2026-09-19T12:26:15Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/132783 2026-03-24 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

application/pdf en text 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 Acceleration of combustion computation fluid dynamics simulations through machine learning Jo, Jun Hyoung 2025-12-03 Lee, Tonghun Deep Operator Networks Scientific machine learning Hydrogen combustion Combustion CFD Reacting flow CFD eng 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. 2025-12 Thesis https://hdl.handle.net/2142/132783 Copyright 2025 Jun Jo Mechanical Sci & Engineering Mechanical Engineering University of Illinois Urbana-Champaign M.S. Thesis