NON PARAMETRIC OPTIMISATION FOR ENGINEERING APPLICATIONS
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<http://icelab.uk/> www.icelab.uk), 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.
*Project Aims and Objective*
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.
*Qualifications*
Applicants should hold a Masters degree in mechanical engineering, applied mathematics or physics.
*Experience*
Experience in the field of engineering analysis, machine learning and optimisation is an asset.
*Starting date* Academic year 2018/2019
*Student eligibility*
UK and EU students.
This project is fully funded.
*Contact*
Annalisa Riccardi ([log in to unmask])
Edmondo Minisci ([log in to unmask])
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