Enriching Digital Mock Up to speed-up simulation assembly models generation

Flavien Boussuge, Christopher Tierney, Cecil Armstrong, Trevor Robinson

Research output: Contribution to conferenceAbstractpeer-review


The Digital Mock Up (DMU) represents the product being developed in a virtual sense, providing full 3D detailed geometric (CAD) models as input for analysis. However, converting large assembly CAD structures into simulation models which are efficient to run and yet sufficiently accurate for their application is extremely time-consuming and requires highly-skilled engineers. The pre-processing task is usually distributed across all the simulation departments, where analysis models are created individually for the same assemblies in order to correspond to the different physics. This practice keeps the modelling and simulation decisions in silos, and makes it difficult to transfer the decisions between disciplines. In practice, during the product development process, the initial DMU extracted from the PLM is limited to a set of individual CAD components located in the 3D space. Mechanisms to preserve and transfer data between the simulation domains are today not existent in the DMU. Consequently, analysts spend considerable time defining the physical domains and their interfaces with adjacent components. The proposed contribution described in this presentation is to enrich the extracted DMU, including by the exploitation of the Simulation Intent concept. This will provide models as not just a collection of components, but as a partitioning of the space in which the structure resides, annotated with a description of analysis attributes for any given simulation.
Concretely, the CAD assembly is enriched with calculated interfaces between components to which analysis attributes can be attached (contacts, interferences and gaps). Fluid domains (not currently described in the DMU) are extracted as new solid bodies and integrated at the assembly level. Then, each component’s shape is analysed to identify shape properties which can be exploited in the analysis model setup (e.g. reflective and cyclic symmetry). These symmetry properties are exploited to identify repetitions of components as well as repetitions of group of components (e.g. linear, circular occurrences of a bolted junction). Finally, once these connections have been established in the design space, as well as geometric properties at the assembly level, functional behaviour in the assembly is inferred. Example applications include component designation: bearings, blades, etc. or sub-assembly designation: bolted junctions etc...
As a result, this enriched DMU is geometrically and functionally structured into a cellular representation containing both structural and void cells (air, gas, etc.). The links between equivalent representations of the same regions for different analysis objectives is tracked. The repetitive and tedious geometric transformations of the CAD models to identify, locate and derive the equivalent simulation models, such as junctions and connectors transformation, boundary conditions applications, directly benefit from the functional information found in this new DMU. The linkages between the structural and fluid domains can be used to automate complex analysis tasks like the application of boundary conditions for aero-thermo-mechanical analyses. Finally, by demonstrating how to efficiently derive equivalent simulation models from the enriched DMU, we aim to reduce the current tedious and highly manual tasks of assembly model preparation for simulation analysis.
Original languageEnglish
Publication statusPublished - 2017
EventAirbus DiPaRT 2017 - Bristol, United Kingdom
Duration: 20 Sept 201722 Sept 2017


ConferenceAirbus DiPaRT 2017
Country/TerritoryUnited Kingdom


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