Incorporating long-range dependence and fractal features in turbulence spectra

Area of Science:

• Fluid Dynamics
• Environmental Science
• Engineering

Background:

• Classical turbulence spectrum models like IEC von Kármán and Kaimal often overlook complex dynamics.
• Riverine and atmospheric boundary layer (ABL) flows exhibit long-range dependence and fractal characteristics crucial for accurate modeling.

Purpose of the Study:

• Introduce an advanced turbulence spectrum model based on covariance functions.
• Capture complex flow dynamics missed by traditional models.
• Provide a flexible model with parameters interpretable from velocity time series data.

Main Methods:

• Developed a novel turbulence spectrum model from a covariance function class.
• Empirically validated the model using extensive field data from tidal currents and ABL flows.
• Outlined a procedure for parameter extraction from time series data.

Main Results:

• The proposed model accurately captures long-range dependence and fractal characteristics.
• Demonstrated superior fidelity in replicating observed phenomena compared to classical models.
• Model parameters provide insights into distinct physical aspects of velocity time series.

Conclusions:

• The advanced turbulence spectrum model is reliable and validated by field data.
• The model enhances predictive modeling for turbulent flows in environmental and engineering applications.
• Integration with simulators like TurbSim can advance turbulent flow analysis.