Abstract
A framework consist of automated workflows was developed to reduce the load of iterative computational fluid dynamics analysis. A proper orthogonal decomposition program was included to construct a reduced order model for the system by decomposing the flow field in the snapshot data into a basis vector. A data mining program was also included to construct an surrogate model based on artificial neural network for input parameters. The developed framework was validated through flow analysis for NACA 2412 airfoil, and it was confirmed that the flow field could be approximated with an error within 4.98% compared to the actual computational fluid dynamics analysis. We also confirmed that the framework can be applied to database generation and optimization, performing them in a significantly reduced time compared to conventional computational fluid dynamics techniques.
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