Topology Optimisation of Hydraulic Devices

Development and application of topology optimisation methods and tools to find the best configuration of innovative hydraulic devices – or some of their components - to achieve the best structural design

Number of places

One

Opens

18 December 2018

Deadline

30 April 2019

Duration

36 months

Eligibility

Applicants must hold Masters in Mechanical Engineering or Applied Mathematics

Project Details

With the spread of modern additive manufacturing techniques, topology optimisation represents an advanced methodology for structure optimisation. Topology optimization algorithms address the problem of structure optimisation, by targeting the optimal distribution of material and void regions within a predefined design space.

As in other fields of optimisation, also in topology optimisation, gradient based optimisation techniques have the well-known limitations for engineering applications (need of a smooth model, convergence to local solutions), while stochastic methods, even if able to handle black-box models they can tackle problems in limited size. The neuro-evolution approach is the one proposed in this research and is aiming at bridging the gap between those two families of techniques. Topology optimisation is performed by optimising the parameters of a neural network that models the material thickness and distribution. Finite element analysis is performed at each step of the optimisation to evaluate the structural performance of the current solution.

This research project is about the development of neuro-evolution topology optimisation techniques and their application to the design of hydraulic devices, or their components  - to achieve the best structural design approach to optimise the shape of existing hydraulic devices.

 

Funding Details

This project is unfunded, and therefore would be suitable to eligible applicants with self funding, or with the possibility of other sources of funding. Partial funding may be available through schemes of the University

Supervisor

Supervisors: Dr E Minisci and Dr Annalisa Riccardi