TY - GEN
T1 - Autonomic closure for turbulent flows using approximate bayesian computation
AU - Doronina, Olga A.
AU - Christopher, Jason D.
AU - Towery, Colin A.Z.
AU - Hamlington, Peter E.
AU - Dahm, Werner J.A.
N1 - Publisher Copyright:
© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Autonomic closure is a new technique for achieving physically accurate adaptive closure of coarse-grained turbulent flow governing equations, such as those solved in large eddy simulations (LES). Although autonomic closure has been shown in recent a priori tests to more accurately represent unclosed terms than do dynamic versions of traditional LES models, the optimization step used in the approach introduces large matrices that must be inverted, resulting in high memory usage. In order to reduce memory requirements, here we propose the use of approximate Bayesian computation (ABC) in place of the optimization step, thereby yielding an autonomic closure implementation that trades memory-intensive for processor-intensive computations. These computations can be handled by co-processors such as general purpose graphical processing units that are becoming increasingly available on petascale supercomputers. In this paper, we outline the formulation of ABC-enabled autonomic closure and present initial results demonstrating the accuracy of the approach.
AB - Autonomic closure is a new technique for achieving physically accurate adaptive closure of coarse-grained turbulent flow governing equations, such as those solved in large eddy simulations (LES). Although autonomic closure has been shown in recent a priori tests to more accurately represent unclosed terms than do dynamic versions of traditional LES models, the optimization step used in the approach introduces large matrices that must be inverted, resulting in high memory usage. In order to reduce memory requirements, here we propose the use of approximate Bayesian computation (ABC) in place of the optimization step, thereby yielding an autonomic closure implementation that trades memory-intensive for processor-intensive computations. These computations can be handled by co-processors such as general purpose graphical processing units that are becoming increasingly available on petascale supercomputers. In this paper, we outline the formulation of ABC-enabled autonomic closure and present initial results demonstrating the accuracy of the approach.
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U2 - 10.2514/6.2018-0594
DO - 10.2514/6.2018-0594
M3 - Conference contribution
AN - SCOPUS:85141628068
SN - 9781624105241
T3 - AIAA Aerospace Sciences Meeting, 2018
BT - AIAA Aerospace Sciences Meeting
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Aerospace Sciences Meeting, 2018
Y2 - 8 January 2018 through 12 January 2018
ER -