On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data

Abstract

AbstractThe development of a data-driven surrogate model (SM) is extensively studied in Eulerian–Lagrangian simulations for its advantage of high computational speed. However, in the application of granular systems with violent fluid-solid flows, how to select sufficient training data to ensure consistency between the high-fidelity model and SM remains unknown and highly challenging. The accuracy of SM can be easily deteriorated due to insufficient training data. This necessitates a trial-and-error process and hinders its industrial applications. To address this issue, this study newly reveals a finding that data density is a key to sufficient training, and we propose a novel technique for deciding the sufficient training data of SM. Specifically, a feasibility index is proposed based on posterior error analysis. It is demonstrated that when the training data is determined under the proposed feasibility index [Formula: see text] 2, the consistency of granular dynamics between SM and the high-fidelity model can be guaranteed. Employed in a representative SM, a reduced order model (ROM), this technique enables the successful decision of sufficient training data, resulting in the remarkable predictability in violent fluid-solid flows without trial-and-error. This technique holds great potential in solving the predicament of deciding training data for data-driven models.

Keywords

• Data-driven ROM
• Fluid-solid flows
• Sufficient training data
• Predictions