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This interdisciplinary study, which combines machine learning, statisticalmethodologies, high-fidelity simulations, and flow physics, demonstrates a newprocess for building an efficient surrogate model for predictingspatiotemporally evolving flow dynamics. In our previous work, a common-gridproper-orthogonal-decomposition (CPOD) technique was developed to establish aphysics-based surrogate (emulation) model for prediction of mean flowfields anddesign exploration over a wide parameter space. The CPOD technique issubstantially improved upon here using a kernel-smoothed POD (KSPOD) technique,which leverages kriging-based weighted functions from the design matrix. Theresultant emulation model is then trained using a dataset obtained throughhigh-fidelity simulations. As an example, the flow evolution in a swirlinjector is considered for a wide range of design parameters and operatingconditions. The KSPOD-based emulation model performs well, and can faithfullycapture the spatiotemporal flow dynamics. The model enables effective designsurveys utilizing high-fidelity simulation data, achieving a turnaround timefor evaluating new design points that is 42,000 times faster than the originalsimulation.

Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics