A novel Python module for statistical analysis of turbulence (P-SAT) in geophysical flows.

Area of Science:

• Fluid Dynamics
• Computational Physics
• Data Analysis

Background:

• Turbulent flow analysis requires robust data processing tools.
• Existing methods for analyzing turbulent statistics can be time-consuming and complex.

Purpose of the Study:

• To introduce the Python Statistical Analysis of Turbulence (P-SAT) framework.
• To automate the parsing, filtering, and computation of turbulent statistics and spectra.
• To provide a flexible and user-friendly tool for analyzing steady and unsteady flow data.

Main Methods:

• Developed a lightweight Python framework (P-SAT).
• Implemented data filtering techniques: velocity correlation, signal-to-noise ratio (SNR), and acceleration thresholding.
• Utilized Fourier-component based averaging for unsteady flow mean velocity calculation.
• Enabled batch and single input processing.

Main Results:

• P-SAT automates the de-spiking of velocity signals for steady flows.
• The framework generates CSV files containing computed turbulent parameters.
• Successfully handles both steady and unsteady flow velocity time series.
• Offers default and user-defined threshold values for filtering methods.

Conclusions:

• P-SAT provides an efficient and automated solution for turbulent flow data analysis.
• The framework's flexibility and cross-platform compatibility enhance its utility for researchers.
• P-SAT facilitates the computation of essential turbulent statistics and spectra.