IMT Physics-based and Data-driven Modelling of pollutant Emissions from Engines

The project that I'm interested is advertised and its titled "Physics-based and Data-driven Modelling of pollutant Emissions from Engines ". The project involves in modeling soot particle emissions from gas turbine engines. Soot is a major pollutant produced by gas turbine engines therefore the ability to model and predict soot is crucial to the development of next generation low emission gas turbine and internal combustion (IC) engines.
Modeling soot emissions a particularly challenging problem due to its small scale interactions between turbulence, particle dynamics and chemistry. To study soot particle evolution in gas turbine engines, it requires four different components: model for background turbulent flow, model for gas phase combustion, model for physico-chemical mechanisms that effects the soot particles by various micro-process like inception, growth and oxidation and model for particle evolution dynamics.
The most accurate way to simulate soot emissions is through direct numerical simulations (DNS) which directly solves the unsteady Navier-Stokes equations and is capable of resolving small scale interactions of soot particles in turbulent flows but these solutions come with a great deal of computational expense. Due to this reason other relatively less computationally expensive models have been extensively used, such as the large eddy simulations (LES). Even though LES is widely employed to model turbulent reacting flows, it still remains a formidable challenge to achieve accurate modeling of small scale interactions between soot particles, chemistry and turbulence. Therefore this PhD project aims to address three issues encountered in LES when modeling soot formation and evolution in order to develop an enhanced LES model to accurately predict soot emissions in a model gas turbine combustor. The three main issues addressed are listed below.
1.Develop a consistent LES/probability density function (PDF) approach on unstructured meshes to accurately characterize small scale interactions between turbulence, soot and chemistry in a gas turbine model combustor by solving the joint sub-filter 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.Incorporate 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.

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 new enhanced LES/PDF-MOM model will be used to simulate a model gas turbine combustor developed by DLR Germany. The results will be validated using a dataset provided by DLR, which was experimentally produced using high speed laser diagnostics in a high pressure gas turbine combustor.
A DNSs will be run on turbulent wall jet-diffusion flame and the valuable dataset obtained will be used to train a convolutional neural network (CNN) based reduced order model for predict soot emissions from gas turbine engines. The aim is to combine the physics-based model (obtained from achieving the previous objective) and the CNN model to develop a CNN assisted hybrid physics-based model that is capable of accurately predicting soot emission at a reduced computational cost.