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

我感兴趣的项目是广告，它的标题是“基于物理和数据驱动的发动机污染物排放建模”。该项目涉及对燃气涡轮发动机的烟尘颗粒排放进行建模。煤烟是燃气轮机发动机产生的主要污染物，因此对煤烟的建模和预测能力对下一代低排放燃气轮机和内燃机的发展至关重要。由于烟尘排放在湍流、粒子动力学和化学之间的小尺度相互作用，因此建模是一个特别具有挑战性的问题。研究燃气涡轮发动机烟尘颗粒演化需要四个不同的组成部分：背景湍流模型、气相燃烧模型、烟尘颗粒通过起始、生长、氧化等各种微过程影响的物理化学机制模型和颗粒演化动力学模型。直接数值模拟（DNS）是模拟烟尘排放最准确的方法，它直接求解非定常Navier-Stokes方程，能够求解湍流中烟尘颗粒的小尺度相互作用，但这些方法的计算开销很大。由于这个原因，其他计算成本相对较低的模型被广泛使用，如大涡模拟（LES）。尽管LES被广泛应用于湍流反应流的建模，但如何精确模拟烟尘颗粒、化学和湍流之间的小尺度相互作用仍然是一个巨大的挑战。因此，本博士项目旨在解决LES在模拟烟灰形成和演化时遇到的三个问题，以开发一个增强的LES模型来准确预测模型燃气轮机燃烧室中的烟灰排放。讨论的三个主要问题列于下文1。通过求解用于描述火焰结构和气相前驱体演化的标量的联合子滤波PDF方程以及烟灰颗粒的数密度函数矩，在非结构化网格上建立一致的LES/概率密度函数（PDF）方法，以准确表征燃气轮机模型燃烧室中湍流、烟灰和化学之间的小尺度相互作用2。将单个物种的分子扩散系数加入到PDF求解器中，研究分辨微分扩散对烟灰颗粒成核、生长和氧化的影响。评价煤烟特性对煤烟前体化学的敏感性和对矩量法（MOM）的选择的敏感性，该方法用于重建煤烟颗粒的NDF。新的增强型LES/PDF-MOM模型将用于模拟德国DLR公司开发的燃气轮机燃烧室模型。结果将使用DLR提供的数据集进行验证，该数据集是在高压燃气轮机燃烧室中使用高速激光诊断实验产生的。本文将在紊流壁面射流扩散火焰上运行dns，并将获得的有价值的数据集用于训练基于卷积神经网络（CNN）的降阶模型，用于预测燃气涡轮发动机的烟尘排放。目的是将基于物理的模型（通过实现上一个目标获得）与CNN模型相结合，开发一个能够以更低的计算成本准确预测煤烟排放的CNN辅助混合物理模型。

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 particles2.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.