These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Due to global competition, reducing development costs and time to market becomes essential also but not only in Automotive industry. In order to achieve this goal, a digital twin is becoming a key factor. Digital twins are virtual representations of physical systems or processes. This enables developers to detect problems much earlier in design process ('œfail early') and also to more efficient find 'œoptimal' solution for design of the product. In this area methods from artificial intelligence can make an important contribution. To make use of this technology, the access to reasonable training data is required. A simulation process and data management system (SPDM) is essential for the successful deployment of a credible digital twin, as it enables the management, organization and use of the extensive simulation data. Without SPDM, managing large amounts of data, ensuring data integrity and traceability would be challenging and hard to achieve. An SPDM system thus ensures that the digital twin can unfold its full functionality and can be used as a powerful tool for predictions, optimizations and decision-making. A digital twin can integrate both systems (1D) and geometry (3D) simulations to create a comprehensive picture of a real-world system. While 1D simulations are well suited to model systemic processes and dynamic flows, 3D simulations provide detailed physical insights into the behavior of individual components. 1D and 3D simulations serve as foundational elements for Digital Twins because they provide complementary approaches to modeling, analyzing, and understanding complex systems. The combination of these two simulation domains enables a complete analysis of both system-level behavior and the detailed physical properties of a product. For an SPDM system, it is therefore essential to be able to serve both simulation domains equally. In order to build a credible digital twin, it must be ensured that all simulations across all relevant domains base on the same input data like PDM/CAD and technology data (physical parameters '“ 'œreal world data'). In this context traceability of the input data for each simulation is a key factor. In this presentation, the concepts and their realization with the help of a SPDM system will be demonstrated: Technical parameters are the base for simulation models across all disciplines. It is important to ensure that all input parameters are provided by a single source. Using intelligent plugin mechanism, the solver model will be populated automatically by the technical parameters. The digital twin will be tested in various 'œvirtual test rigs' (different load cases and disciplines) and rated based on specified KPIs. A digital twin consists of simulations from multiple disciplines. In some cases the result of one simulation (from discipline A) is used as input in other simulations (in discipline B). Therefore a SPDM system should be capable to handle 1D and 3D simulations. This will give full traceability from technical input parameters to the result and vice versa - a key enabler for a credible digital twin. The paper describes the process consisting of the steps collecting technical parameters, setup simulations for multiple disciplines, analysis of results and gain the full traceability for data representing the digital twin.