The Rail4Future Virtual Validation and Simulation Platform will enable for the first time: A fully virtual assessment of the behavior of the railway infrastructure system by deploying largescale simulations based on hybrid-models enhanced with Al capabilities, real-time data input from the railway operations and visual-analytics methods.
D1.1.1 Report of requirements
This report presents the requirements of the R4F platform, based on which we designed a conceptual model-based digital twin for the holistic large-scale railway infrastructure.
D1.1.2 Blueprint architecture with simulation workflow
In this report, a conceptual model-based digital twin platform that aims to integrate measurement data, numerical models, and machine learning models from different railway subsystems into a holistic large-scale railway infrastructure platform is proposed for the first time.
D1.1.3 Multi Domain Model Description
In this deliverable, a multi domain model is shown and described, which connects all simulations (multiphysics, machine learning) with its relevant data and the required semantic to interconnect all characteristics of the model. As the name suggests, it includes more than one domain, which are used to integrate and interoperate different assets, belonging to different subsystems such as track, vehicle, turnout, bridge, with each other in a DT platform called Rail4Future.
D1.1.4 Interface and Adapter Specifications
In this report, interfaces and adapters are shown and described, which are implemented into the multi domain model, mentioned in the previous Deliverable 1.1.3 (M18), to improve and optimize the model and data integration process in the Rail4Future (R4F) platform for the digitalization of the holistic large-scale railway infrastructure system.
D1.2.1 Report of Requirement Analysis
This document outlines the requirements of the visual analysis framework for the Rail4Future platform. The framework should be adaptable, integrating new insights and knowledge as the project progresses. It should adhere to principles such as the use of permissive licenses for third-party tools, facilitating extension for new use cases and data types, and enabling feature restrictions for different user groups.
D1.2.2 Specification of Visual Analysis Framework
This document describes the system architecture of the visualization layer of the Rail4Future platform. This is a living document. We will integrate new insights and knowledge generated in the course of this project, and adapt and extend it as required.
The visualization layer consists of modules dedicated to the different visualisation modalities. The dashboard module combines the modules for point cloud visualisation, maps, charts and simulations. It also includes a workspace module for asset management.
D1.2.2 OnePager - Specification of Visual Analysis Framwork.pdf
D1.2.2 Report - Specification of Visual Analysis Framwork.pdf
D1.3.2 Generic Model definition for multi-level optimization (M14)
This report presents a generic model definition for multi-level optimization in railway systems, which encompasses different model types and their corresponding use cases relevant to railways. The methodology for developing and implementing this model is outlined, alongside a modular and scalable architecture - Rail4Future (R4F) platform that integrates various components and subsystems of the railway system, such as the track, trains, and other systems.
D1.3.2 OnePager - Generic Model definition for multi-level optimization.pdf
D1.3.2 Report - Generic Model Definition for multi-level optimization.pdf
D1.3.3 Report - Enabling large-scale simulations (M18)
The holistic railway infrastructure digital twin (DT) platform is sophisticated and consists of a series of submodels (e.g., turnouts, tracks, vehicles, etc.) that are built through various methodologies and software. However, integrating these submodels i nto the DT platform is tremendously challenging due to considerable computational complexity, software and interface restrictions. In this report, we present a machine learning (ML) based surrogate modelling methodology for the submodel integration in the holistic railway infrastructure DT platform to enable the large scale simulations and illustrate the methodology through a case study.