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Non Parametric Optimisation for Engineering Applications

Project Description

The Department of Mechanical & Aerospace Engineering at the University of Strathclyde (Glasgow, UK) is looking for a motivated student to be enrolled in their PhD program.

The student will be part of the Intelligent Computational Engineering Laboratory (ICE Lab, and he/she will be working, alongside the other researchers in the group, in the development and application of the latest computational intelligence techniques to the solution of challenging engineering problems.

Development and application of non-parametric optimisation methods and tools to find the best configuration of innovative engineering devices – or some of their components.

With the spread of modern additive manufacturing techniques, non-parametric optimisation techniques (operating at the node/element level to derive optimal structures) represents an advanced methodology for engineering optimisation with additional design freedom with respect to parametric methods. Non-parametric optimisation algorithms address the problem of optimising a geometry, by targeting the optimal distribution of material, and void regions, within a predefined design space.

As in other fields of optimisation, also in non-parametric optimisation, gradient-based optimisation techniques have the well-known limitations for engineering applications (need of a smooth model, convergence to local solutions). The proposed research is aiming at investigating novel technologies, such as neuro-evolution, that are more suitable for practical engineering problems.
This research project is about the development of non-parametric optimisation techniques and their application on a real case study, to the design of engineering devices, or their components – to achieve the best fluid-structural design.

Proposed Start date: October 2019

Funding Notes

This project, which is funded by Industrial partners, covers Home/EU t, and a monthly stipend, of approximately £15,000 per year, for the 3 year period of study.

Applicants should hold a Masters degree in Mechanical Engineering, Applied Mathematics or Physics. Experience in the field of engineering analysis, machine learning and optimisation is an asset.

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