Abstract Optimization of efficiency and pollution control for gaseous species and particulate matter are common to any combustion system. Combustor lean blowout (LBO) is a concern for aircraft safety and for land-based gas turbines designed to operate at lean equivalence ratios to achieve better fuel efficiency and to limit NOx emissions. This paper provides an experimental demonstration of model-based control applied to a laboratory jet-stirred reactor (JSR) approaching LBO. The approach uses (1) combustor temperature measurements, coupled with (2) the calculation of free radical concentrations in the reactor using a real-time chemical reactor network (RT-CRN) model as the reactor approaches LBO, which in turn (3) are used by a predictive control algorithm to achieve stable combustion. The RT-CRN represents the combustor as three perfectly stirred reactors (PSRs) in series with a recirculation pathway; the model inputs include real-time measurements of temperature and mass flow rates of fuel and air. In a series of experiments, the combustor is operated on a premixed methane-air mixture; after achieving stable combustion, the air flow rate is increased beyond the stable air-fuel ratio either as a step function or by ramping up linearly. The predictive RT-CRN control algorithm calculates the distribution of hydroxyl (OH) radicals in the free jet, impinging jet, and recirculation regions of the JSR in near real-time (∼1 s delay), and determines the leanest stable state based on the OH uniformity in the combustor. As the OH shifts towards the recirculation region, the reactor approaches LBO, if this condition is detected the control algorithm injects additional fuel; reactor stabilization is achieved within a 5–15 s time frame. Although this proof-of-concept demonstration is performed for LBO control in a JSR with ceramic walls, the control methodology is applicable to other types of high-intensity recirculation stabilized combustors.

中文翻译：

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使用预测性化学反应器网络控制防止稀薄火焰爆裂

摘要 气态物质和颗粒物的效率优化和污染控制对于任何燃烧系统都是常见的。燃烧室贫燃 (LBO) 是飞机安全和陆基燃气轮机的一个关注点，这些燃气轮机旨在以贫油当量比运行，以实现更好的燃料效率并限制 NOx 排放。本文提供了应用于接近 LBO 的实验室喷射搅拌反应堆 (JSR) 的基于模型的控制的实验演示。该方法使用 (1) 燃烧器温度测量，以及 (2) 在反应器接近 LBO 时使用实时化学反应器网络 (RT-CRN) 模型计算反应器中的自由基浓度，进而 (3)由预测控制算法使用，以实现稳定燃烧。RT-CRN 将燃烧室表示为三个串联的完美搅拌反应器 (PSR)，并带有一个再循环路径；模型输入包括燃料和空气的温度和质量流量的实时测量值。在一系列实验中，燃烧器在预混的甲烷-空气混合物上运行；在实现稳定燃烧后，空气流量增加超过稳定空燃比作为阶跃函数或线性增加。预测性 RT-CRN 控制算法近乎实时地（约 1 秒延迟）计算 JSR 的自由射流、撞击射流和再循环区域中羟基 (OH) 自由基的分布，并基于燃烧器中 OH 的均匀性。随着 OH 向再循环区域移动，反应器接近 LBO，如果检测到这种情况，控制算法会喷射额外的燃料；反应堆稳定是在 5-15 秒的时间范围内实现的。尽管此概念验证演示是针对具有陶瓷壁的 JSR 中的 LBO 控制进行的，但该控制方法适用于其他类型的高强度再循环稳定燃烧器。