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Comparison of Ensemble Predictions of a New Probabilistic Fast-Time Wake Vortex Model and Lidar Observed Vortex Circulation Intensities and Trajectories

机译:新概率快速时间尾涡涡流模型和激光葡萄球菌的集合预测比较涡流循环强度和轨迹

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Lidar observations of wake vortex circulation intensity and trajectories are compared to the predictions of a new probabilistic fast-time wake vortex model. The new probabilistic model utilizes several different deterministic fast-time vortex models and produces ensemble model predictions of wake vortex behavior. Multiple numerical predictions are conducted in the form of a Monte Carlo analysis with slightly different initial conditions that are all plausible given the past or current set of observations and measurements. The probabilistic model predictions attempt to account for the two primary sources of uncertainty in fast-time wake vortex models: the errors introduced by sensitivity to the initial aircraft parameters and environmental conditions; and errors introduced because of imperfections in the model assumptions and simplifications. There are three main findings of this study. First, the new probabilistic fast-time wake vortex prediction model reproduces observed vortex behavior and predicts approximately the same mean and spread as the observations. Second, uncertainties in the generator aircraft parameters are as important as uncertainties in the atmospheric conditions in generating estimated spread in the model predictions. And third, the spread between the different deterministic fast-time wake vortex models overlaps when plausible uncertainties in initial conditions are used. While development and validation of the new probabilistic fast-time wake vortex model is on-going, it appears that it will develop into a useful tool to help assess deterministic fast-time model performance, to assist in planning future wake vortex field observation campaigns, and eventually to examine current or proposed wake separation standards.
机译:唤醒漩涡循环强度和轨迹的LIDAR观察与新的概率快速时间尾涡涡流模型的预测进行了比较。新的概率模型利用了几种不同的确定性快速时间涡流模型,并产生了Wake涡旋行为的集合模型预测。多种数值预测以蒙特卡罗分析的形式进行,初始条件略有不同,初始条件是给予过去或当前的观测和测量的所有合理的。所述概率模型预测尝试帐户在快速时间尾涡流模型不确定性的两个主要来源:由灵敏度引入到初始飞机参数和环境条件中的错误;由于模型假设和简化中的缺陷,因此引入的错误。这项研究有三个主要结果。首先,新的概率快速时间唤醒涡流预测模型再现观察到的涡旋行为,并预测大致相同的平均值并传播作为观察。其次,发电机飞机参数中的不确定性在模型预测中产生估计扩散的大气条件下的不确定性是重要的。第三,当使用初始条件下的合理的不确定性时,不同的确定性快速唤醒涡流模型之间的扩散重叠。虽然开发和验证了新的概率快速时间尾迹涡旋模型,但它似乎将开发成一个有用的工具,以帮助评估确定性的快速时间模型性能,以协助规划未来的苏醒涡流场观察活动,最终检查当前或提出的唤醒分离标准。

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