» Physics-based and Data-driven Modelling of Pollutant Emissions from Engines
Physics-based and Data-driven Modelling of Pollutant Emissions from Engines
In the development of next-generation aero or diesel engines, one of the main concerns is the reduction of particulate emissions. Soot is a particularly challenging modelling problem due to the small scale interactions between turbulence, particle dynamics, and chemistry. Numerical simulation and modeling of soot evolution in turbulent reacting flows require models for four different components: (1) the background turbulent flow, (2) gas-phase combustion, (3) the physico-chemical mechanisms that alert soot particles by various micro-processes of inception, growth, and oxidation for soot particles, and (4) particle evolution dynamics.
The project aims to address three issues in the large-eddy simulation (LES) of soot evolution in gas turbine engines:
developing a consistent LES/probability density function (PDF) approach on unstructured meshes to accurately characterize the small-scale interactions between turbulence, soot, and chemistry in a gas turbine model combustor by solving the joint subfilter PDF equation of the scalars used to describe the flame structure and gas-phase precursor evolution as well as the moments of number density function (NDF) of soot particles;
incorporating molecular diffusivities of individual species into the PDF solver to study the effects of resolved differential diffusion on nucleation, growth, and oxidation of soot particles; and
assessing the sensitivity of soot characteristics to soot-precursor chemistry and to the choice of method of moments (MOM) that is used to reconstruct the NDF of soot particles.
The enhanced LES/PDF model will be validated by high-speed laser diagnostics data produced at DLR Germany in a high-pressure gas turbine combustor (https://doi.org/10.1016/j.proci.2014.05.135). Moreover, the valuable databases during achieving the above objectives will be used to train a Convolutional Neural Network (CNN) based reduced-order model for predicting soot emissions from gas turbine engines.
https://www.eng.ed.ac.uk/about/people/dr-wang-han
https://edinsmartlab.github.io/
Principal Supervisor:
Dr Wang Han
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate)
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
Further information and other funding options.
Informal Enquiries:
wang.han@ed.ac.uk