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openpiv

k-dense-ai/scie…t-skills

Performs Particle Image Velocimetry (PIV) analysis with OpenPIV. Use when extracting velocity fields from PIV image pairs, analyzing fluid dynamics or flow visualization experiments, cross-correlating interrogation windows, validating and replacing spurious PIV vectors, or computing vorticity, strain rate, and turbulence statistics from measured velocity fields.

  • 985 次安装
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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill openpiv

SKILL.md

OpenPIV

Overview

OpenPIV (Open Particle Image Velocimetry) analyzes fluid flow from PIV image pairs. It covers preprocessing, cross-correlation, vector validation, outlier replacement, smoothing, and scaling to physical units.

Targets openpiv 0.26.1. Synthetic checks ran on Python 3.13, NumPy 2.5.3, SciPy 1.18.1, scikit-image 0.26.0, and Matplotlib 3.11.2. Rust was not installed; use backend="scipy" for the tested path. See review evidence.

When to use

Use this skill when working with experimental PIV or flow-visualization image pairs: measuring 2D velocity fields, tuning interrogation-window parameters, validating vectors, or deriving vorticity, strain rate, and turbulence statistics. For simulating flow rather than measuring it, use a CFD skill instead.

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