Characterization modeling of wake vortex circulation decay under reasonable worst case conditions with cuckoo search algorithm

Ridvan Oruc, T. Baklacioglu, Ozlem Sahin
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Abstract

Wake vortex (WV) produced by a large aircraft has the potential to cause serious damage to smaller aircraft following it. In this context, characterization of WV circulation decay under the reasonable worst case (RWC) conditions allows the separation minima to be found safely. In this study, modeling of dimensionless decay curves, which were developed using three experimental LIDAR (Light Detection and Ranging) datasets in the RECAT-EU project and is a useful tool to characterize the wake vortex circulation decay under RWC conditions, was carried out using cuckoo search algorithm (CSA). The decay curves used in the modeling are the median (P50), 10th (P10), and 90th (P90) percentile decay curves of the RWC tracks, which constitute the top 2% longest lasting wakes. The fact that the correlation coefficient (R) values are very close to 1 for all datasets as a result of the error analysis shows that the prediction success of the CSA model is quite high.
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利用布谷鸟搜索算法建立合理最坏情况下的尾流涡旋环流衰减特性模型
大型飞机产生的尾流涡旋(WV)有可能对尾随其后的小型飞机造成严重损害。在这种情况下,在合理的最坏情况(RWC)条件下对 WV 环流衰减进行表征,可以安全地找到分离最小值。在本研究中,使用布谷鸟搜索算法(CSA)对无量纲衰减曲线进行了建模。无量纲衰减曲线是利用 RECAT-EU 项目中的三个激光雷达(光探测和测距)实验数据集开发的,是描述 RWC 条件下尾涡环流衰减特征的有用工具。建模中使用的衰减曲线是 RWC 轨道的中位数(P50)、第 10 百分位数(P10)和第 90 百分位数(P90)衰减曲线,它们构成了持续时间最长的前 2% 纬流。误差分析结果表明,所有数据集的相关系数 (R) 值都非常接近 1,这表明 CSA 模型的预测成功率相当高。
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