Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration
Type
Conference PaperAuthors
Angelilli, Lorenzo
Ciottoli, Pietro Paolo
Malpica Galassi, Riccardo
Hernandez Perez, Francisco
Soldan, Mattia
Lu, Zhen
Valorani, Mauro
Im, Hong G.

KAUST Department
Mechanical EngineeringPhysical Science and Engineering (PSE) Division
King Abdullah University of Science and Technology
Clean Combustion Research Center
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Mechanical Engineering Program
Date
2021-01-04Permanent link to this record
http://hdl.handle.net/10754/667594
Metadata
Show full item recordAbstract
In 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.Citation
Angelilli, 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:10.2514/6.2021-0412Sponsors
The 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).ISBN
9781624106095Additional Links
https://arc.aiaa.org/doi/10.2514/6.2021-0412ae974a485f413a2113503eed53cd6c53
10.2514/6.2021-0412