@article{kumar_echekki_2024, title={Combustion chemistry acceleration with DeepONets}, volume={365}, ISSN={["1873-7153"]}, url={https://doi.org/10.1016/j.fuel.2024.131212}, DOI={10.1016/j.fuel.2024.131212}, abstractNote={A combustion chemistry acceleration scheme for implementation in reacting flow simulations is developed based on deep operator nets (DeepONets). The scheme is based on a mapping of a subset of the thermochemical scalars’ vector between incremental and adaptive time steps. The subset corresponds to variables that are adequate to represent the evolution of the chemical system. The DeepONet-based scheme also relies on a mapping of the solution of this subset onto a corresponding solution at an adaptive time increment to overcome the restrictive requirements of integrating such solutions with stiff chemistry. Training for the DeepONet is also implemented differently from previous approaches to the solution of PDEs with DeepONets. Instead of constructing solutions from their initial to their final states using DeepONets, the training relies on prescribed short-horizon time windows for training where intermediate solutions also serve as initial states for training. An additional framework of latent-space kinetics identification with modified DeepONet is proposed, which enhances the computational efficiency and widens the applicability of the proposed scheme. The scheme is demonstrated on the “simple” chemical kinetics of hydrogen oxidation and the more complex chemical kinetics of n-dodecane high- and low-temperatures. The proposed framework accurately learns the chemical kinetics and efficiently reproduces species and temperature temporal profiles. Moreover, a very large speed-up with a good extrapolation capability is also observed with the proposed scheme.}, journal={FUEL}, author={Kumar, Anuj and Echekki, Tarek}, year={2024}, month={Jun} }
 @article{jung_kumar_echekki_chen_2024, title={On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions}, volume={260}, ISSN={["1556-2921"]}, url={https://doi.org/10.1016/j.combustflame.2023.113204}, DOI={10.1016/j.combustflame.2023.113204}, abstractNote={Principal component transport-based data-driven reduced-order models (PC-transport ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to mitigate the computational cost associated with incorporating detailed chemical kinetics. Previous studies were mainly limited to replicating relatively-simple chemistry in canonical configurations. The objective of the present study, therefore, is to further explore the accuracy of PC-transport ROM on more complex combustion phenomenon where, for example, large hydrocarbon fuel chemistry spanning a broad range of thermochemical space governs sequential multi-stage compression ignition processes. The cumulative error of PC-transport for this problem, and for others that depend upon sequential highly nonlinear physics, has to be minimal as the combustion phasing and heat release rate in internal combustion engines depends upon accurate predictions of minor ignition species whose concentrations start from ashes and grow orders of magnitude over the course of low- and high-temperature autoignition. Specifically, the PC-transport ROM is applied to predict the compression ignition characteristics of lean n-heptane/air and primary reference fuel (PRF)/air mixtures in a two-dimensional (2-D) constant volume computational domain initialized with a two-dimensional isotropic turbulence spectrum and temperature inhomogeneities. PCA is used to define the low-dimensional manifold that represents the original thermochemical state vector, and artificial neural network (ANN) models are adopted to tabulate chemical kinetics, transport, and thermodynamic properties. A series of 2-D pseudo-turbulent simulations are performed at engine pressures by varying the initial mean and r.m.s. of temperature, turbulence intensity, and the composition of fuel/air mixture. The results show that the PC-transport ROM accurately reproduces the instantaneous and statistical ignition characteristics of the fuel/air mixture, aided by pre-processing techniques including species subsetting, data clustering, and data transformation. It is found that PCs are not properly scaled with a power transformer if reactants are included in the species subset, which leads to a decrease in the accuracy of the PC-transport ROM. A separation of the reactants from the species subset ensures that the temporal evolution of the PCs starts from zero and spans orders of magnitude with time, and as such, this approach is found to effectively redistribute both PCs and their source terms with a power transformer. The computational speed-up factor of the PC-transport ROM ranges between 5.1 and 15.0 for the cases with n-heptane/air mixture and PRF/air mixture, respectively. Moreover, a potential further speed-up is anticipated through a combination of reduction in grid resolution requirements and in the stiffness of the chemical system. As an example, many of the pre-processing methods for inhomogeneous compression ignition may also apply to other complex intermittent combustion phenomena. Novelty and significance statement • The PCA-based reduced-order model (PC-transport ROM) has been applied to the multi-stage compression ignition of large hydrocarbon fuels under HCCI-relevant conditions. The present work presents a systematic procedure to accurately capture the two-stage ignition behavior of lean n-heptane/air or PRF50/air mixture. • The present work demonstrates an advantage of the PC-transport ROM in terms of computational speed-up. The computational speed-up factor for the ROM is up to 15, and moreover, a potential additional speed-up is anticipated through the reduction in the spatial and temporal resolution required. • A series of 2-D PC-transport ROMs are conducted to demonstrate the robustness of the ROM. A limitation of the ROM against different operating conditions is also discussed.}, journal={COMBUSTION AND FLAME}, author={Jung, Ki Sung and Kumar, Anuj and Echekki, Tarek and Chen, Jacqueline H.}, year={2024}, month={Feb} }
 @article{kumar_rieth_owoyele_chen_echekki_2023, title={Acceleration of turbulent combustion DNS via principal component transport}, volume={255}, ISSN={["1556-2921"]}, url={https://doi.org/10.1016/j.combustflame.2023.112903}, DOI={10.1016/j.combustflame.2023.112903}, abstractNote={We investigate the implementation of principal component (PC) transport to accelerate the direct numerical simulation (DNS) of turbulent combustion flows. The acceleration is achieved using the transport of PCs and the tabulation of the closure terms in the PC-transport equations using machine learning. Further acceleration is achieved by a treatment for bottlenecks associated with the acoustic time steps for low Mach number flows. The approach is implemented in 2D and 3D on a laboratory scale lean premixed methane-air flame stabilized on a slot burner. DNS based on the transport of thermochemical scalars (species and energy) is also carried out, first to develop a 2D DNS database for PC-transport equations’ closure terms and, second, to validate the approach against species DNS in 2D and 3D, a principal goal of the present effort. The results show that surrogate PC DNS can reproduce instantaneous profiles as well as statistics associated with turbulence, flame topology properties and measures of flame-turbulence interactions. The study also demonstrates that parametric simulations with surrogate PC DNS can be implemented at a fraction of the cost of a full 3D DNS with species and energy transport.}, journal={COMBUSTION AND FLAME}, author={Kumar, Anuj and Rieth, Martin and Owoyele, Opeoluwa and Chen, Jacqueline H. and Echekki, Tarek}, year={2023}, month={Sep} }