Understanding Soot Formation from Combustion with Numerical Modelling

通过数值模拟了解燃烧过程中烟灰的形成

• 批准号: RGPIN-2019-04628
• 负责人: Dworkin, Seth
• 金额: $ 3.35万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715078
• 关键词: Understanding; Soot; Formation; Combustion; Numerical

Atmospheric pollution and the associated climate change are responsible for a myriad of detrimental environmental, ecological, health, and socio-economic effects. One particularly hazardous pollutant, particulate matter emitted from combustion (commonly referred to as soot' or black carbon') is a key contributor to climate change and leads to adverse respiratory health conditions. This research program seeks to address atmospheric soot emissions through detailed computational modelling of combustion systems, fundamental studies of soot formation, and the development of new algorithms to predict engine emissions. Whereas larger soot particles absorb solar radiation either while in the air or once they have settled on the Earth's surface, smaller particles are respired and absorbed through the lungs into the bloodstream. To comply with anticipated emissions regulations pertaining to particle size, fundamental understanding in this area needs to advance. Therefore, this research will focus on improving the state-of-the-art in soot formation modelling from the current benchmarks of semi-empirical inception models and quantitative concentration prediction. Research initiatives will seek to accurately model the particle nucleation phase, and predict size and shape. This research will proceed by analyzing and processing experimental trends to develop new fundamental models. It will also consider elevated pressures and transportation fuels. Another research thrust will focus on developing computational tools to predict soot emissions from engines. Engine modelling pushes the limits of computational capability and existing soot models are either inaccurate or too cumbersome for reliable use. Canadian industry currently suffers from a lack of accurate predictive capability in the prototype design or modification processes. Such a capability could reduce the need for expensive prototype fabrication and testing and help facilitate cleaner engine design. This research will seek to leverage our understanding of soot particle physics to devise innovative methods to inexpensively estimate concentrations, and later, more detailed particle characteristics such as size and shape. This research has a dramatic potential impact on industry as it will lead to a priori emissions predictions from new designs. This research program will continue to generate new knowledge for academia and industry on the mechanisms of particle formation, and distribute the newly developed high-performance computing tools to other researchers for expanded use. Advancements in soot particle emissions understanding and predictive capability will lead to improved public health and reduced environmental impact. This research will continue to strengthen our industry, knowledge base, and Canada's role in a cleaner energy future.

大气污染和相关的气候变化造成了无数有害的环境、生态、健康和社会经济影响。一种特别危险的污染物，即燃烧排放的颗粒物质（通常称为煤烟或炭黑）是气候变化的主要因素，并导致不利的呼吸健康状况。该研究计划旨在通过燃烧系统的详细计算建模，烟尘形成的基础研究以及预测发动机排放的新算法的开发来解决大气烟尘排放问题。 较大的烟尘颗粒在空气中或在地球表面沉降后吸收太阳辐射，而较小的颗粒则通过肺部呼吸和吸收进入血液。为了符合与颗粒尺寸有关的预期排放法规，需要推进对该领域的基本理解。因此，本研究将侧重于改善国家的最先进的烟尘形成建模从目前的基准半经验的初始模型和定量浓度预测。研究计划将寻求精确地模拟颗粒成核阶段，并预测尺寸和形状。 本研究将从分析和处理实验趋势着手，开发新的基础模型。它还将考虑高压和运输燃料。 另一个研究重点将集中在开发计算工具来预测发动机的碳烟排放。发动机建模推动了计算能力的极限，现有的碳烟模型要么不准确，要么过于繁琐，无法可靠使用。加拿大工业目前在原型设计或修改过程中缺乏准确的预测能力。这种能力可以减少昂贵的原型制造和测试的需要，并有助于促进更清洁的发动机设计。这项研究将寻求利用我们对烟尘粒子物理学的理解来设计创新的方法，以廉价的方式估计浓度，以及更详细的粒子特征，如大小和形状。这项研究对工业具有巨大的潜在影响，因为它将导致新设计的先验排放预测。 该研究计划将继续为学术界和工业界提供有关颗粒形成机制的新知识，并将新开发的高性能计算工具分发给其他研究人员以供扩大使用。对烟尘颗粒排放的理解和预测能力的提高将改善公众健康，减少对环境的影响。这项研究将继续加强我们的工业，知识基础和加拿大在清洁能源未来的作用。