About the Project
The project aims to address three issues in the large-eddy simulation (LES) of soot evolution in gas turbine engines:
(1) 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;
(2) 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
(3) 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.
To Apply: https://www.eng.ed.ac.uk/studying/postgraduate/research/phd/physics-based-and-data-driven-modelling-pollutant-emissions
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere
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