Dynamically parameterising CAD models using sensitivities for optimisation

To design complex products, engineers need to consider and optimise many different attributes. In aerospace, optimisation mainly considers both structural (e.g. displacements, accelerations) and fluid (e.g. pressures acting on a body) attributes. One of the main factors which can impact performance is product shape, which affects a number of disciplines. When changing the shape of the design the options are to change the analysis model (i.e. a mesh) or the geometry model which represents the design. The preferred option is to optimise the geometry model as the result is integrated with the wider design enterprise (e.g. it can also be used for manufacturing considerations). This is particularly true if the geometry model is a feature based CAD model (e.g. Catia V5 or Siemens NX). In a feature based CAD system, the object shape is modified using the parameters which define the features that make up the model itself.

One challenge is that the variables which define the shape of the design and control how it can change, may not actually be well suited for the disciplines driving the optimisation. This means that regardless of how much effort the optimiser puts in, it will not be possible to reach a truly optimum design. This three year project will ensure the parameterisation is suited to optimisation by investigating robust methodologies to automatically insert new features into the CAD model, for which the associated parameters will be new optimisation variables. This will rely on robust and efficient new methods for computing multi-disciplinary sensitivities. The project benefits from collaboration with a major UK industrial partner (Airbus) and developers of key analysis software (DLR). They will assist in researching a new capability with the overall aim of "delivering a step change in the configuration, time to market and performance of new designs." The following objectives have been set:

1. Implement strategies for improving CAD parameterisations for multi-disciplinary optimisation by automatically inserting features into the model based on sensitivity.

2. Investigate efficient and robust methodologies for computing aero-structural sensitivities. This will see a novel approach to the calculation of the sensitivities.

3. Develop strategies for coupling and coherently meshing solid and fluid models. This is a key piece of research required in any aero-structural analysis.

4. Combine aero-structural sensitivities with CAD parameterisation strategies, in an automated optimisation framework, for a range of test cases. This is where the benefits of the work will be demonstrated to industry.

5. Quantify the decrease in time to market and increase in performance due to this research.

Application areas for this research include the design of products which require the optimisation of complex shapes. It will be particularly relevant in industries where feature based CAD systems underpin the design process, and where the physics of the problem may identify the need for shape features which may not be apparent when the CAD models are being setup. An example may be where the surface sensitivities suggest the need for a winglet, but where the parameterisation of a basic wing does not include the parameters to allow such a feature to form. Benefits include:

1. the ability to discover new, optimum, configurations. This is a route to innovative design solutions which will help to keep the UK as a world leader in the design and manufacture of complex products;

2. improved product performance due to the improved optimisation variables (CAD parameters) created based on the requirements of the physics of the problem. For air travel this will result in more environmentally friendly aircraft and lower travel prices;

3. reduced development times due to an automated and efficient optimisation processes, leading to new, better performing, products being available sooner;

By introducing automated design optimisation procedures, this research will impact industrial productivity and accelerate the development of new products. The automatic placement of new features in a feature based CAD model, guided by multi-disciplinary sensitivity information, means that many of the design decisions about where to place features and parameters, which are currently made by a designer, will now be informed by physics based methods. As such, the skillset required by, and role of, the designer will change. This will reduce the expense of designing new, complex products which will reduce overall product cost. As the positioning and choice of the features to be added will be based on rational metrics, they may indicate a route to performance improvement not previously considered. The benefit of such information could be considerable if an innovative solution is produced which offers a large performance improvement. The new parameters will provide optimisation variables capable of producing innovative designs that can outperform alternatives obtained using the original, immutable, parameters, resulting in a commercial advantage and reduce time to market.

The UK will benefit because the processes will mean its position as a world leader in the design and manufacture of complex products (such as aircraft) will be enhanced. The automated addition of new features will mean that innovative design solutions will emerge, resulting in a commercial advantage. The improved integration of structural and fluid domains will have a significant impact on all complex engineering projects, where the consideration of both disciplines is essential. The ability to automate the addition of features, and the removal of the roadblocks when integrating structural and fluid models, will mean that the time to run an optimisation cycle for a new product will be shorter. This benefit can be exploited by products undergoing more optimisation cycles, thereby improving performance, or by products reaching the market more quickly, resulting in significant commercial advantage. As manufacturing is an essential part of the UK economy, the advantages brought about by this increased capability will be significant. This could translate into a significant competitive advantage for Airbus and UK PLC in the commercial airline market and allow subsequently lower airfares.

Environmental benefit will be generated through better performing products, which in terms of aerospace translates into lower emissions and noise. The global nature of aerospace, and the contribution it makes to environmental pollution, means that a percentage reduction in the emissions from an aerospace product will have a significant global impact.

Societal benefit will be realised not just through the UK economic and environmental benefits, but also by the improvement in performance. The use of automated toolsets will result in much lower product and usage costs, therefore a reduction in travel fares and the associated increase in social benefits that more accessible transport systems create.

The PDRAs will benefit from being involved in an innovative, industrially relevant and well supported project. This will develop them as individuals, researchers and future technology leaders.

The PhD student will have the significant benefit of conducting a PhD in a collaborative environment, with the PDRAs playing a key role in developing and mentoring the PhD student. The association of the project with key partners, and the opportunity to be seconded to them, will add another dimension to the student's knowledge and skill set.