On the paradigm of combining data analytics and CFD

Computer-Aided Engineering (CAE) has supported the industry in its transition from trial-and-error towards physics-based modelling, but our ways of treating and exploiting the simulation results have changed little during this period. Indeed, the business model of CAE centers almost exclusively around delivering base-case simulation results with a few additional operational conditions. In this paper, we describe a new paradigm for treating and exploiting simulation data: instead of reporting simulations of base-case and additional scenarios, databases covering a wide spectrum of operational conditions are built on the basis of simulation data with machine learning algorithms. We refer to the end product resulting from hybrid physics-based and data-driven modelling as Simulation Digital Twin. While the approach is suitable for all sorts of CAE applications, the present contribution addresses the fluid-flow simulation (CFD) sub-branch of CAE.