersema"],"dc:subject":["combustion, chemical kinetics, SAF"],"dc:title":["Efficient development and uncertainty propagation of aviation fuel chemical kinetic models"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}