Integrating supervised learning and applied computational multi-fluid dynamics

Abstract

The transition from iterative methods of engineering design towards physics-based modeling has been assisted by the advent of Computer-Aided-Engineering. However, post-processing of simulation results, based on a standard workflow providing base-case simulations complemented by selected operating conditions, has changed little. In this work, we propose a new paradigm for handling simulation data by deploying machine learning to encompass a wide spectrum of operating conditions, bypassing the need for additional simulations. This hybrid physics-based and data-driven modeling procedure yields to what we refer to as a Simulation-based Digital Twin. In this paper, we make the case for Computational Fluid Dynamics in multiphase flow systems, although the workflow can be generalized to any other computational engineering method. We quantify the computational speed-up to conclude that the combination of these two fields generates potential for improvement on the conventional methods used in the broad area of computational engineering.