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Fatigue_Life/LHS_Sampling.py

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+import numpy as np
+from scipy.stats import qmc
+
+# 1. Define the number of new samples you want to generate
+num_samples = 26
+
+# 2. Define the parameter space [min, max] for each variable
+# [Large Diameter D, Small Diameter d, Fillet Radius r]
+param_bounds = [
+    [14.0, 16.0],  # Range for D
+    [5.0, 7.5],   # Range for d
+    [1.0, 3.5]    # Range for r
+]
+
+# 3. Create the Latin Hypercube Sampler
+# The sampler generates points in a normalized space (between 0 and 1)
+# We use a seed for reproducibility, so you always get the same "random" set
+sampler = qmc.LatinHypercube(d=len(param_bounds), seed=42)
+unit_hypercube_samples = sampler.random(n=num_samples)
+
+# 4. Scale the normalized samples to your actual parameter ranges
+scaled_samples = qmc.scale(unit_hypercube_samples, 
+                           [b[0] for b in param_bounds], 
+                           [b[1] for b in param_bounds])
+
+# 5. Print the results in your desired format
+print("--- Generated 26 New Shaft Combinations using LHS ---")
+for i, sample in enumerate(scaled_samples):
+    # Ensure that d is always less than D
+    # If by chance LHS generates d > D, we can swap them or adjust.
+    # A simple approach is to ensure d is at least a certain amount smaller than D.
+    D_val = sample[0]
+    d_val = sample[1]
+    
+    # Simple constraint: ensure d is at most 95% of D
+    if d_val >= D_val:
+        d_val = D_val * 0.95
+        
+    r_val = sample[2]
+    
+    # Format the output to one decimal place for clarity
+    print(f"{i+1:2d}: life_D{D_val:.1f}_d{d_val:.1f}_r{r_val:.1f}")
+

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