[HTML][HTML] Combination of polynomial chaos and Kriging for reduced-order model of reacting flow applications
Abstract The combination of Proper Orthogonal Decomposition (POD) with Kriging has been
shown to be a reliable choice for the development of Reduced-Order Models (ROMs) for the
prediction of combustion data at unexplored operating conditions. In this study, POD is
combined with Polynomial Chaos Expansion (PCE), with a combination of PCE and Kriging
(PC-Kriging) and with Artificial Neural Networks (ANN) for the development of a ROM that
can predict 2D combustion data for unexplored operating conditions. The choice of Non …
shown to be a reliable choice for the development of Reduced-Order Models (ROMs) for the
prediction of combustion data at unexplored operating conditions. In this study, POD is
combined with Polynomial Chaos Expansion (PCE), with a combination of PCE and Kriging
(PC-Kriging) and with Artificial Neural Networks (ANN) for the development of a ROM that
can predict 2D combustion data for unexplored operating conditions. The choice of Non …
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