{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132783"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132783","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Acceleration of combustion computation fluid dynamics simulations through machine learning","abstract":"This work demonstrated the acceleration of combustion computational fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly replacing the ODE solver for chemical reactions with cost-effective machine learning inference. Moreover, the proposed framework offered a practical and scalable method for combustion CFD simulations. The trained DeepONet models are integrated into an open-source CFD framework, OpenFOAM, using LibTorch, replacing the conventional chemistry and heat-release subroutines with inference results from DeepONets. Validation through quasi-zero-dimensional and one-dimensional hydrogen combustion cases shows that the proposed framework accurately predicts chemical species and temperature evolution computed by the baseline numerical solver, while delivering significant computational accelerations. Moreover, to enable efficient training of the machine learning models, a physics-informed approach is investigated using the UCSD hydrogen chemistry. Furthermore, a tailored DeepONet architecture is developed to accommodate various thermochemical variables as well as increase scalability for larger fuel mecahnisms. The model incorporates physics-informed loss functions derived from the governing chemical kinetics equations, allowing the models to learn the chemical kinetics with higher accuracy from fewer datasets compared to purely data-driven approaches. Furthermore, a sequential optimization procedure and specialized normalization techniques are introduced to handle the stiffness and wide dynamic range inherent in combustion chemistry. Overall, this study demonstrated that operator learning can provide an efficient, accurate, and CFD compatible surrogate for detailed chemical kinetics. Real CFD demonstrations were included to compare the accuracy and acceleration of the machine learning integrated CFD framework.","abstract_html":"This work demonstrated the acceleration of combustion computational fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly replacing the ODE solver for chemical reactions with cost-effective machine learning inference. Moreover, the proposed framework offered a practical and scalable method for combustion CFD simulations. The trained DeepONet models are integrated into an open-source CFD framework, OpenFOAM, using LibTorch, replacing the conventional chemistry and heat-release subroutines with inference results from DeepONets. Validation through quasi-zero-dimensional and one-dimensional hydrogen combustion cases shows that the proposed framework accurately predicts chemical species and temperature evolution computed by the baseline numerical solver, while delivering significant computational accelerations. Moreover, to enable efficient training of the machine learning models, a physics-informed approach is investigated using the UCSD hydrogen chemistry. Furthermore, a tailored DeepONet architecture is developed to accommodate various thermochemical variables as well as increase scalability for larger fuel mecahnisms. The model incorporates physics-informed loss functions derived from the governing chemical kinetics equations, allowing the models to learn the chemical kinetics with higher accuracy from fewer datasets compared to purely data-driven approaches. Furthermore, a sequential optimization procedure and specialized normalization techniques are introduced to handle the stiffness and wide dynamic range inherent in combustion chemistry. Overall, this study demonstrated that operator learning can provide an efficient, accurate, and CFD compatible surrogate for detailed chemical kinetics. Real CFD demonstrations were included to compare the accuracy and acceleration of the machine learning integrated CFD framework.","abstract_has_math":false,"creators":["Jo, Jun Hyoung"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Lee, Tonghun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Deep Operator Networks","Scientific machine learning","Hydrogen combustion","Combustion CFD","Reacting flow CFD"],"languages":["en"],"rights":["Copyright 2025 Jun Jo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132783","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lee, Tonghun"]},{"key":"dc:creator","label":"Author","values":["Jo, Jun Hyoung"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-03"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Deep Operator Networks","Scientific machine learning","Hydrogen combustion","Combustion CFD","Reacting flow CFD"]}]},{"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 Jun Jo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132783"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This work demonstrated the acceleration of combustion computational fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly replacing the ODE solver for chemical reactions with cost-effective machine learning inference. Moreover, the proposed framework offered a practical and scalable method for combustion CFD simulations. The trained DeepONet models are integrated into an open-source CFD framework, OpenFOAM, using LibTorch, replacing the conventional chemistry and heat-release subroutines with inference results from DeepONets. Validation through quasi-zero-dimensional and one-dimensional hydrogen combustion cases shows that the proposed framework accurately predicts chemical species and temperature evolution computed by the baseline numerical solver, while delivering significant computational accelerations. Moreover, to enable efficient training of the machine learning models, a physics-informed approach is investigated using the UCSD hydrogen chemistry. Furthermore, a tailored DeepONet architecture is developed to accommodate various thermochemical variables as well as increase scalability for larger fuel mecahnisms. The model incorporates physics-informed loss functions derived from the governing chemical kinetics equations, allowing the models to learn the chemical kinetics with higher accuracy from fewer datasets compared to purely data-driven approaches. Furthermore, a sequential optimization procedure and specialized normalization techniques are introduced to handle the stiffness and wide dynamic range inherent in combustion chemistry. Overall, this study demonstrated that operator learning can provide an efficient, accurate, and CFD compatible surrogate for detailed chemical kinetics. Real CFD demonstrations were included to compare the accuracy and acceleration of the machine learning integrated CFD framework.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Jun Jo, accepted the attached license on 2025-12-03 at 10:12.","The student, Jun Jo, submitted this Thesis for approval on 2025-12-03 at 10:15.","This Thesis was approved for publication on 2025-12-03 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22967 on 2026-02-19 at 20:09:45"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Acceleration of combustion computation fluid dynamics simulations through machine learning"]}]}],"canonical_facts":{"dc:contributor":["Lee, Tonghun"],"dc:creator":["Jo, Jun Hyoung"],"dc:date":["2025-12","2025-12-03"],"dc:description":["This work demonstrated the acceleration of combustion computational fluid dynamics simulations through Deep Operator Networks (DeepONets), achieving up to an 18 times speedup in the chemical source term evaluation. Another numerical experiment showed an overall reduction of approximately 30% in computation time. This was achieved by directly replacing the ODE solver for chemical reactions with cost-effective machine learning inference. Moreover, the proposed framework offered a practical and scalable method for combustion CFD simulations. The trained DeepONet models are integrated into an open-source CFD framework, OpenFOAM, using LibTorch, replacing the conventional chemistry and heat-release subroutines with inference results from DeepONets. Validation through quasi-zero-dimensional and one-dimensional hydrogen combustion cases shows that the proposed framework accurately predicts chemical species and temperature evolution computed by the baseline numerical solver, while delivering significant computational accelerations. Moreover, to enable efficient training of the machine learning models, a physics-informed approach is investigated using the UCSD hydrogen chemistry. Furthermore, a tailored DeepONet architecture is developed to accommodate various thermochemical variables as well as increase scalability for larger fuel mecahnisms. The model incorporates physics-informed loss functions derived from the governing chemical kinetics equations, allowing the models to learn the chemical kinetics with higher accuracy from fewer datasets compared to purely data-driven approaches. Furthermore, a sequential optimization procedure and specialized normalization techniques are introduced to handle the stiffness and wide dynamic range inherent in combustion chemistry. Overall, this study demonstrated that operator learning can provide an efficient, accurate, and CFD compatible surrogate for detailed chemical kinetics. Real CFD demonstrations were included to compare the accuracy and acceleration of the machine learning integrated CFD framework.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Jun Jo, accepted the attached license on 2025-12-03 at 10:12.","The student, Jun Jo, submitted this Thesis for approval on 2025-12-03 at 10:15.","This Thesis was approved for publication on 2025-12-03 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22967 on 2026-02-19 at 20:09:45"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132783"],"dc:language":["en"],"dc:rights":["Copyright 2025 Jun Jo"],"dc:subject":["Deep Operator Networks","Scientific machine learning","Hydrogen combustion","Combustion CFD","Reacting flow CFD"],"dc:title":["Acceleration of combustion computation fluid dynamics simulations through machine learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}