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.

中文翻译：

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将数据分析和 CFD 嵌入数字孪生概念中

摘要 计算机辅助工程 (CAE) 支持该行业从试错法向基于物理的建模过渡，但在此期间，我们处理和利用模拟结果的方式几乎没有变化。实际上，CAE 的业务模型几乎完全围绕提供具有一些附加操作条件的基本案例仿真结果。在这一贡献中，我们引入了一种用于利用计算物理数据的新范式，包括使用机器学习来扩大模拟数据库，以涵盖更广泛的操作条件并直接在现场提供快速响应。这种基于物理信息和数据驱动的混合建模的结果被称为模拟数字孪生 (SDT)。虽然该范式可以在不同的 CAE 应用程序中同等使用，但在本文中，我们将讨论其在计算流体动力学 (CFD) 背景下的实现。我们表明，通过结合 CFD 工具 TransAT 和数据分析平台 eDAP，可以有效地生成仿真数字孪生模型。