Novel Combustion for Aerospace and Power Generation: Multi-Scale Combustion Dynamics

用于航空航天和发电的新型燃烧：多尺度燃烧动力学

• 批准号: RGPIN-2017-06501
• 负责人: Steinberg, Adam
• 金额: $ 3.21万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=688799
• 关键词: Novel; Combustion; Aerospace; Power; Generation

Gas turbine engines are the de facto power source for aeronautical propulsion, and also play an increasingly large role in electrical power generation. Research and development drivers for gas turbine engines include reducing pollutant emissions (primarily NOx,, CO, and particulates) and increasing sustainability/decreasing climate impact (through use of biofuels), while maintaining safety, robustness, and costs. Two of the major inhibitors to achieving these goals can be summarized as follows:***1) There is a fundamental lack of understanding regarding the potential trajectories for converting reactants to products (i.e. chemical energy conversion pathways) that are enabled by turbulence/combustion interactions at the rather extreme turbulence conditions occurring in gas turbine engines. While traditional models prescribe the same trajectories found in laminar flames, recent experimental and computational evidence disputes this. Such non-laminar trajectories have major implications for how combustors are designed, and open possibilities for novel technologies.***2) All currently known methods for achieving the aforementioned goals increase the probability of various forms of non-stationary combustion dynamics occurring. Phenomena of particular concern are thermoacoustic instabilities, blowout, flashback, and autoignition, any of which can render a system inoperable or cause (potentially catastrophic) damage. It currently is not possible to predict how design or operational changes influence the probability of these phenomena occurring due to gaps in mechanistic understanding and lack of a predictive framework. ***The proposed research addresses these issues through two concurrent research themes. Firstly, we will conduct unique experiments that unravel the micro-scale dynamics of turbulent reacting flows at conditions of practical relevance. Specifically, we will use laser measurement techniques to describe the myriad of trajectories through temperature, composition, and reaction rate that fluid can take as it converts from reactants to products. ***The second theme takes a larger scale view of combustor dynamics, with the objective of providing a physically-grounded reduced-order method for predicting non-stationary phenomena based on data that is obtainable by engineers working on realistic large-scale systems. To do so, we first will use laser diagnostics to discover and explain the various feedback mechanisms that we hypothesize to drive the different forms of non-stationary behavior. These mechanisms will then be expressed in low-dimensional spaces that best capture the critical perturbation/response behavior of the combustor, but could be accessed in real engine development programs. We then will generate and test various metrics that describe how this behavior changes as the probability of dynamics increases, leading to a predictive framework.

燃气涡轮发动机实际上是航空推进的动力源，在发电中也发挥着越来越大的作用。燃气涡轮发动机的研发驱动因素包括减少污染物排放（主要是氮氧化物、一氧化碳和颗粒物），提高可持续性/减少气候影响（通过使用生物燃料），同时保持安全性、稳健性和成本。实现这些目标的两个主要障碍可以总结如下：***1)对于在燃气涡轮发动机中发生的相当极端的湍流条件下由湍流/燃烧相互作用实现的将反应物转化为产物的潜在轨迹（即化学能转换途径），从根本上缺乏理解。虽然传统模型给出了与层流火焰相同的轨迹，但最近的实验和计算证据却反驳了这一点。这种非层流轨迹对如何设计燃烧器具有重要意义，并为新技术开辟了可能性。***2)目前已知的实现上述目标的所有方法都增加了各种形式的非平稳燃烧动力学发生的可能性。特别值得关注的现象是热声不稳定性、井喷、闪回和自燃，其中任何一种都可能导致系统无法运行或造成（潜在的灾难性）损坏。目前还不可能预测设计或操作变化如何影响这些现象发生的可能性，这是由于在机制理解上的差距和缺乏预测框架。***拟议的研究通过两个并行的研究主题来解决这些问题。首先，我们将进行独特的实验，揭示在实际相关条件下湍流反应流动的微观尺度动力学。具体来说，我们将使用激光测量技术来描述流体从反应物到产物转化过程中通过温度、成分和反应速率产生的无数轨迹。***第二个主题采用了燃烧室动力学的更大尺度视图，目的是提供一种基于物理基础的降阶方法，用于预测基于实际大型系统的工程师可获得的数据的非平稳现象。为此，我们首先将使用激光诊断来发现和解释我们假设的驱动不同形式的非平稳行为的各种反馈机制。然后，这些机制将在低维空间中表达，以最好地捕捉燃烧室的临界扰动/响应行为，但可以在实际的发动机开发计划中访问。然后，我们将生成和测试各种指标，描述这种行为如何随着动态概率的增加而变化，从而产生预测框架。