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.

我感兴趣的项目被广告，其标题为“基于物理和数据驱动的引擎排放剂的建模”。该项目涉及对燃气轮机发动机的烟灰颗粒排放进行建模。烟灰是由燃气轮机发动机产生的主要污染物，因此对烟灰进行建模和预测的能力对于下一代低排放燃气轮机和内部燃烧（IC）发动机的发展至关重要。修改烟灰烟灰排放一个特别具有挑战性的问题，由于其小规模相互作用之间的小规模相互作用，粒子动力学和化学性质。要研究燃气轮机发动机中的烟灰颗粒进化，它需要四个不同的组成部分：背景湍流模型，气相燃烧模型，用于影响各种微型过程的物理化学机制的模型，例如，粒子进化动力学的最准确的方式（直接启用了（通过）数字（直接）（直接启用）（direction simienty simienty n dimection simient simient simienty simienty n dimection n dimection） Navier-Stokes方程，能够解决湍流中烟灰颗粒的小规模相互作用，但这些解决方案具有大量的计算费用。由于这个原因，其他相对较少的计算昂贵模型已被广泛使用，例如大型涡流模拟（LES）。即使LE被广泛用于建模湍流反应流，但要实现烟灰颗粒，化学和湍流之间的小规模相互作用的准确建模仍然是一个巨大的挑战。因此，该博士学位项目旨在解决LES在建模烟灰组成和演化时遇到的三个问题，以开发增强的LES模型，以准确预测模型燃气涡轮机燃烧器中的烟灰发射。 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烟灰颗粒的（NDF）2。在PDF求解器中纳入分子分子扩散，以研究解决方案的差异扩散对烟灰颗粒成核，生长和氧化的影响。3。遵守烟灰特征对烟灰化学的敏感性，以使烟灰化学的敏感性以及对烟灰化的选择（启用）新的方法（MOM）。 LES/PDF-MOM模型将用于模拟DLR德国开发的模型燃气轮机燃烧器。 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.目的是结合基于物理的模型（从实现先前的目标获得）和CNN模型，以开发CNN辅助混合物理学模型，该模型能够以降低的计算成本准确预测烟灰发射。