Fast-running CFD calculations of the ONR Tumblehome performing maneuvers in calm water and in waves using a Gaussian process regression propeller and rudder model

Abstract

The accurate and efficient prediction of vessel maneuvering characteristics in calm water and in waves using numerical methods is challenging. The objective of this work is to demonstrate the use of a Gaussian Process Regression based data-driven propeller and rudder model for vessel maneuvering in both calm water and in waves. The data-driven propeller and rudder model is trained with CFD simulations of the discretized propeller and rudder operating in the behind condition at different forward speeds, drift angles, and rudder angles. The data-driven propeller and rudder model enables the accurate calculation of maneuvering performance with low computational expense. This work describes the process for training and implementing the data-driven propeller and rudder model in a CFD package. The calm water zigzag and turning circle maneuvering characteristics for the ONR Tumblehome are evaluated and are compared to archival experiments. Furthermore, the maneuvering characteristics are evaluated in regular waves with several different wave conditions. In addition to demonstrating the accurate and efficient calculation of maneuvering performance, this work provides a multi-fidelity database of propeller and rudder forces at different forward speeds and drift angles, which can be used to train future data-driven propeller and rudder models.