Understanding Soot Formation from Combustion with Numerical Modelling

大气污染和与之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。燃烧排放的颗粒物(通常称为烟尘或黑碳)是一种特别有害的污染物，是气候变化的关键因素，并导致不利的呼吸道健康状况。这项研究计划旨在通过燃烧系统的详细计算模型、碳烟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碳烟排放问题。 虽然较大的烟尘颗粒在空气中或在地球表面稳定下来后会吸收太阳辐射，但较小的颗粒会被呼吸并通过肺部吸收到血液中。为了遵守与颗粒大小有关的预期排放法规，需要推进对这一领域的基本理解。因此，本研究将致力于从目前的半经验初始模型和定量浓度预测基准出发，改进碳烟形成模拟的最新水平。研究计划将寻求准确地模拟颗粒成核阶段，并预测颗粒的大小和形状。本研究将从分析和处理实验趋势入手，开发新的基本模型。它还将考虑压力升高和运输燃料。 另一项研究重点将集中在开发计算工具，以预测发动机的烟尘排放。发动机建模突破了计算能力的极限，现有的碳烟模型要么不准确，要么过于繁琐，无法可靠地使用。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颗粒物理学的了解，设计出创新的方法，以廉价地估计浓度，并在以后更详细地估计颗粒特征，如大小和形状。这项研究将对工业产生巨大的潜在影响，因为它将导致新设计的先验排放预测。 这项研究计划将继续为学术界和工业界创造关于粒子形成机制的新知识，并将新开发的高性能计算工具分发给其他研究人员以供推广使用。烟尘颗粒排放理解和预测能力的进步将改善公众健康，减少对环境的影响。这项研究将继续加强我们的行业、知识基础和加拿大在清洁能源未来中的作用。

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.