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

IMT 基于物理和数据驱动的发动机污染物排放建模

• 批准号: 2586071
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2586071
• 关键词: IMT; Physics; based; Data; driven

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.

我感兴趣的项目是广告，它的标题是“发动机污染物排放的基于物理和数据驱动的建模”。该项目涉及对燃气轮机发动机的碳烟颗粒排放进行建模。碳烟是燃气轮机产生的一种主要污染物，因此碳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。由于湍流、颗粒动力学和化学之间的小尺度相互作用，碳烟排放建模是一个特别具有挑战性的问题。要研究燃气轮机碳烟颗粒演化，需要四个不同的组成部分：背景湍流模型、气相燃烧模型、通过初始、生长和氧化等各种微观过程影响碳烟颗粒的物理化学机理模型和颗粒演化动力学模型。直接数值模拟是模拟碳烟排放最准确的方法，它直接求解非定常的N-S方程，能够解决湍流中碳烟颗粒之间的小尺度相互作用，但这些方法都需要大量的计算费用。由于这个原因，其他计算成本相对较低的模型已经被广泛使用，例如大涡模拟(LES)。尽管大涡模拟被广泛用于湍流反应流动的模拟，但要实现烟尘颗粒、化学和湍流之间的小尺度相互作用的精确模拟仍然是一个艰巨的挑战。因此，本博士项目旨在解决大涡模拟碳烟形成和演化过程中遇到的三个问题，以开发一个改进的大涡模拟模型，以准确地预测模型燃气轮机燃烧室中的碳烟排放。1.在非结构网格上发展了一致的大涡模拟/概率密度函数(PDF)方法，通过求解描述火焰结构和气相前驱演化的标量的联合子滤波PDF方程和烟尘颗粒的数密度函数(NDF)矩，在非结构网格上发展了一致的大涡模拟/概率密度函数(PDF)方法来精确描述燃气轮机模型燃烧室中湍流、烟尘和化学之间的小尺度相互作用。2.为了研究分辨微分扩散对成核的影响，3.分析了碳烟特性对碳烟前体化学成分的敏感性，以及用于重建碳烟粒子NDF的矩量法(MOM)的选择。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技术实验产生的。DNSS将在湍流壁面喷流扩散火焰上运行，所获得的有价值的数据集将被用于训练基于卷积神经网络(CNN)的降阶模型来预测燃气轮机发动机的碳烟排放。其目的是将基于物理的模型(通过实现前面的目标而获得)与CNN模型相结合来开发基于CNN的混合物理模型，该模型能够在降低计算成本的情况下准确地预测碳烟排放。