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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665902
• 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.

燃气涡轮机发动机是航空推进的事实上的动力源，并且在发电中也发挥越来越大的作用。燃气涡轮机发动机的研发驱动因素包括减少污染物排放（主要是NOx、CO和颗粒物）和提高可持续性/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抑制因素可以总结如下：*1）对于在燃气涡轮机发动机中发生的相当极端的湍流条件下通过湍流/燃烧相互作用实现的将反应物转化为产物的潜在轨迹（即，化学能转化途径），基本上缺乏理解。虽然传统的模型规定了层流火焰中发现的相同轨迹，但最近的实验和计算证据对此提出了质疑。这种非层流轨迹对燃烧室的设计有着重要的意义，并为新技术的发展提供了可能性。2)用于实现上述目标的所有当前已知的方法增加了发生各种形式的非稳态燃烧动态的可能性。特别关注的现象是热声不稳定性、井喷、回火和自燃，其中任何一种都可能使系统无法操作或造成（潜在的灾难性）损坏。由于机械理解的差距和缺乏预测框架，目前无法预测设计或操作变化如何影响这些现象发生的概率。* 拟议的研究通过两个并行的研究主题解决这些问题。首先，我们将进行独特的实验，揭示在实际相关的条件下湍流反应流的微观动力学。具体来说，我们将使用激光测量技术来描述流体从反应物转化为产物时的温度、成分和反应速率的无数轨迹。* 第二个主题从更大规模的角度看待燃烧室动力学，目的是提供一种基于物理基础的降阶方法，用于根据工程师在现实大规模系统上获得的数据预测非平稳现象。为此，我们首先将使用激光诊断来发现和解释我们假设驱动不同形式的非稳态行为的各种反馈机制。这些机理将在低维空间中表示，这些空间最好地捕捉燃烧室的临界扰动/响应特性，但可以在真实的发动机研制程序中得到。然后，我们将生成和测试各种度量，这些度量描述了这种行为如何随着动态概率的增加而变化，从而形成一个预测框架。