Numerical Simulation and Hydrodynamic Performance Predicting of 2 Two-Dimensional Hydrofoils in Tandem Configuration Academic Article uri icon


  • In this study we investigated the performance of NACA 0012 hydrofoils aligned in tandem using parametric method and Neural Networks. We use the 2D viscous numerical model (STAR-CCM+) to simulate the hydrofoil system. To validate the numerical model, we modeled a single NACA 0012 configuration and compared it to experimental results. Results are found in concordance with the published experimental results. Then two NACA 0012 hydrofoils in tandem configuration were studied in relation to 788 combinations of the following parameters: spacing between two hydrofoils, angle of attack (AOA) of upstream hydrofoil and AOA of downstream hydrofoil. The effects exerted by these three parameters on the hydrodynamic coefficients Lift coefficient (CL), Drag Coefficient (CD) and Lift-Drag Ratio (LDR), are consistent with the behavior of the system. To establish a control system for the hydrofoil craft, a timely analysis of the hydrodynamic system is needed due to the computational resource constraints, analysis of a large combination and time consuming of the three parameters established. To provide a broader and faster way to predict the hydrodynamic performance of two hydrofoils in tandem configuration, an optimal artificial neural network (ANN) was trained using the large combination of three parameters generated from the numerical simulations. Regression analysis of the output of ANN was performed, and the results are consistent with numerical simulation with a correlation coefficient greater than 99.99%. The optimized spacing of 6.6c are suggested where the system has the lowest CD while obtaining the highest CL and LDR. The formula of the ANN was then presented, providing a reliable predicting method of hydrofoils in tandem configuration.

published proceedings


author list (cited authors)

  • Shang, Y., & Horrillo, J. J.

citation count

  • 0

complete list of authors

  • Shang, Yuchen||Horrillo, Juan J

publication date

  • January 2021