[heatmap-rainflow] Rainflow Counting Matrix for Fatigue Analysis

[MarkusNeusinger]

Description

A rainflow counting matrix (also called rainflow histogram) visualizes the results of rainflow cycle counting from a load or stress time history. The matrix displays cycle counts as a heatmap or 3D histogram where one axis represents cycle amplitude (or range), the other represents cycle mean (or from-to levels), and color/height represents the frequency of each cycle combination. This is a fundamental tool in fatigue analysis and durability engineering.

Applications

• Fatigue life prediction and damage assessment of mechanical components
• Analyzing load spectra from real-world measurements (vehicles, aircraft, wind turbines)
• Comparing measured vs. design load spectra
• Quality assurance in structural and mechanical engineering

Data

• amplitude (numeric) — half-range of each counted cycle
• mean (numeric) — mean value of each counted cycle
• count (numeric) — number of cycles at each amplitude-mean combination
• Alternatively: raw time series signal from which rainflow counting is performed

Notes

• Typically displayed as a 2D heatmap or 3D bar histogram
• Often paired with an S-N curve (Wöhler curve) for damage calculation
• The rainflow counting algorithm (ASTM E1049) extracts closed hysteresis loops from irregular load histories
• Color scale should clearly distinguish high-frequency from low-frequency cycle bins
• Consider showing the original time series signal alongside the rainflow matrix

[claude]

✅ Spec ID: heatmap-rainflow

Rationale: A rainflow counting matrix is a specialized heatmap where axes represent cycle amplitude and mean, and color encodes cycle frequency — a fundamental tool in fatigue/durability engineering with no existing duplicate.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: heatmap-rainflow

PR: #4466

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specification.md

heatmap-rainflow: Rainflow Counting Matrix for Fatigue Analysis

Description

A rainflow counting matrix visualizes the results of rainflow cycle counting from a load or stress time history. The matrix displays cycle counts as a 2D heatmap where one axis represents cycle amplitude (half-range), the other represents cycle mean value, and color intensity represents the frequency of each cycle combination. This is a fundamental tool in fatigue analysis and durability engineering, used to characterize variable-amplitude loading for fatigue life prediction.

Applications

• Fatigue life prediction and damage assessment of mechanical components under variable-amplitude loading
• Analyzing load spectra from real-world measurements on vehicles, aircraft structures, or wind turbines
• Comparing measured vs. design load spectra in structural and mechanical engineering quality assurance
• Identifying dominant cycle combinations that contribute most to cumulative fatigue damage

Data

• amplitude (numeric) — half-range of each counted cycle (stress or load units)
• mean (numeric) — mean value of each counted cycle (stress or load units)
• count (numeric) — number of cycles at each amplitude-mean combination
• Size: 10x10 to 64x64 bins typical for binned rainflow matrices
• Example: Rainflow counting results from a simulated or measured variable-amplitude load signal with ~20 amplitude bins and ~20 mean bins

Notes

• Display as a 2D heatmap with amplitude on the y-axis and mean on the x-axis
• Use a sequential colormap (e.g., viridis or hot) with a logarithmic or linear color scale to clearly distinguish high-frequency from low-frequency cycle bins
• Include a colorbar indicating cycle count values
• Axis labels should indicate physical units (e.g., MPa, kN) where applicable
• Consider adding axis tick labels corresponding to bin centers or edges
• Zero-count bins should be visually distinct (e.g., white or transparent background)

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[github-actions]

🚀 Specification Merged!

Specification: heatmap-rainflow
PR: #4466 (merged)

The specification is now in main and synced to PostgreSQL.

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Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

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