Embedding data analytics and CFD into the digital twin concept. (15th January 2021) - Molinaro, Roberto; Singh, Joel-Steven; Catsoulis, Sotiris; Narayanan, Chidambaram; Lakehal, Djamel
Highlights: A new paradigm for the exploitation of computational physics data is proposed, consisting in using machine learning to cover a wider spectrum of operational conditions and allow real-time response on field for faster decisions. We create Simulation Digital Twins (SDT) to replicate asset's health (the so-called predictive maintenance), control the production, and optimize the processes. We show that data analytics should rather be combined with CAE, including CFD. With the gap between the two being bridged, the design of a new class of 'integrated' models for system behaviour modelling can materialize. Abstract: 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 contribution, we introduce a new paradigm for the exploitation of computational physics data, consisting in using machine learning to enlarge the simulation databases in order to cover a wider spectrum of operational conditions and provide quick response directly on field. The resulting product from this hybrid physics-informed and data-driven modelling is referred to as Simulation Digital Twin (SDT). While the paradigm can be equally used in different CAE applications, in this paper we address its implementation in the context of Computational Fluid Dynamics (CFD). We show that the generation of Simulation Digital Twins can be efficiently accomplished with the combination of the CFD tool TransAT and the data analytics platform eDAP .