Towards Reduced Order Models of Small-Scale Acoustically Significant Components in Gas Turbine Combustion Chambers

S. Kowshik, S. Sridhar, N. Treleaven
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Abstract

Gas turbine combustion chambers contain numerous small-scale features that help to dampen acoustic waves and alter the acoustic mode shapes. This damping helps to alleviate problems such as thermoacoustic instabilities. During computational fluid dynamics simulations (CFD) of combustion chambers, these small-scale features are often neglected as the corresponding increase in the mesh cell count augments significantly the cost of simulation while the small physical size of these cells can present problems for the stability of the solver. In problems where acoustics are prevalent and critical to the validity of the simulation, the neglected small-scale features and the associated reduction in overall acoustic damping can cause problems with spurious, non-physical noise and prevents accurate simulation of transients and limit cycle oscillations. Low-order dynamical systems (LODS) and artificial neural networks (ANNs) are proposed and tested in their ability to represent a simple two-dimensional acoustically forced simulation of an orifice at multiple frequencies. These models were built using compressible CFD, using OpenFOAM, of an orifice placed between two ducts. The acoustic impedance of the orifice has been computed using the multi-microphone method and compared to a commonly used analytical model. Following this, the flow field downstream of the orifice has been modelled using both a LODS and ANN model. Both methods have shown the ability to closely represent the simulated dynamical flows at much lower computational cost than the original CFD simulation. This work opens the possibility of models that can dynamically predict the flow through, for instance, acoustic liners, dilution ports and fuel injectors in real engines during thermoacoustic instabilities without having to mesh and simulate these small-scale features directly. Such models may also assist in the accurate simulation of flame quenching due to cooling flows or the design of effusion cooled aerodynamic surfaces such as nozzle guide vanes (NGVs) and turbine blades.
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燃气轮机燃烧室小型声学重要部件的降阶模型研究
燃气轮机燃烧室包含许多有助于抑制声波和改变声模态形状的小尺度特征。这种阻尼有助于缓解热声不稳定性等问题。在燃烧室计算流体动力学模拟(CFD)过程中,这些小尺度特征往往被忽略,因为相应的网格数量增加会显著增加模拟成本,而这些网格的小物理尺寸可能会给求解器的稳定性带来问题。在声学普遍存在且对模拟有效性至关重要的问题中,被忽视的小尺度特征和相关的总体声阻尼降低可能导致虚假、非物理噪声问题,并妨碍瞬态和极限环振荡的准确模拟。提出了低阶动力系统(LODS)和人工神经网络(ann),并测试了它们在多个频率下表示孔板简单二维声强迫模拟的能力。这些模型使用可压缩CFD,使用OpenFOAM,在两个管道之间放置一个孔板。用多传声器法计算了孔板的声阻抗,并与常用的解析模型进行了比较。在此之后,使用LODS和ANN模型对孔板下游的流场进行了建模。这两种方法都能较好地反映模拟的动态流动,且计算成本比原始CFD模拟低得多。这项工作开启了动态预测模型的可能性,例如,在热声不稳定期间,真实发动机中的声学衬垫、稀释端口和燃油喷射器的流动,而无需直接网格化和模拟这些小尺度特征。这些模型还可以帮助精确模拟由于冷却流引起的火焰淬火,或设计射流冷却的气动表面,如喷嘴导叶(ngv)和涡轮叶片。
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