Kernel-smoothed proper orthogonal decomposition-based emulation for spatiotemporally evolving flow dynamics prediction

Yu Hung Chang, Liwei Zhang, Xingjian Wang, Shiang Ting Yeh, Simon Mak, Chih Li Sung, C. F. Jeff Wu, Vigor Yang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This interdisciplinary study, which combines machine learning, statistical methodologies, high-fidelity simulations, projection-based model reduction, and flow physics, demonstrates a new process for building an efficient surrogate model to predict spatiotemporally evolving flow dynamics for design survey. In our previous work, a common proper-orthogonal-decomposition (CPOD) technique was developed to establish a physics-based surrogate (emulation) model for prediction of useful flow physics and design exploration over a wide parameter space. The emulation technique is substantially improved upon here using a kernel-smoothed POD (KSPOD) technique, which leverages kriging-based weighted functions from the design matrix. The resultant emulation model is then trained using a large-scale dataset obtained through high-fidelity simulations. As an example, the flow evolution in a swirl injector is considered for a wide range of design parameters and operating conditions. The KSPOD-based emulation model performs well and can faithfully capture the spatiotemporal flow dynamics. The model enables effective design surveys using high-fidelity simulation data, achieving a turnaround time for evaluating new design points that is 42,000 times faster than the original simulation.

Original languageEnglish
Pages (from-to)5269-5280
Number of pages12
JournalAIAA journal
Issue number12
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering


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