Domain #3: Scientific computing

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Understanding reality through physical modelling

The understanding of the functioning of systems through their physical modelling (mechanical, thermal, fluidic behaviour, etc.) requires a fine mathematical and analytical description for conducting realistic simulations over wide validity domains.
Model reduction and distributed computing methods help to achieve compromises between the level of representativeness of the models (margins, robustness) and the computing time. These approaches are very useful for the «extended enterprise», where co-simulation involves several partners.

Challenges

Companies have to reduce calculation times and better prepare the physical tests necessary for the approval phases of their products, and for the monitoring phases once they are deployed. There are multiple objectives to be addressed, from the design phases of products to the description of their life cycle (ageing, damage, maintenance).

Positioning of the institute

In recent years, IRT SystemX has invested heavily in the field of scientific computing and simulation, a priority area for the digital transformation of companies. The institute provides state-of-the-art solutions and develops more upstream research work in order to address challenges such as the hybridisation of scientific computing with data-based approaches, co-simulation approaches, the propagation of margins in design and the deployment of digital chains in additive manufacturing.

Roadmap

Scientific and technological challenges	Related research fields
Multi-scale, multi-physics	• Hybridization of simulation and data-based models • Topological optimisation • Modelling and simulation of the impact of the manufacturing process on part performance • Modelling and simulation of the topology induced by the manufacturing process • Reduction of models
Numerical simulation engineering	• Specification of simulation requirements in relation to system engineering • Assistance in the development of simulation and co-simulation • Visualisation for decision support • Quantum computing
Propagation of margins and uncertainties	• Explanation and calculation of margins in design • Correlation of manufacturing parameters with structural behaviour and defect occurrence