Data-driven Learning of LPV Surrogate Models of Fuel Sloshing

Abstract

This paper aims to enhance the efficiency of validation and verification campaigns involving fuel sloshing phenomena. Our first contribution is the development of an open-source, high-fidelity and computationally efficient two-dimensional smoothed-particle hydrodynamics-based fuel sloshing simulator that reproduces the dynamics of a spacecraft with a partially filled tank with liquid propellant. Implemented in Python using Jax, the simulator leverages GPU parallelization and supports automatic differentiation, enabling rapid generation of simulation data and system linearizations for general surrogate modelling purposes. Our second contribution is the demonstration of a practical methodology for constructing surrogate models of fuel sloshing from input--output data generated by the simulator, targeting rapid simulation and model-based control applications. The surrogate model employs a Linear Parameter-Varying (LPV) state-space structure with affine dependence on the scheduling variables, providing an accurate yet computationally efficient approximation of the sloshing dynamics. The capabilities of the proposed approach are demonstrated through closed-loop simulations of a rigid spacecraft with a partially filled fuel tank for two manoeuvre profiles under zero-gravity conditions. The identified surrogate enables simulations that are two orders of magnitude faster than the high-fidelity model.