Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration
Ciottoli, Pietro Paolo
Malpica Galassi, Riccardo
Hernandez Perez, Francisco
Im, Hong G.
KAUST DepartmentMechanical Engineering
Physical Science and Engineering (PSE) Division
King Abdullah University of Science and Technology
Clean Combustion Research Center
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Mechanical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/667594
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AbstractIn the context of large eddy simulation of turbulent reacting flows, flamelet-based models are key to affordable simulations of large and complex systems. However, as the complexity of the problem increases, higher-dimensional look-up tables are required, rendering the conventional look-up procedure too demanding. This work focuses on accelerating the estimation of flamelet- based data for the flamelet/progress variable model via an artificial neural network. The neural network hyper-parameters are defined by a Bayesian optimization and two different architectures are selected for comparison against the classical look-up procedure on the well known Sandia flame D. The performance in terms of execution time and accuracy are analyzed, showing that the neural network model reduces the computational time by 30%, as compared to the traditional table look-up, while retaining comparable accuracy.
CitationAngelilli, L., Ciottoli, P. P., Malpica Galassi, R., Hernandez Perez, F. E., Soldan, M., Lu, Z., … Im, H. G. (2021). Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration. AIAA Scitech 2021 Forum. doi:10.2514/6.2021-0412
SponsorsThe authors acknowledge the support of King Abdullah University of Science and Technology (KAUST). Computational resources were provided by the KAUST Supercomputing Laboratory (KSL). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 682383).