cfd-vv-suite

Digitization Guide: Adding Experimental Data to cfd-vv-suite

This guide explains how to extract reference data from published experimental graphs and add it to the project.

When to digitize

Many experimental papers present results as graphs rather than tables. To use these as validation data:

1. The paper must be from a reputable source (journal, conference, technical report)
2. The experimental setup should be well-documented (geometry, flow conditions, measurement technique)
3. The graphs should be clear and high-resolution
4. Measurement uncertainties should be quoted

Tools

Recommended free tool: WebPlotDigitizer (https://automeris.io)

# Install (web version works without installation)
# Or use the desktop app:
# https://github.com/ankitrohatgi/WebPlotDigitizer/releases

Alternative: PlotDigitizer (https://plotdigitizer.com), Engauge Digitizer

Wolfram Mathematica

If you have access to Wolfram Mathematica, the built-in image processing tools provide a fast digitization workflow (2-3 minutes per graph):

1. Import the graph image:

   img = Import["figure.png"];
   

2. Use LocatorPane for interactive calibration — click known axis points:

   pts = {{x1, y1}, {x2, y2}};  (* replace with axis min/max coordinates *)
   

3. Extract the curve with ImageLines or manual tracing via Graphics + Locator:

   curve = ImageData[Binarize[img, 0.5]];
   (* or use DynamicModule with Locator for interactive point selection *)
   

4. Transform pixel coordinates to physical units with LinearModelFit or a manual affine transform:

   transform[{px_, py_}] := {
       a1 + b1*px + c1*py,
       a2 + b2*px + c2*py
   };
   (* fit coefficients from calibration points *)
   

5. Export to CSV for cfd-vv-suite:

   Export["reference/experimental/profile.csv", data, "CSV"]
   

Tips for Mathematica digitization:

• Use ImageTake to crop to the graph area before processing
• ColorNegate and Binarize help isolate curves from the background
• FindPeaks can automate extraction from profile plots
• ComponentMeasurements is useful for scatter plot digitization

Step-by-step workflow

1. Obtain a high-resolution image

• Download the PDF of the paper
• Use a screenshot tool at high zoom (Windows: Win+Shift+S at 200%+ zoom)
• Crop to the graph area only
• Save as PNG (lossless)

2. Calibrate axes

In WebPlotDigitizer:

1. File -> Load Image
2. Click on “2D (X-Y) Plot”
3. Align axes: click “Axes” -> “Calibrate Axes”
4. Set four calibration points:
   • X-min, Y-min (bottom-left of axes)
   • X-max, Y-min (bottom-right)
   • X-min, Y-max (top-left)
   • X-max, Y-max (top-right)
5. Enter the actual numerical values for each calibration point
6. Verify: check several tick marks to ensure the calibration is correct

3. Extract data points

• Use “Automatic Extraction” for curves (color-based detection)
• Use “Manual Mode” for sparse data or noisy backgrounds
• For profile plots (e.g., u(y) profile): use “Automatic” with a line width of 3-5 pixels
• For scatter plots: use “Manual Mode” and click on each visible data point
• Export as CSV: File -> Export Data -> CSV

4. Format for cfd-vv-suite

The exported CSV needs to be transformed to the project format:

import csv

# Example: digitized velocity profile u(y/c) at x/c=0.5
# WebPlotDigitizer output: x_pixel, y_pixel, u_Uinf
# We need: y, u, v  (or x, y, u, v for 2D)

with open('digitized.csv', 'r') as f_in:
    reader = csv.reader(f_in)
    next(reader)  # skip header
    data = [(float(row[1]), float(row[2])) for row in reader]

with open('reference/experimental/profile_mid.csv', 'w', newline='') as f_out:
    w = csv.writer(f_out)
    w.writerow(['y', 'u', 'v'])  # standard field names
    for y, u in data:
        w.writerow([y, u, 0.0])

5. Document the source

Every digitized dataset requires:

1. Source paper: full citation with DOI/URL in case.yaml and README.md
2. Original figure number: e.g., “Digitized from Fig. 7”
3. Measurement method: e.g., “LDA velocity measurements”, “Pressure taps”
4. Uncertainty: quoted from the paper or estimated from digitization accuracy
5. Digitization date and tool: e.g., “Digitized with WebPlotDigitizer v4.6, 2026-07”

Add this information to the README.md of the case and to docs/data-provenance.md.

6. Add to case.yaml

reference:
  type: experimental
  source: "Author et al. Journal, Vol, pp, Year. Digitized from Fig. X."
  doi: "https://doi.org/..."  # if available
  digitization:
    tool: "WebPlotDigitizer v4.6"
    figure: "Fig. 7"
    notes: "21 points extracted from u/U_inf vs y/delta plot"

7. Verify digitization accuracy

# Compare digitized data against known checkpoints
# For example, the maximum value should match the quoted value in the paper
python -c "
import numpy as np
data = np.loadtxt('reference/experimental/profile.csv', delimiter=',', skiprows=1)
print(f'Max u/U_inf = {np.max(data[:,1]):.4f} (paper quotes: 0.995)')
print(f'{len(data)} points extracted')
"

8. Add data provenance entry

Update docs/data-provenance.md with a new entry describing what was digitized and how.

Quality checklist

Before submitting a digitized dataset:

• Axes are correctly calibrated (compare tick marks)
• The curve is correctly traced (compare visually)
• Quantities match known integral values from the paper (e.g., Cd, Nu)
• Number of points is sufficient (typically 50-200 for a smooth profile)
• Data is in the correct physical units (check axis labels)
• Field names follow the project convention (u, v, w, p, T, Cp, Cf, …)
• Source is properly cited with DOI/URL

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