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

Abstract

Computer-Aided Engineering (CAE) relies on physics-based computational models to perform analysis tasks of industrial products at reduced cost and time-to-market. The possibility to simulate the behaviour of different design variants, with limited resort to physical prototyping and testing, facilitates the achievement of quality and sustainability targets while increasing profit margins. However, the positive effects of driving product development and manufacturing via CAE depend essentially on the predictive capability of the computational models used in the simulations. Potential sources of uncertainty for the results of numerical simulations include all the intrinsic elements of the model building and analysis processes, such as modelling assumptions, variability of physical properties, measurement uncertainty, and numerical errors. Furthermore, human errors in the use of the models as well as in the way the models are managed during the whole lifecycle might make simulation outcomes deviate from reality. Simulation Governance is the process to ensure that the predictive capability of numerical simulations is adequate for their intended use (essentially, a Quality Assurance function tailored to CAE). Activities such as model verification, validation, and uncertainty quantification fall within the scope of Simulation Governance. The systematic, large-scale implementation of Simulation Governance is often hindered by the lack of dedicated resources resulting often from the lack of guidance on how to translate key concepts and methods from the scientific context where they originate to the context of industrial research and development. In this contribution, we will present reflections and outcomes from the recently ended research project TRUSTIT, which targeted the problem of integrating uncertainty quantification and sensitivity analysis into industrial CAE workflows, namely in complete vehicle simulations performed at Volvo Cars with the in-house developed tool VSIM. The key requirements to establish a complete simulation credibility assessment framework are identified, together with the implications for simulation-driven design and virtual testing. The practical challenges to integrate uncertainty quantification in existing simulation platforms will be illustrated with an example based on the virtual representation of the coastdown test, which is designed to determine the aerodynamic and mechanical resistive forces acting on vehicles.