TY - GEN
T1 - Data-driven dimension reduction in turbulent combustion
T2 - AIAA Scitech Forum, 2019
AU - Nunno, A. Cody
AU - Perry, Bruce A.
AU - Macart, Jonathan F.
AU - Mueller, Michael E.
N1 - Funding Information:
The authors gratefully acknowledge valuable support in the form of computational time on the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Princeton University Office of Information Technology’s Research Computing department. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. B.A.P. is thankful for support from the National Science Foundation Graduate Research Fellowship Program, Award DGE-1148900.
Publisher Copyright:
© 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Turbulent combustion involves many interdependent variables evolving over a wide range of length and time scales. Due to the tremendous computational cost required to solve equations for all of these variables over all relevant length scales, reduced-order models are required for predictive simulations of engineering systems. Common approaches to reduced-order combustion modeling include physically-informed methods of dimension reduction, such as Flamelet Generated Manifolds (FGM) and the Flamelet/Progress Variable model (FPV), and, more recently, data-driven dimension reduction, such as Principal Component Analysis (PCA). While the accuracy of both approaches has been previously assessed, they have not been directly compared or used in conjunction with one another. To this end, this work examines both how insights from physically-derived reduced-order manifolds (PDROM) can be used to benchmark data-driven approaches and how the data-driven approaches can be used to validate the assumptions of PDROM models, using data from Direct Numerical Simulations (DNS) of premixed and nonpremixed turbulent flames and from one-dimensional flame calculations. PCA assumes a linear structure and therefore cannot achieve as large of a dimension reduction as PDROM while maintaining accuracy. While nonlinear dimension-reduction methods perform similarly to PDROM, these methods suffer from a lack of physical interpretability that would make implementation impractical. However, the data-driven approaches are successfully used to assess the critical assumptions of the physical models, with the similarity of principal components between different data sets validating structural similarity between the DNS and one-dimensional flame data.
AB - Turbulent combustion involves many interdependent variables evolving over a wide range of length and time scales. Due to the tremendous computational cost required to solve equations for all of these variables over all relevant length scales, reduced-order models are required for predictive simulations of engineering systems. Common approaches to reduced-order combustion modeling include physically-informed methods of dimension reduction, such as Flamelet Generated Manifolds (FGM) and the Flamelet/Progress Variable model (FPV), and, more recently, data-driven dimension reduction, such as Principal Component Analysis (PCA). While the accuracy of both approaches has been previously assessed, they have not been directly compared or used in conjunction with one another. To this end, this work examines both how insights from physically-derived reduced-order manifolds (PDROM) can be used to benchmark data-driven approaches and how the data-driven approaches can be used to validate the assumptions of PDROM models, using data from Direct Numerical Simulations (DNS) of premixed and nonpremixed turbulent flames and from one-dimensional flame calculations. PCA assumes a linear structure and therefore cannot achieve as large of a dimension reduction as PDROM while maintaining accuracy. While nonlinear dimension-reduction methods perform similarly to PDROM, these methods suffer from a lack of physical interpretability that would make implementation impractical. However, the data-driven approaches are successfully used to assess the critical assumptions of the physical models, with the similarity of principal components between different data sets validating structural similarity between the DNS and one-dimensional flame data.
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U2 - 10.2514/6.2019-2010
DO - 10.2514/6.2019-2010
M3 - Conference contribution
AN - SCOPUS:85083944301
SN - 9781624105784
T3 - AIAA Scitech 2019 Forum
BT - AIAA Scitech 2019 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
Y2 - 7 January 2019 through 11 January 2019
ER -