Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels
Predictive computational fluid dynamics (CFD) simulations of reacting flows in energy conversion systems are accompanied by a major computational bottleneck of solving a stiff system of coupled ordinary differential equations (ODEs) associated with detailed fuel chemistry. This issue is exacerbated...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Thermal Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fther.2025.1594443/full |
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| author | Tadbhagya Kumar Anuj Kumar Anuj Kumar Pinaki Pal |
| author_facet | Tadbhagya Kumar Anuj Kumar Anuj Kumar Pinaki Pal |
| author_sort | Tadbhagya Kumar |
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| description | Predictive computational fluid dynamics (CFD) simulations of reacting flows in energy conversion systems are accompanied by a major computational bottleneck of solving a stiff system of coupled ordinary differential equations (ODEs) associated with detailed fuel chemistry. This issue is exacerbated with the complexity of fuel chemistry as the number of reactive scalars and chemical reactions increase. In this work, a physics-constrained Autoencoder (AE)-NeuralODE framework, termed as PhyChemNODE, is developed for data-driven modeling and temporal emulation of stiff chemical kinetics for complex hydrocarbon fuels, wherein a non-linear AE is employed for dimensionality reduction of the thermochemical state and the NODE learns temporal dynamics of the system in the low-dimensional latent space obtained from the AE. Both the AE and NODE are trained together in an end-to-end manner. We further enhance the approach by incorporating elemental mass conservation constraints directly into the loss function during model training. This ensures that total mass as well as individual elemental species masses are conserved in an a-posteriori manner. Demonstration studies are performed for methane combustion kinetics (32 species, 266 chemical reactions) over a wide thermodynamic and composition space at high pressure. Effects of various model hyperparameters, such as relative weighting of different terms in the loss function and dimensionality of the AE latent space, on the accuracy of Phy-ChemNODE are assessed. The physics-based constraints are shown to improve both training efficiency and physical consistency of the data-driven model. Further, a-posteriori autoregressive inference tests demonstrate that Phy-ChemNODE leads to reduced temporal stiffness in the latent space, and achieves 1-3 orders of magnitude speedup relative to the detailed kinetic mechanism depending on the type of ODE solver (implicit or explicit) used for numerical integration, while ensuring prediction fidelity. |
| format | Article |
| id | doaj-art-ec3885b071f4440b970a71da2e5fca97 |
| institution | Kabale University |
| issn | 2813-0456 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Thermal Engineering |
| spelling | doaj-art-ec3885b071f4440b970a71da2e5fca972025-08-20T04:01:03ZengFrontiers Media S.A.Frontiers in Thermal Engineering2813-04562025-08-01510.3389/fther.2025.15944431594443Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuelsTadbhagya Kumar0Anuj Kumar1Anuj Kumar2Pinaki Pal3Transportation and Power Systems Division, Argonne National Laboratory (DOE), Lemont, IL, United StatesTransportation and Power Systems Division, Argonne National Laboratory (DOE), Lemont, IL, United StatesDepartment of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, United StatesTransportation and Power Systems Division, Argonne National Laboratory (DOE), Lemont, IL, United StatesPredictive computational fluid dynamics (CFD) simulations of reacting flows in energy conversion systems are accompanied by a major computational bottleneck of solving a stiff system of coupled ordinary differential equations (ODEs) associated with detailed fuel chemistry. This issue is exacerbated with the complexity of fuel chemistry as the number of reactive scalars and chemical reactions increase. In this work, a physics-constrained Autoencoder (AE)-NeuralODE framework, termed as PhyChemNODE, is developed for data-driven modeling and temporal emulation of stiff chemical kinetics for complex hydrocarbon fuels, wherein a non-linear AE is employed for dimensionality reduction of the thermochemical state and the NODE learns temporal dynamics of the system in the low-dimensional latent space obtained from the AE. Both the AE and NODE are trained together in an end-to-end manner. We further enhance the approach by incorporating elemental mass conservation constraints directly into the loss function during model training. This ensures that total mass as well as individual elemental species masses are conserved in an a-posteriori manner. Demonstration studies are performed for methane combustion kinetics (32 species, 266 chemical reactions) over a wide thermodynamic and composition space at high pressure. Effects of various model hyperparameters, such as relative weighting of different terms in the loss function and dimensionality of the AE latent space, on the accuracy of Phy-ChemNODE are assessed. The physics-based constraints are shown to improve both training efficiency and physical consistency of the data-driven model. Further, a-posteriori autoregressive inference tests demonstrate that Phy-ChemNODE leads to reduced temporal stiffness in the latent space, and achieves 1-3 orders of magnitude speedup relative to the detailed kinetic mechanism depending on the type of ODE solver (implicit or explicit) used for numerical integration, while ensuring prediction fidelity.https://www.frontiersin.org/articles/10.3389/fther.2025.1594443/fullstiff chemical kineticsfuel combustionautoencodersneural ordinary differential equationsphysics-informed neural networkchemistry acceleration |
| spellingShingle | Tadbhagya Kumar Anuj Kumar Anuj Kumar Pinaki Pal Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels Frontiers in Thermal Engineering stiff chemical kinetics fuel combustion autoencoders neural ordinary differential equations physics-informed neural network chemistry acceleration |
| title | Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels |
| title_full | Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels |
| title_fullStr | Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels |
| title_full_unstemmed | Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels |
| title_short | Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels |
| title_sort | phy chemnode an end to end physics constrained autoencoder neuralode framework for learning stiff chemical kinetics of hydrocarbon fuels |
| topic | stiff chemical kinetics fuel combustion autoencoders neural ordinary differential equations physics-informed neural network chemistry acceleration |
| url | https://www.frontiersin.org/articles/10.3389/fther.2025.1594443/full |
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