Study level

Vacation research experience scheme

Faculty/School

Topic status

We're looking for students to study this topic.

Supervisors

Professor Chris Drovandi
Position
Professor
Division / Faculty
Faculty of Science
Associate Professor Emilie Sauret
Position
Associate Professor
Division / Faculty
Faculty of Engineering

Overview

High-density fluids are commonly use in renewable applications, and in particular for low-to-medium renewable power cycles using Organic Rankine cycles (ORC). The optimisation of the turbo-expander, a key component in these cycles, is of critical importance for robust design.

This project aims at optimising such turbo-expander for ORC using Bayesian techniques that allow for uncertainties to be accounted for in the optimisation process. This is an important aspect while considering the strong impact of uncertainties on the fluid flow properties and efficiency calculation in such expanders.

Research activities

  • Turbine design and simulations using in-house and commercial software.
  • Carry out CFD simulations and Bayesian optimisation.
  • Carry out literature review.
  • Report results and findings.

Outcomes

The project aims at computationally optimising ORC turbines using a Bayesian framework. Outcomes include a detailed ORC design, CFD simulations and optimisation.

Skills and experience

You should have skills and experience in:

  • fluid mechanics and dynamics
  • computational modelling
  • Ansys
  • Matlab.

Keywords

Contact

Contact the supervisor for more information.