Development of procedures for reduced mechanism generation for prediction of particle formation in turbulent reacting flows

开发用于预测湍流反应流中颗粒形成的减少机制生成程序

• 批准号: EP/F036965/1
• 负责人: Terese Lovas
• 金额: $ 38.49万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF036965%2F1
• 关键词: Development; procedures; reduced; mechanism; generation

The tremendous progress in combustion science and technology in the past two decades would have been impossible without todays advances in computational resources. We are now able to model and simulate with growing confidence simplified combustion phenomena, using simple fuels for scientific studies, while engine manufacturers employ tailored combustion codes for their development work on a daily basis.Since combustion phenomena in practical devices are turbulent in nature, the challenge for predictive combustion is to describe complex combustion kinetics in a realistic environment. Hence, within the goal of achieving predictive reliability of turbulent combustion lies the requirement that the chemistry is described in an accurate and computationally adaptable manner. Also, the demanded accuracy of model predictions is extremely high if the concentrations of the pollutants are low, as found in the exhausts of modern, highly optimised devices, such as lean, premixed, pre-vaporised (LPP) gas turbines and in new concepts like the homogenous charge compression ignition (HCCI) engine. New challenges for predictive simulations are related to the yet unknown behaviour of alternative clean fuels, such as bio-fuels. However, alternative fuels are often very complex in nature, such as biomass fuels. An increasing number of renewable energy projects using biomass are under development, using waste products from agriculture and industry. Although overall green-house gas emissions are typically low for biomass fuels (carbon neutral fuels), concern need to be addressed regarding high concentrations of heavy metals and significant particle formation associated with such fuels. They are responsible for deposits and harmful products that may hinder the efficient, clean and durable run of these devices. It is crucial that the scientific community working with reduced reaction mechanisms urgently addresses the impact of particulate formation in combustion processes. When using computationally demanding simulation tools, reduced chemical schemes are necessary in order to achieve practical computing times. Indeed, detailed mechanisms for even moderately complex hydrocarbons (C7 and C8) contain hundreds of species reacting through thousands of reactions. Furthermore, hydrocarbon oxidation is also characterized by the formation of highly reactive radicals reacting on much smaller time scales than the major species. The resulting dramatic difference in time scales consequently result in stiffness in the system of differential equation (ODEs) governing the chemical evolution, which causes the simulations to progress only according to the shortest time steps. Hence, the proposed work addresses the urgent need for compact, yet comprehensible reaction kinetic models, also capable of predicting the precursors for particle formation.Harmful particulate emissions in urban areas largely originate from combustion in IC engines. The formation of the particles is located in fuel rich regions during non-premixed combustion such as in diesel engines, or in small fuel rich pockets due to inhomogeneities during combustion e.g. in petrol engines, in particular run in direct injection mode. As most practical combustion processes involve the turbulent mixing of gases, the interactions between the turbulent flow field and the chemical processes are important. The formulated reduced model will in turn be used to study the effect of preferential diffusion under moderate turbulent mixing, conditions under which mixing and chemistry can not be decoupled, and typical conditions at the end of the combustion phase of in diesel engines when most of particulate emissions are formed. This important new understanding of the effect of preferential diffusion on particle formation under the influence of turbulence will in turn represent the basis of further model development towards implementation into commercial codes for realistic engine simulation.

如果没有今天计算资源的进步，燃烧科学和技术在过去二十年中的巨大进步是不可能的。现在，我们可以使用简单的燃料对简化的燃烧现象进行建模和仿真，并将其用于科学研究，而发动机制造商则可以使用定制的燃烧代码进行日常开发工作。由于实际设备中的燃烧现象本质上是湍流的，因此预测燃烧的挑战是在现实环境中描述复杂的燃烧动力学。因此，在实现湍流燃烧的预测可靠性的目标内，要求以准确和计算上可适应的方式描述化学。此外，如果污染物的浓度低，则模型预测的所需精度极高，如在现代高度优化的装置（例如，贫、预混合、预蒸发（LPP）燃气涡轮机）的排气中以及在新概念（例如，均质充量压缩点火（HCCI）发动机）中所发现的。预测模拟的新挑战与替代清洁燃料（如生物燃料）的未知行为有关。然而，替代燃料在性质上往往非常复杂，例如生物质燃料。正在开发越来越多的利用生物量的可再生能源项目，这些项目利用农业和工业的废物。虽然生物质燃料（碳中性燃料）的温室气体总排放量通常较低，但需要解决与此类燃料相关的高浓度重金属和大量颗粒形成问题。他们负责沉积物和有害产品，可能会阻碍这些设备的高效，清洁和耐用运行。至关重要的是，科学界必须致力于减少反应机制，紧急解决燃烧过程中颗粒形成的影响。当使用计算要求很高的模拟工具时，为了达到实际的计算时间，减少化学方案是必要的。事实上，即使是中等复杂的碳氢化合物（C7和C8）的详细机制也包含数百种通过数千种反应进行反应的物质。此外，烃氧化的特征还在于在比主要物质小得多的时间尺度上反应形成高反应性自由基。由此产生的时间尺度上的巨大差异因此导致控制化学演化的微分方程（ODE）系统中的刚度，这导致模拟仅根据最短的时间步长进行。因此，拟议的工作解决了迫切需要紧凑，但易于理解的反应动力学模型，也能够预测的前体颗粒formation.Harmful颗粒排放在城市地区主要来自内燃机燃烧。颗粒的形成位于非预混燃烧期间的富燃料区域中，例如在柴油发动机中，或者由于燃烧期间的不均匀性而位于小的富燃料袋中，例如在汽油发动机中，特别是在直接喷射模式下运行。由于大多数实际燃烧过程涉及气体的湍流混合，湍流流场和化学过程之间的相互作用是重要的。所制定的简化模型将反过来被用来研究在中等湍流混合下的优先扩散的效果，在这种条件下，混合和化学不能解耦，以及在柴油机中的燃烧阶段结束时的典型条件下，大多数颗粒排放物形成。这一重要的新的理解的影响下，优先扩散的颗粒形成的湍流的影响下，将反过来代表进一步的模型开发的基础上实现到商业代码的现实发动机模拟。