Analog-Based Forecasting of Turbulent Velocity: Relationship between Unpredictability and Intermittency

TL;DR

This paper assesses analog-based methods for predicting turbulent flow velocity, revealing their limitations in capturing complex dependencies and highlighting intermittency and unpredictable events in turbulence.

Contribution

It introduces a statistical analysis linking prediction innovation to intermittency and evaluates the impact of analog selection on turbulence velocity forecasting.

Findings

Analog methods capture linear correlations but miss higher-order dependencies.

Innovation correlates with extreme velocity gradient events and intermittency.

Current models have limitations in predicting complex turbulent behaviors.

Abstract

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.