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.

我感兴趣的项目已发布广告，其标题为“发动机污染物排放的基于物理和数据驱动的建模”。该项目涉及对燃气涡轮发动机的烟灰颗粒排放进行建模。烟灰是燃气涡轮发动机产生的主要污染物，因此烟灰建模和预测的能力对于下一代低排放燃气轮机和内燃机 (IC) 发动机的开发至关重要。由于规模较小，烟灰排放建模是一个特别具有挑战性的问题湍流、粒子动力学和化学之间的相互作用。为了研究燃气涡轮发动机中的烟灰颗粒演化，需要四个不同的组成部分：背景湍流模型、气相燃烧模型、通过各种微观过程（如开始、生长和氧化）影响烟灰颗粒的物理化学机制模型模拟烟尘排放的最准确方法是通过直接数值模拟 (DNS)，它直接求解非定常纳维-斯托克斯方程，并且能够解决烟尘颗粒在湍流，但这些解决方案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被广泛使用，例如大涡模拟（LES）。尽管 LES 被广泛用于模拟湍流反应流，但要实现烟灰颗粒、化学物质和湍流之间小尺度相互作用的精确建模仍然是一个艰巨的挑战。因此，该博士项目旨在解决 LES 在模拟烟灰形成和演化时遇到的三个问题，以便开发增强的 LES 模型来准确预测模型燃气轮机燃烧器中的烟灰排放。下面列出了要解决的三个主要问题。 1. 在非结构化网格上开发一致的 LES/概率密度函数 (PDF) 方法，通过求解联合子模型来准确表征燃气轮机模型燃烧器中湍流、烟灰和化学物质之间的小尺度相互作用。过滤标量的 PDF 方程，用于描述火焰结构和气相前体演化以及烟灰颗粒的数密度函数 (NDF) 矩2。将单个物种的分子扩散率纳入 PDF求解器来研究解析微分扩散对烟灰颗粒成核、生长和氧化的影响。3.评估烟灰特性对烟灰前体化学以及用于重建 NDF 的矩方法 (MOM) 的选择的敏感性新的增强型 LES/PDF-MOM 模型将用于模拟德国 DLR 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。DNS 将在湍流壁射流扩散火焰上运行，获得的有价值的数据集将用于训练基于卷积神经网络 (CNN) 的降阶模型来预测燃气涡轮发动机的烟灰排放。目的是将基于物理的模型（通过实现先前的目标获得）和 CNN 模型相结合，开发一种 CNN 辅助的基于混合物理的模型，该模型能够以较低的计算成本准确预测烟尘排放。