Learning slosh dynamics by means of data

B. Moya (1) , David González (1) , Icíar Alfaro (1) , Francisco Chinesta (2) , Elías G. Cueto (1)

1 University of Zaragoza - Universidad de Zaragoza [Zaragoza]
2 PIMM - Laboratoire Procédés et Ingénierie en Mécanique et Matériaux

In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics settings. This model is extracted from data by exploring several strategies. First, a linear one, based on the employ of Proper Orthogonal Decomposition techniques is analyzed. Second, a strategy based on the employ of Locally Linear Embedding is studied. Finally, Topological Data Analysis is employed to the same end. All the three distinct possibilities rely on a numerical integration scheme to advance the dynamics in time. This thermodynamically consistent integrator is developed on the basis of the General Equation for Non-Equilibrium Reversible–Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Phys. Rev. E. 56 (6): 6620–6632], framework so as to guarantee the satisfaction of first principles (particularly, the laws of thermodynamics). We show how the resulting method employs a few degrees of freedom, while it allows for a realistic reconstruction of the fluid dynamics of sloshing processes under severe real-time constraints. The proposed method is shown to run faster than real time in a standard laptop.

Mots clés: Data-driven fluid simulation - GENERIC formalism - Model order reduction - Real-time simulatio

Domaines: Matériaux

B. Moya, David González, Icíar Alfaro, Francisco Chinesta, Elías G. Cueto. Learning slosh dynamics by means of data. Computational Mechanics, 2019, 64 (2), pp.511-523. ⟨10.1007/s00466-019-01705-3⟩. ⟨hal-02456410⟩