TY - GEN
T1 - Large eddy simulation with flamelet progress variable approach via neural network acceleration
AU - Angelilli, Lorenzo
AU - Ciottoli, Pietro Paolo
AU - Galassi, Riccardo Malpica
AU - Pérez, Francisco E.Hernández
AU - Soldan, Mattia
AU - Lu, Zhen
AU - Valorani, Mauro
AU - Im, Hong G.
N1 - Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85100294112&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85100294112
SN - 9781624106095
T3 - AIAA Scitech 2021 Forum
SP - 1
EP - 10
BT - AIAA Scitech 2021 Forum
PB - American Institute of Aeronautics and Astronautics Inc. (AIAA)
T2 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
Y2 - 11 January 2021 through 15 January 2021
ER -