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An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure | | Rishikesh Ranade
; Genong Li
; Shaoping Li
; Tarek Echekki
; | | Date: |
18 May 2020 | | Abstract: | Probability density function (PDF) based turbulent combustion modelling is
limited by the need to store multi-dimensional PDF tables that can take up
large amounts of memory. A significant saving in storage can be achieved by
using various machine-learning techniques that represent the thermo-chemical
quantities of a PDF table using mathematical functions. These functions can be
computationally more expensive than the existing interpolation methods used for
thermo-chemical quantities. More importantly, the training time can amount to a
considerable portion of the simulation time. In this work, we address these
issues by introducing an adaptive training algorithm that relies on multi-layer
perception (MLP) neural networks for regression and self-organizing maps (SOMs)
for clustering data to tabulate using different networks. The algorithm is
designed to address both the multi-dimensionality of the PDF table as well as
the computational efficiency of the proposed algorithm. SOM clustering divides
the PDF table into several parts based on similarities in data. Each cluster of
data is trained using an MLP algorithm on simple network architectures to
generate local functions for thermo-chemical quantities. The algorithm is
validated for the so-called DLR-A turbulent jet diffusion flame using both RANS
and LES simulations and the results of the PDF tabulation are compared to the
standard linear interpolation method. The comparison yields a very good
agreement between the two tabulation techniques and establishes the MLP-SOM
approach as a viable method for PDF tabulation. | | Source: | arXiv, 2005.9747 | | Services: | Forum | Review | PDF | Favorites | |
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