利用FGM和PISO预测航空发动机燃烧室设计变化的排放

Megan F. Karalus, Dustin M. Brandt, L. McManus, E. Munktell
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摘要

在Simcenter STAR-CCM+版本2021.3中评估了一种快速预测一氧化碳(CO)和未燃烧碳氢化合物(UHC)排放的建模方法。在空转条件下,对航空发动机测试燃烧室的设计变化进行了幅度和趋势评估。采用大涡模拟(LES)方法,结合非迭代的PISO (split of Operators)压力隐式方法和火焰生成歧管(FGM)燃烧模型。同一试验燃烧室有四种几何设计变化,其中对穹顶射流冷却,主旋流器和下游孔进行了改变。本研究选择这四种几何形状是因为它们产生明显不同的CO和UHC排放,从而在评估建模方法的预测能力时减少了信噪比。给出了结果对液体燃料喷雾破碎模型中一个关键参数的敏感性。通过改变分解速率,CO排放的预测结果在量级和趋势上都与实验测量值非常吻合。四种设计中有三种的UHC排放量比较好。结果也产生了半隐式方法的压力链接方程(SIMPLE)方案;它们表现出与PISO相同的行为。然而,使用PISO的结果可以比传统方法快2.5到3.5倍。结合FGM和PISO的计算效率,可以快速准确地预测受管制的排放,从而在设计过程的早期减少设计的选择。
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Predicting Emissions Across Design Variations of an Aero-Engine Combustor Using FGM and PISO
A modeling approach for quickly predicting Carbon Monoxide (CO) and Unburned Hydrocarbon (UHC) emissions is assessed in Simcenter STAR-CCM+ version 2021.3. Both magnitude and trends are evaluated across design variations of an aero-engine test combustor operating at an idle condition. Large Eddy Simulation (LES) is used with the non-iterative Pressure Implicit by Splitting of Operators (PISO) scheme, and the Flamelet Generated Manifold (FGM) combustion model. There are four geometric design variations of the same test combustor where changes are made to the dome effusion cooling, main swirler, and downstream orifices. These four geometries are chosen for this study because they yield distinctly different CO and UHC emissions, thus reducing signal to noise in assessing the predictive capability of the modeling approach. Sensitivity of the results to a key parameter in the liquid fuel spray breakup model is provided. By varying the breakup rate, the prediction of the CO emissions is shown to compare very favorably both in magnitude and trend to the experimentally measured values. The UHC emissions are shown to compare well for three of the four designs. Results are also generated using the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) scheme; these show the same behavior as PISO. However, the results with PISO can be obtained between 2.5 and 3.5X faster than the more traditional approach. Combining the computational efficiency of FGM and PISO allows for fast and accurate predictions of regulated emissions, and therefore down-selection of designs earlier in the design process.
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