Computational Simulation is essential to meet these challenges, but setting up complex numerical simulations for large assembly structures is extremely time-consuming and requires highly-skilled engineers. Even though solve time is often used as a key performance indicator, model preparation is typically orders of magnitude larger that the solution time.
The main objective of the MUMPS project was to explore the basis of the next generation of Digital Mock-Ups, where geometric models are structured to cover the entire design world, not only the physical parts but also the adjacent fluid domains.
To link the geometric models, the interfaces between regions in the design space to the high-level modelling and idealisation decisions, we proposed a knowledge-based CAE framework. In this framework, to represent the next generation Digital Mock-Up, an ontology for defining simulation intent has been developed which incorporates two key capabilities, cellular modelling and equivalencing. Cellular modelling introduces the concept of cells, which subdivide the 3D space appropriately, and to which simulation attributes can be attached. Equivalencing maintains the link between different representations of the cells required for different analyses throughout the analysis lifecycle.
Firstly, an analysis tool based on relational machine learning has been developed to help analysts and designer to speed up the tedious tasks to identify and isolate the components of interest for the analysis of the CAD assemblies extracted from the PLM system. The user can identify duplicate components, or correct inconsistent models with misaligned parts or with poor-quality B-Rep geometry.
Secondly, the consistent CAD assemblies are transferred into a cellular model where information on cell geometry is linked to simulation attributes and meshing strategies through the ontology concepts and relations.
Finally, the enriched cellular model defines the next generation Digital Mock-Ups. The analysts or designers can then request the cells of interest from their CAD/CAE systems. They can use pre-defined ontology rules to capture the high-level of modelling and idealisation decisions required to set up a fit-for-purpose analysis model.
Applying the ontology rules with the reasoner enriches the CAE-oriented knowledge model and makes it available for the various CAD/CAE packages across the simulation departments. Using an ontology approach as the simulation intent framework makes it independent of CAD/CAE packages and allows the generated models to be shared by multiple simulation domains. This provides a mechanism to maintain multiple models at different levels of detail and fidelity and exploit the links between them.
