Research into the feasibility of an additive-manufactured ultra high efficiency, high temperature micro gas turbine. The project aims to carry out fundamental research into a highly novel micro gas turbine by designing, manufacturing and testing a combustion system with industry support from HiETA Technologies utilising Additive Manufacturing to create high efficiency cooling systems. The objective is to prove the feasibility of running a system at very high gas temperatures to yield efficiency improvements. To start, research will be conducted on already existing combustor designs for similar micro-gas turbine applications, to gain an understanding of the already existing technology in the market and identify possible improvements that can be implemented with the use of additive manufacturing. This research will then feed into the initial proof of concept design that will then be analysed using CFD, manufactured by the project industrial partner HiETA and tested in the hot gas stand cell at Bath once it is fitted with a high temperature turbine. Further research on state of the art combustion cooling designs and CFD analysis on fuel delivery and combustion processes will follow, which will lead to multiple designs for a state of the art combustion system, which HiETA will assist in manufacturing. The designs will then be tested at high temperatures in the hot gas stand test cell at Bath again to validate the designs.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

TBC 22/23

This project will investigate the use of machine learning and neural network methodologies to solve problems involving partial and stochastic differential equations. We will initially consider contaminant dispersal models as an exemplar; in this problem pollutant particles are modelled individually and we are interested in learning the distribution of a large number of such particles. The Fokker-Planck equation for this models is high dimensional, and currently only solvable using Monte Carlo methods. The first part of the project will focus on the efficient approximation of the solution to the forward problem using deep learning methods. A TensorFlow implementation of a deep learning high dimensional PDE solver will be created which incorporates suitable boundary conditions and background flow field. This approach will be analysed analytically where possible and compared to existing methods, such as the MLMC method developed by G. Katsiolides. Once implemented this method of solution will create avenues which can be used to approach the inverse problem of using data to parameterise the model by applying deep learning techniques and/or Bayesian methods; this part of the problem will be explored subsequently. There are a range of applications which could be considered in the later stages of the project, these include, but are not limited to, stochastic PDE models of particle movements, and stochastic optimal control.