Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs

Abstract

In the present study, hydrodynamic performance of 2D and 3D submerged hydrofoils in terms of various geometries were simulated by computational fluid dynamic (CFD). Then, by selecting optimal artificial neural networks (ANN) hydrodynamic performance of hydrofoils are predicted. For this purpose, a finite volume method based on Navier–Stokes equation solver available in OpenFOAM, open-source CFD software, was used. After mesh size analyzing, to verify computational procedure, numerical results were compared with experimental ones which appropriate accuracy was observed. In this simulation, environmental and geometrical conditions such as, angle of attack, Reynolds number (Re), aspect ratio (AR) and taper ratio (TR) of hydrofoils are relevant on performance criteria of lift to drag ratio (LDR). To select a proper feed-forward ANNs to predict the performance of 2D and 3D hydrofoils under considered conditions based on the iterative algorithm, ANN architecture analysis was conducted. According to CFD results, larger value of AR and lower TR lead to greater LDR for 3D hydrofoils. Meanwhile, ANNs output showed that the maximum mean square error in predicting the LDR of 2D and 3D submerged hydrofoils are 0.0043 and 0.0035, respectively. In addition, based on the ANN weights and bias, two set of equations for predicting LDR of considered 2D and 3D submerged hydrofoils were proposed.