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Uncertainty Quantification of Aeroacoustic Power Sources in Corrugated Pipes

机译:波纹管中气动声源的不确定度量化

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Gas transport in corrugated pipes often exhibit whistling behavior, due to periodic flow-induced pulsations generated in the pipe cavities. These aero-acoustic sources are strongly dependent on the geometrical dimensions and features of the cavities. As a result, uncertainties in the exact shape and geometry play a significant role in determining the singing behavior of corrugated pipes. While predictive modelling for idealized periodic structures is well established, this paper focusses on the sensitivity analysis and uncertainty quantification (UQ) of uncertain geometrical parameters using probabilistic models. The two most influential geometrical parameters varied within this study are the cavity width and downstream edge radius. Computational Fluid Dynamics (CFD) analysis was used to characterize the acoustic source. Stochastic collocation method was used for propagation of input parameter uncertainties. The analysis was performed with both full tensor product grid and sparse grid based on level-2 Clenshaw-Curtis points. The results show that uncertainties in the width and downstream edge radius of the cavity have an effect on the acoustic source power, peak Strouhal number and consequently the whistling onset velocity. Based on the assumed input parameters distribution functions, the confidence levels for the prediction of onset velocity were calculated. Finally, the results show the importance of performing uncertainty analysis to get more insights in the source of errors and consequently leading to a more robust design or risk-management oriented decision.
机译:波纹管中的气体运输通常表现出吹口哨的行为,这是由于在管腔中产生周期性的流动引起的脉动。这些空气声源在很大程度上取决于腔的几何尺寸和特征。结果,确切形状和几何形状的不确定性在确定波纹管的起皱行为中起着重要作用。虽然理想的周期性结构的预测模型已经很好地建立,但本文着重于使用概率模型对不确定性几何参数进行敏感性分析和不确定性量化(UQ)。在这项研究中变化最大的两个几何参数是空腔宽度和下游边缘半径。计算流体动力学(CFD)分析用于表征声源。随机配置法用于输入参数不确定性的传播。基于2级Clenshaw-Curtis点,使用完整张量积网格和稀疏网格进行分析。结果表明,腔体宽度和下游边缘半径的不确定性会影响声源功率,峰值斯特劳哈尔数,从而影响啸叫声的起音速度。基于假定的输入参数分布函数,计算了预测开始速度的置信度。最后,结果表明进行不确定性分析的重要性,以便获得更多关于错误源的见解,从而导致更健壮的设计或面向风险管理的决策。

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