SIMULATION: Transactions of The Society for Modeling and Simulation International

Gomes, Claudio

doi: 10.1177/00375497241269721pmid: N/A

Diakité, Mama; Traoré, Mamadou Kaba

doi: 10.1177/00375497241228281pmid: N/A

Nowadays, smart systems require the use of Digital Twins (DTs) for their engineering and management. Self-updating capability is a key feature in the DT technology. This raises the challenge of model inference from data collected on the system and requires a formal framework be defined, in which a system representation can be coupled with inference methods to achieve automatic model updating. While data-based process model inference is a well-known technique, the inference of a simulation model from existing knowledge and collected data is yet an unexplored area. In this paper, we first clarify and explicitly define some key elements of the terminology related to the DT concept, including model update and model inference, and we propose a framework of inference capabilities based on a formal DT specification, which formally captures the conditions for different updating capabilities and relates them. That way, on one hand, the framework enables the use of symbolic approaches to build a DT with the desired inference capability, and on the other hand, it establishes a partial order relation between inference capabilities. Through a case study, we show how the framework helps formally specifying the DT model of a mobility system toward realizing a fully capable DT.

Dirnfeld, Ruth; De Donato, Lorenzo; Somma, Alessandra; Azari, Mehdi Saman; Marrone, Stefano; Flammini, Francesco; Vittorini, Valeria

doi: 10.1177/00375497241229756pmid: N/A

In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber–physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.

Liu, Zizheng; Chu, Yingguang; Li, Guoyuan; Hildre, Hans Petter; Zhang, Houxiang

doi: 10.1177/00375497241228623pmid: N/A

Marine cranes are one of the most important industrial equipment in the maritime field. The base of a marine crane is dynamically moving as the motion of the ship’s six degrees of freedom that is affected by offshore environmental loads. There is a coupling between the crane and the ship, which means the crane operation and the ship motion affect each other. In this paper, co-simulation technology is employed to construct the virtual marine operation system which is composed of diverse Functional Mock-Up Units (FMUs) exported using the Functional Mock-Up Interface (FMI) standard and System Structure and Parameterization (SSP) standard to define the structure and parameters based on the co-simulation platform Vico. A path planning case for the Palfinger crane is implemented using the A* algorithm. The physical three-dimensional working space of the crane is discretized into a finite number of nodes in joint space. The cost is defined by the variable of the ship motion to optimize the marine operation. The obtained discrete nodes are smoothed to get the velocity of the actuators as control signals. Simulation of the crane operation is carried out in the virtual operating system following the planned path.

Marah, Hussein; Challenger, Moharram

doi: 10.1177/00375497231226436pmid: N/A

The digital twin (DT) mainly acts as a virtual exemplification of a real-world entity, system, or process via multiphysical and logical models, allowing the capture and synchronization of its functions and attributes. The bridge between the actual system and the digital realm can be utilized to optimize the system’s performance, and forecast and predict its behavior. Incorporating intelligent and adaptive reasoning mechanisms into DTs is crucial to enable them to reason, adapt, and take efficacious actions in complex and dynamic environments. To this end, we introduce an approach for deploying agent-based DTs for cyber-physical systems. The foundation pillars of this approach are (1) integrating the concepts, entities, and relations of Zeigler’s modeling and simulation framework from the perspective of agent-based DTs; (2) utilizing an expandable and scalable architecture for designing and materializing these twins, which handily enables extending and scaling physical and digital assets of the system; and finally (3) a two-tier reasoning strategy; reactive and rational models are conceptually redefined in the context of the modeling and simulation framework of agent-based DTs and technically deployed to boost the adaptive reasoning and decision-making function of DTs. As a result, an implemented simulation and control platform for a multi-robot system demonstrates the approach’s applicability and feasibility, manifesting its usability and efficiency. The platform represents physical entities as autonomous agents, creates their DTs, and assigns adequate reasoning capability to promote adaptive planning, autonomous resource management, and flexible logical decision-making to handle different situations and scenarios.