Model generation architecture for digital twins and deployment system

A Digital Twin is a virtual copy or representation of a physical object. Given the same input, both should produce the same output. A bidirectional data exchange between both of them should keep the objects in the same state by automatically reflecting changes from one of the objects to the other. Despite the popularity of the topic, there is a lack of standardization and no widespread adoption of techniques or architectures, resulting in ad-hoc implementations for each use case. This paper proposes a methodology for the generation of digital twins based on the definition of their life cycle, with phases that include training, operation and retraining. To allow efficient management of resources, each phase of the Digital Twin is implemented as a PyCOMPSs workflow. The proposed methodologies and architectures aims to simplify and strengthen the design and development of digital twins. It also allows for the extension of existing ones, the replacement of their components with more advanced ones, and the support of their reuse. We also consider workflow deployment, which is often a difficult task requiring specialized personnel and time. This article focuses on presenting the training phase of a Digital Twin life-cycle and demonstrates its operation by applying it to two real-world use case scenarios in ongoing projects. The use of the proposal led to a simplification of the developed code, allowed a distribution of the computational work among the available resources in a user-agnostic manner and the digital models created faithfully represent the physical object. Additionally, the paper presents the design of the operating life-cycle phase of a Digital Twin.