Model generation architecture for digital twins and deployment system

Abstract

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.

Author Fernando Vázquez is supported by PRE2022-104134 funded by MICIU/AEI /10.13039/501100011033 and by the FSE+. This work has been partially funded by the the projects PID2019- 107255GB, CEX2021-001148-S, and PID2023-147979NB-C21 from the MCIN/AEI and MICIU/AEI /10.13039/501100011033 and by FEDER, UE, and by the Departament de Recerca i Universitats de la Generalitat de Catalunya, research group MPiEDist (2021 SGR 00412), and by the European Commission through the program Horizon 2020 and the European High-Performance Computing Joint Undertaking (JU) with contract 955558 and by the MCIN/AEI/10.13039/501100011033 y European Union NextGenerationEU/PRTR (PCI2021-121957), project eFlows4HPC; as well as by the European Commission under program Horizon Action under contract 101056886 (CAELESTIS). This work has also received funding from the European High Performance Computing Joint Undertaking (JU) and Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement No 101093038.

Peer Reviewed

Postprint (author's final draft)