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Details of Grant 

EPSRC Reference: EP/P025692/1
Title: Dynamically parameterising CAD models using sensitivities for optimisation
Principal Investigator: Robinson, Professor TT
Other Investigators:
Marques, Dr SP Armstrong, Professor CG
Researcher Co-Investigators:
Project Partners:
Airbus Operations Limited German Aerospace Center (DLR)
Department: Sch Mechanical and Aerospace Engineering
Organisation: Queen's University of Belfast
Scheme: Standard Research
Starts: 01 July 2017 Ends: 31 January 2021 Value (£): 595,059
EPSRC Research Topic Classifications:
Aerodynamics Design & Testing Technology
Design Engineering Fluid Dynamics
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Feb 2017 Engineering Prioritisation Panel Meeting 9 and 10 February 2017 Announced
Summary on Grant Application Form
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;

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.qub.ac.uk