Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.

Generating structured meshes is often an expensive process, limiting the use of high-fidelity numerical simulation methods for design, especially when the design is not yet fixed and design updates are likely. For hexahedral meshes generated by decomposing a B-Rep model into regions for which simple meshing strategies are known, robustly propagating design modifications to the decomposed representation and subsequent mesh is very challenging. In this paper, an approach is presented to propagate parametric update and feature changes to structured meshes. Geometric and topological modifications on the design model are identified first, enabling the equivalent modifications to be identified on the decomposition of the design model. Virtual topology operations used to generate the initial decomposition are combined with the meshing constraints of individual sub-regions to update the decomposition and associated mesh locally. The approach is demonstrated for a number of design updates.

Defining Simulation Intent involves capturing high level modelling and idealisation decisions in order to create an efficient and fit-for-purpose analysis. These decisions are recorded as attributes of the decomposed design space. An approach to defining Simulation Intent is described utilising three known technologies: Cellular Modelling, the subdivision of space into volumes of simulation significance (structures, gas paths, internal and external airflows, etc.); Equivalencing, maintaining functional links between different analysis representations of the same region of design space across multiple analysis models; and Virtual Topology, which offers tools for partitioning and de-partitioning the model without disturbing the manufacturing oriented design geometry. The end result is a convenient framework to which high-level analysis attributes can be applied, and from which detailed analysis models can be generated with a high degree of controllability, repeatability and automation. There are multiple novel aspects to the approach, including its reusability, robustness to changes in model topology and the inherent links created between analysis models at different levels of fidelity and physics. By utilising Simulation Intent, CAD modelling for simulation can be fully exploited and simulation work-flows can be more readily automated, reducing many repetitive manual tasks (e.g. the definition of appropriate coupling between elements of different types and the application of boundary conditions). The approach has been implemented and tested with practical examples, and significant benefits are demonstrated.