Reduced order modeling of hydrodynamic interactions between a submarine and unmanned underwater vehicle using non-myopic multi-fidelity active learning

Abstract

Several efforts have been dedicated to developing computational tools capable of predicting the hydrodynamic forces and moments of Unmanned Underwater Vehicles (UUVs). However, there is no method at the moment that allows for real-time computational modeling of all the complex hydrodynamic interaction forces and moments that a UUV experiences when operating in close proximity to a moving submarine. Real-time modeling of these hydrodynamic interactions is essential to simulate the motion required to launch and recover UUVs from submarines. Potential flow models are often fast enough to be used in real time, but lack the accuracy of Computational Fluid Dynamics (CFD) simulations, which often take hours or days to solve. Here, we formulate the problem in the context of machine learning, specifically active learning. The goal is to develop a surrogate model capable of predicting the UUV and submarine hydrodynamic interactions in real time using a very small number of carefully selected CFD simulations. We introduce a new active learning framework called Non-Myopic Multi-Fidelity Active Learning for Gaussian Process (GP) regression that accelerates the convergence of the surrogate model by utilizing the low cost of the low fidelity simulations to explore the domain, as well as optimally selected high fidelity simulations to improve the model accuracy. The resulting surrogate model can be integrated into UUV control and autonomy systems and motion simulators to further enable UUV launch and recovery from submarines. This new active learning method may also be used to create higher accuracy and lower cost surrogate models in other real world applications.