TY - GEN
T1 - Data-driven Learning of Unsteady Flamelet Progress Variable Manifolds via Hierarchical Clustering and Grouped Multi-Target Artificial Neural Networks
AU - Kumar, Tadbhagya
AU - Almeldein, Ahmed
AU - Pal, Pinaki
AU - Kabil, Islam
N1 - Publisher Copyright:
© 2024 by UChicago Argonne, LLC, Operator of Argonne National Laboratory. Published by the American Institute of Aeronautics and Astronautics, Inc.
PY - 2024
Y1 - 2024
N2 - In this work, a data-driven framework is presented with the goal of replacing tabulation of multidimensional unsteady flamelet progress variable (UFPV) manifolds with deep learning models for computational fluid dynamics (CFD) simulations of turbulent reacting flows. Tabulated flamelet combustion models require accessing lookup tables and performing interpolation for online retrieval of quantities of interest (thermochemical scalars, PV source term, etc.), at the cost of high memory footprint. The data-driven approach is divided into two parts: a) grouping together strongly correlated chemical species using hierarchical clustering, and b) training a separate artificial neural network (ANN) to infer species mass fractions within each cluster. A separate ANN is trained for predicting the PV source term. For a priori demonstration of the proposed approach, a 4D UFPV lookup table (containing 25 species) generated from methane/air counterflow flame simulations at constant pressure is employed. Sensitivity studies are carried out with respect to the number of clusters, wherein the impact of clustering on the predictive accuracy of ANNs is evaluated against a baseline case with no clustering for species. Additionally, with regard to the prediction of species mass fractions within individual clusters, hyperparameter tuning indicates that having a separate hidden layer for each species before the output layer improves ANN accuracy.
AB - In this work, a data-driven framework is presented with the goal of replacing tabulation of multidimensional unsteady flamelet progress variable (UFPV) manifolds with deep learning models for computational fluid dynamics (CFD) simulations of turbulent reacting flows. Tabulated flamelet combustion models require accessing lookup tables and performing interpolation for online retrieval of quantities of interest (thermochemical scalars, PV source term, etc.), at the cost of high memory footprint. The data-driven approach is divided into two parts: a) grouping together strongly correlated chemical species using hierarchical clustering, and b) training a separate artificial neural network (ANN) to infer species mass fractions within each cluster. A separate ANN is trained for predicting the PV source term. For a priori demonstration of the proposed approach, a 4D UFPV lookup table (containing 25 species) generated from methane/air counterflow flame simulations at constant pressure is employed. Sensitivity studies are carried out with respect to the number of clusters, wherein the impact of clustering on the predictive accuracy of ANNs is evaluated against a baseline case with no clustering for species. Additionally, with regard to the prediction of species mass fractions within individual clusters, hyperparameter tuning indicates that having a separate hidden layer for each species before the output layer improves ANN accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85192276958&partnerID=8YFLogxK
U2 - 10.2514/6.2024-0801
DO - 10.2514/6.2024-0801
M3 - Conference contribution
AN - SCOPUS:85192276958
SN - 9781624107115
T3 - AIAA SciTech Forum and Exposition, 2024
BT - AIAA SciTech Forum and Exposition, 2024
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA SciTech Forum and Exposition, 2024
Y2 - 8 January 2024 through 12 January 2024
ER -