基于模拟的湍流速度预测：不可预测性与间歇性的关系
Analog-Based Forecasting of Turbulent Velocity: Relationship between Unpredictability and Intermittency
AI总结：
本研究评估了基于模拟的湍流纵向速度预测方法，发现预测创新与速度梯度极端事件及间歇性相关，指出模型在捕捉高阶相关性上的局限，为改进提供方向。
摘要：
本研究评估了基于模拟的方法在统计层面预测湍流中纵向速度的性能。所用数据来自Modane风洞的热线实验测量结果。我们比较了不同方法，并探究了模拟数量及其规模变化对预测精度的影响。我们发现，创新（定义为真实速度值与预测值的差值）突出了特别不可预测的事件，这些事件与速度梯度的极端事件直接相关，进而与间歇性相关。对创新的统计研究表明，虽然估计器有效捕捉了线性相关性，但未能完全捕捉高阶相关性。创新强调了间歇性的存在，揭示了当前预测模型的局限性，并为湍流预测的未来改进指明了方向。本研究评估了基于模拟的方法在统计层面预测湍流中纵向速度的性能。所用数据来自Modane风洞的热线实验测量结果。我们比较了不同方法，并探究了模拟数量及其规模变化对预测精度的影响。我们发现，创新（定义为真实速度值与预测值的差值）突出了特别不可预测的事件，这些事件与速度梯度的极端事件直接相关，进而与间歇性相关。这一结果表明，虽然估计器有效捕捉了线性相关性，但未能完全捕捉高阶相关性。创新强调了间歇性的存在，揭示了当前预测模型的局限性，并为湍流预测的未来改进指明了方向。
英文摘要：
This study evaluates the performance of analog-based methodologies to predict, in a statistical way, the longitudinal velocity in a turbulent flow. The data used comes from hot wire experimental measurements from the Modane wind tunnel. We compared different methods and explored the impact of varying the number of analogs and their sizes on prediction accuracy. We illustrate that the innovation, defined as the difference between the true velocity value and the prediction value, highlights particularly unpredictable events that we directly link with extreme events of the velocity gradients and so to intermittency. A statistical study of the innovation indicates that while the estimator effectively seizes linear correlations, it fails to fully capture higher-order dependencies. The innovation underscores the presence of intermittency, revealing the limitations of current predictive models and suggesting directions for future improvements in turbulence forecasting.This study evaluates the performance of analog-based methodologies to predict the longitudinal velocity in a turbulent flow. The data used comes from hot wire experimental measurements from the Modane wind tunnel. We compared different methods and explored the impact of varying the number of analogs and their sizes on prediction accuracy. We illustrate that the innovation, defined as the difference between the true velocity value and the prediction value, highlights particularly unpredictable events that we directly link with extreme events of the velocity gradients and so to intermittency. This result indicates that while the estimator effectively seizes linear correlations, it fails to fully capture higher-order dependencies. The innovation underscores the presence of intermittency, revealing the limitations of current predictive models and suggesting directions for future improvements in turbulence forecasting.
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