Python for Test and FEA Data Comparison
How to use Python to compare strain-gauge and modal test data with FEA results — data parsing, interpolation, correlation metrics and reporting.
Engineering Task
Comparing physical test measurements with FEA predictions to validate the model.
Strain-Gauge Correlation
# Compare FE strain with gauge measurements
import numpy as np
import pandas as pd
# FE strain at gauge locations
fe_strain = pd.read_csv("fe_strain_at_gauges.csv")
# Test data
test_strain = pd.read_csv("test_results.csv")
# Correlation
correlation = pd.merge(fe_strain, test_strain, on="gauge_id")
correlation["error_pct"] = (correlation["fe_microstrain"] - correlation["test_microstrain"]) / correlation["test_microstrain"] * 100
mean_error = correlation["error_pct"].mean()
max_error = correlation["error_pct"].abs().max()Modal Correlation
- Compare natural frequencies: percentage error per mode pair
- Calculate MAC (Modal Assurance Criterion) between FE and test mode shapes
- Use numpy.dot for MAC calculation: MAC = |φ_test^T × φ_fe|² / (|φ_test|² × |φ_fe|²)
- Plot MAC matrix as a heatmap to identify mode pairing
Key Considerations
- Extract FE strain at the exact gauge location and orientation — interpolate, do not use nearest node
- Account for gauge integration length — average FE strain over the gauge active length
- Scale test data to the FE load level if different (verify linearity first)
- Account for measurement uncertainty — a 3% match may be within test uncertainty