While the digital twin has become an intrinsic part of the product creation process, its true power lies in the connectivity of the digital representation with its physical counterpart. Data acquired on the physical asset can validate, update and enrich the digital twin. The knowledge contained in the digital representation brings value to the physical asset itself. When a dedicated encapsulation is extracted from the digital twin to model a specific set of behaviors in a specific context, delivering a stand-alone executable representation, such instantiated and self-contained model is referred to as an Executable Digital Twin. In this contribution, key building blocks such as model order reduction, real-time models, state estimation and co-simulation are reviewed, and a number of characteristic use cases are presented. These include virtual sensing, hybrid testing and hardware-in-the loop, model-based control and model-based diagnostics.

翻译：尽管数字孪生已成为产品创建过程中不可或缺的组成部分，其真正价值在于数字表示与其物理对应物之间的连通性。从物理资产获取的数据能够验证、更新并丰富数字孪生，而数字表示中的知识则为物理资产本身带来价值。当从数字孪生中提取专用封装，用于在特定情境中建模特定行为集合，从而生成独立可执行的表示时，这种实例化且自包含的模型被称为可执行数字孪生。本文综述了模型降阶、实时模型、状态估计和协同仿真等关键构建模块，并呈现了若干典型用例，包括虚拟传感、混合测试与硬件在环、基于模型的控制以及基于模型的诊断。