Does Scatter Matter? Improved Understanding of UH-60A Wind Tunnel Rotor Measurements Using Data-Driven Clustering and CREATE-AV Helios

M. Ramasamy, T. Norman, R. Jain
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引用次数: 1

Abstract

A data-driven clustering algorithm based on proper orthogonal decomposition was applied to assess the scatter found in the UH-60A wind tunnel airloads measurements. Upon verifying the capability of the algorithm, pushrod loads, blade surface pressure, sectional loads, and torsional moments were analyzed. Spatial eigenmodes resulting from the decomposition provided the optimal basis; projection of the individual cycles on to the high singular value modes allowed visualizing the statistical distribution of data over the entire azimuth. While not all cases showed furcation in the data, bimodal distribution was found in the high thrust cases, where statistically normal distribution is generally assumed. Consequent clustering of the measured cycles produced excellent correlation among clusters found in the pushrod loads, blade surface pressure, and torsional moment that suggest a common source for furcation in the data. The cycles assigned to one group repeatedly showed distinguishable variations from the other group in terms of the presence/absence of a dynamic stall vortex, azimuthal occurrence of stall, chordwise location of separation and reattachment etc. When one of the cluster is smaller in size compared to the other, the conventional phase-average obscured all the intricate features even when the loads are substantially higher than the larger cluster. In general, clustering the data set when warranted showed not only higher peak loads but also lower variance for both the clusters across the entire azimuth compared to the conventional simple phase-average results. Computational simulations were conducted using CREATETM-AV Helios towards understanding the underlying flow field. Misjudged earlier as under/over-predictive when compared with the simple phase-average data, Helios results consistently showed significantly improved correlation with the smaller of the two clusters. Combining the clustered results and the flow visualization provided by Helios, aperiodicity in the spatial location and the strength of both the trim tab vortices and tip vortices have also been hypothesized as potential sources of furcation.
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散射很重要吗?利用数据驱动聚类和CREATE-AV Helios改进对UH-60A风洞转子测量的理解
采用一种基于适当正交分解的数据驱动聚类算法,对UH-60A风洞气动载荷测量中发现的散射进行了评估。在验证算法的能力后,分析了推杆载荷、叶片表面压力、截面载荷和扭转矩。由分解得到的空间特征模态为优化提供了依据;将单个周期投影到高奇异值模式上,可以可视化整个方位角上数据的统计分布。虽然并非所有情况下的数据都显示分叉,但在高推力情况下发现双峰分布,通常假设统计上的正态分布。测量周期的聚类结果在推杆载荷、叶片表面压力和扭转力矩的聚类之间产生了极好的相关性,这表明数据中存在一个共同的分岔源。分配给一组的周期反复显示出与另一组在动态失速漩涡的存在/不存在、失速发生的方位、弦向分离和重新连接的位置等方面的明显差异。当一个簇的大小比另一个簇小时,即使负载比大簇高得多,传统的相位平均也会掩盖所有复杂的特征。一般来说,与传统的简单相位平均结果相比,在保证对数据集进行聚类时,不仅可以显示更高的峰值负载,而且可以显示整个方位角上两个聚类的方差更低。利用CREATETM-AV Helios进行了计算模拟,以了解底层流场。与简单的相位平均数据相比,Helios的结果更早被误判为预测不足或预测过度,结果始终显示出与两个星团中较小星团的相关性显著提高。结合聚类结果和Helios提供的流动可视化,空间位置的非周期性以及翼缘旋涡和叶尖旋涡的强度也被假设为分叉的潜在来源。
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