Multi-Objective Experimental Combustor Development Using Surrogate Model-Based Optimization

IF 1.4 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Johann Moritz Reumschüssel, Jakob G.R. von Saldern, Bernhard Cosic, Christian Oliver Paschereit
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引用次数: 0

Abstract

Abstract The majority of premixed industrial gas turbine combustion systems feature two or more separately controlled fuel lines. Every additional fuel line improves the operational flexibility but increases the complexity of the system. When designing such a system, the goals are low emissions of various pollutants and avoiding lean blowout or extinction. Typically, these limitations become critical under different load conditions of the machines. Therefore, it is particularly challenging to develop combustors for stable and clean combustion over a wide operating range. In this study, we apply the Gaussian process regression machine learning method for application to burner development, with the aim of improving the process, which is often driven by a trial-and-error approach. To do so, a special pilot unit is installed into a full-scale industrial swirl combustor. The pilot features 61 positions of fuel injection, each of which is equipped with an individual valve, allowing to modify the fuel–air mixture close to the flame root in various degrees. In fully automatized atmospheric tests, we use the pilot system to train two surrogate models for different design objectives of the combustor, relevant for full load and part load operation, respectively. Once trained, the models allow for prediction for any possible injection scheme. In combination, they can be used to identify pilot injector configurations with an improved operation range in terms of low NOx emissions and part load stability. The adopted multimodel approach enables combustor design specifically for high operational flexibility of gas turbines, but can also be extended to other similar industrial development processes.
基于代理模型优化的多目标实验燃烧室研制
大多数预混工业燃气轮机燃烧系统具有两个或多个单独控制的燃料管。每增加一根燃料管,操作的灵活性就会提高,但也会增加系统的复杂性。在设计这样一个系统时,目标是降低各种污染物的排放,避免精益井喷或灭绝。通常,这些限制在机器的不同负载条件下变得至关重要。因此,开发在大范围内稳定清洁燃烧的燃烧器是一项特别具有挑战性的工作。在本研究中,我们将高斯过程回归机器学习方法应用于燃烧器开发,目的是改进通常由试错方法驱动的过程。为此,一个特殊的先导装置被安装到一个全尺寸的工业涡流燃烧器中。飞行员有61个燃油喷射位置,每个位置都配备一个单独的阀门,允许在不同程度上修改靠近火焰根部的燃料-空气混合物。在完全自动化的大气试验中,我们使用先导系统分别针对燃烧室的不同设计目标训练两个代理模型,分别与满载和部分负荷运行相关。经过训练后,这些模型可以预测任何可能的注入方案。结合使用,它们可以用于确定中试喷油器配置,从而在低氮氧化物排放和部分负载稳定性方面提高工作范围。所采用的多模型方法使燃烧室设计专门针对燃气轮机的高操作灵活性,但也可以扩展到其他类似的工业开发过程。
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来源期刊
CiteScore
3.80
自引率
20.00%
发文量
292
审稿时长
2.0 months
期刊介绍: The ASME Journal of Engineering for Gas Turbines and Power publishes archival-quality papers in the areas of gas and steam turbine technology, nuclear engineering, internal combustion engines, and fossil power generation. It covers a broad spectrum of practical topics of interest to industry. Subject areas covered include: thermodynamics; fluid mechanics; heat transfer; and modeling; propulsion and power generation components and systems; combustion, fuels, and emissions; nuclear reactor systems and components; thermal hydraulics; heat exchangers; nuclear fuel technology and waste management; I. C. engines for marine, rail, and power generation; steam and hydro power generation; advanced cycles for fossil energy generation; pollution control and environmental effects.
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