High-repetition-rate laser diagnostics experiments for prediction and control of thermo-acoustic instabilities in low-emission gas turbine engines

本研究的重点是预测和控制低排放、燃料柔性燃气涡轮发动机热声不稳定性的方法。燃气轮机在绝大多数商用飞机上使用，并且越来越多地被用于地面发电。因此，为了减少航空旅行和能源生产对环境的影响，有必要开发未来的发动机，使污染物排放最小化，并使用各种可持续的替代燃料安全运行。不幸的是，低排放的火焰非常不稳定。在燃气轮机燃烧室中，这往往导致高振幅燃烧和压力振荡的自激，或热声不稳定，从而降低效率，增加排放，关闭发动机，甚至造成灾难性故障。这些不稳定性的激发是由复杂的流动-燃烧-声耦合引起的，目前还无法预测或鲁棒控制。

This research focuses on methods to predict and control thermo-acoustic instabilities in low-emission, fuel-flexible gas turbine engines. Gas turbines are used on the vast majority of commercial aircraft and are increasingly being employed for terrestrial power generation. In order to reduce the environmental impact of air travel and energy production, it therefore is necessary to develop future engines that minimize pollutant emissions and safely operate with a variety of sustainable alternative fuels. Unfortunately, flames that produce low emissions are very unstable. In gas turbine combustors, this often leads to the self-excitation of high-amplitude combustion and pressure oscillations, or thermo-acoustic instabilities, which can reduce efficiency, increase emissions, shutdown the engine, and even cause catastrophic failure. Excitation of these instabilities is caused by a complex flow-combustion-acoustic coupling that currently cannot be predicted or robustly controlled. In this research, state-of-the-art high-repetition-rate laser diagnostics will be used to quantify the mechanisms driving these instabilities. Model gas turbine combustors, derived from industrial designs, will be employed in various configurations and operated with both conventional and alternative fuels. Measurements will be made of the velocity field and flame topography inside the combustors at a rate of 10 kHz (1000x the rate of conventional diagnostics) using a newly acquired laser and imaging system. Thermo-acoustic instabilities typically occur at frequencies of approximately 0.1-1 kHz, and these new measurement capabilities therefore can resolve the relevant dynamics. The resultant data will provide maps of where and why energy is transferred to oscillations, and how the energy transfer depends on the geometric and operational parameters of the engine. This will greatly enhance our ability to predict and control oscillations. Furthermore, it will facilitate the training of exceptionally qualified experimental researchers, who are able to conduct the complex experiments needed to solve modern engineering challenges.