Application of a Stochastic Reactor Model Approach for Prediction of Gas Turbine Engine Emissions

应用随机反应器模型方法预测燃气轮机排放

• 批准号: 543735-2019
• 负责人: Eaves, Nickolas
• 金额: $ 1.82万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680274
• 关键词: Application; Stochastic; Reactor; Model; Approach

Siemens Canada Limited (SCL) is a manufacturer of industrial aero-derivative gas turbines for power generation. Given ever increasing effort by SCL to develop combustion that meet the most stringent emissions standards, there is a need to continually improve how new innovative low emission concepts can be design and brought to market rapidly. At the early stages of the design process, simplified models are utilized to understand global implications on emission levels of any alterations. This reliance on simplified modeling reduces the cost versus complex models or experimentation; however, there are large degrees of uncertainty in these simplified models. Understanding the uncertainty would allow for better decisions as to what designs are pursued further in the development process, avoiding costs increased from longer than expected development time. One of the largest sources of uncertainties in the simplified models is the inability to capture turbulence-chemistry interaction (TCI) with adequate fidelity to ensure reliable analysis. Developing of a computationally-efficient simplified modeling technique for gas turbines that can capture TCI will reduce the cost of and time required for developing new engine designs, enhancing the competitiveness of SCL. Currently, such simplified modeling techniques for gas turbines have not achieve the same level of maturity as the high end commercially available simulation tools like CFD. The proposed research will apply an existing Stochastic Reactor Model (SRM) software to modeling gas turbine emissions to address SCL emissions modeling challenges. The project is focused on a "proof of concept", demonstrating that a SRM approach is not only feasible for modeling gas turbine emissions, but also provide a robust and cost effective approach. The SRM approach is unique in that it can capture TCI while having low computational costs.The major milestones and deliverables are: (1) code modifications to apply the SRM approach to gas turbines, (2) determination of initial model parameters for gas turbine modeling, (3) initial proof of concept simulation of gas turbine emissions using the SRM approach, (4) assessment of the SRM approach using experimental data, and (5) a report on the findings of the proof of concept study.

加拿大西门子有限公司（SCL）是用于发电的工业航空衍生燃气轮机的制造商。鉴于SCL不断努力开发符合最严格的排放标准的燃烧，因此有必要不断提高新的创新低排放概念如何设计并迅速推向市场。在设计过程的早期阶段，简化的模型被用来了解对任何变化的排放水平的全球影响。对简化建模的这种依赖会降低成本与复杂模型或实验。但是，这些简化模型中存在很大程度的不确定性。了解不确定性将使关于在开发过程中进一步追求的设计做出更好的决策，从而避免成本比预期的开发时间更长。简化模型中最大的不确定性来源之一是无法捕获具有足够忠诚度的湍流化学相互作用（TCI）以确保可靠的分析。开发一种可以捕获TCI的燃气轮机的计算效率简化建模技术将减少开发新发动机设计所需的成本和时间，从而增强SCL的竞争力。当前，这种简化的燃气轮机建模技术尚未达到与CFD等高端市售模拟工具相同的成熟度。拟议的研究将应用现有的随机反应器模型（SRM）软件来建模燃气轮机排放以解决SCL排放建模挑战。该项目的重点是“概念验证”，表明SRM方法不仅对于对燃气轮机排放进行建模是可行的，而且还提供了一种强大且具有成本效益的方法。 SRM方法的独特之处在于它可以捕获TCI，而计算成本较低。主要的里程碑和可交付成果是：（1）将SRM方法应用于燃气轮机的代码修改，（2）确定燃气涡轮机建模的初始模型参数，（3）使用SRM的概念模拟方法的初步概念验证，使用SRM的概念模拟方法，（4）SRM INSERTIONS（4）SRM（4）SRM（4）SRM（4）（4）概念研究证明。