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Job Record #16716
TitleTurbulent Combustion and Soot Modeling
CategoryPhD Studentship
EmployerUniversity of Edinburgh
LocationUnited Kingdom, Scotland, Edinburgh
InternationalYes, international applications are welcome
Closure DateFriday, January 01, 2021
Description:
Overview:
====================
The Smart Lab in the School of Engineering at The University of Edinburgh invites 
application for a PhD position to conduct research in the area of turbulent 
combustion and soot modeling. Tuition fees + stipend are available for UK/EU 
students (International students can apply, but the funding only covers the 
Home/EU fee rate). Applications are also welcomed from those who have secured 
their own funding through scholarship, sponsorship or similar. The anticipated 
starting date is January 2021 at the latest. Early starting date is possible.

Project Description:
====================
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. 

Project objectives: 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.

Application:
====================
To apply, please email your detailed CV along with research statement in a single 
PDF file to wang.han@ed.ac.uk.

For more information follow the following links
https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=947
https://www.eng.ed.ac.uk/about/people/dr-wang-han
https://edinsmartlab.github.io/
Contact Information:
Please mention the CFD Jobs Database, record #16716 when responding to this ad.
NameWang Han
Emailwang.han@ed.ac.uk
Email ApplicationYes
URLhttps://edinsmartlab.github.io/
Record Data:
Last Modified17:03:30, Thursday, August 27, 2020

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