Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Dopple...Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.展开更多
基金supported by the National Natural Science Foundation of China (No.U2133210).
文摘Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.
文摘利用有利的侧风条件适度缩减尾流间隔以提升空域容量已成为国际空管研究的热点问题之一。在建立了A320机翼尾涡流场上,基于RANS方法采用RKE涡粘模型对雷诺应力项进行二方程封闭,提出利用UDF(用户自定义函数)编程技术分别施加静风、1 m/s、4 m/s、7 m/s 4个不同侧风风场,在"天河一号"超级计算机上开展数值模拟实验。基于试验数据,分析了不同侧风影响下的尾涡下沉运动、涡量衰减、尾涡横向运动、涡心速度等参数的变化规律。结果表明:受到侧风扰动后,尾涡涡量快速上升,其滚转力矩在短时间内迅速增加,尾涡涡心间距快速减小后又迅速反弹分离。在垂直方向上,尾涡反复上下跳跃,呈现出不稳定性,强侧风时的诱导湍流形成的剪切梯度会造成涡核脱落,涡体迸裂进而快速消散。在水平方向上,尾涡会被强侧风快速吹离主航路,有利于缩减所需的尾流间隔、提高机场运行效率。
