Dimensionality reduction and unsupervised classification for high-fidelity reacting flow simulations
AuthorsMalik, Mohammad Rafi
Hernandez Perez, Francisco
Im, Hong G.
KAUST DepartmentClean Combustion Research Center
Computational Reacting Flow Laboratory (CRFL)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Embargo End Date2024-08-10
Permanent link to this recordhttp://hdl.handle.net/10754/680308
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AbstractThe development of reduced-order combustion models able to accurately reproduce the physics of reactive systems has been a highly challenging aspect of numerical combustion research in the recent past. The complexity of the problem can be reduced by identifying and using low-dimensional manifolds able to account for turbulence-chemistry interactions. Recently, Principal Components Analysis (PCA) has shown its potential in reducing the dimensionality of a chemically reactive system while minimizing the reconstruction error. The present work demonstrates the application of the Manifold Generated by Local PCA (MG-L-PCA) approach in direct numerical simulation (DNS) of turbulent flames. The approach is enhanced with an unsupervised clustering based on Vector Quantization PCA (VQPCA) and an on-the-flyPCA-based classification technique. The reduced model is then applied on a three-dimensional (3D) turbulent premixed NH3/air flame by transporting only a subset of the original state-space variables on the computational grid and using the PCA basis to reconstruct the non-transported variables. Results are compared with both a detailed reaction mechanism and a Computational Singular Perturbation (CSP) reduced skeletal mechanism. A comparison between training the reduced model using one-dimensional (1D) and 3D data sets is also included. Overall, the MG-L-PCA allows not only for a reduction in the number of transport equations, but also a significant reduction in the stiffness of the system, while providing highly accurate results.
CitationMalik, M. R., Khamedov, R., Hernández Pérez, F. E., Coussement, A., Parente, A., & Im, H. G. (2022). Dimensionality reduction and unsupervised classification for high-fidelity reacting flow simulations. Proceedings of the Combustion Institute. https://doi.org/10.1016/j.proci.2022.06.017
SponsorsThis work was sponsored by King Abdullah University of Science and Technology (KAUST). Computational resources were provided by the KAUST Supercomputing Laboratory (KSL). The work of A. Parente has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement no. 714605.