🎓 Lesson 20 D5

Geometric, Kinematic, and Dynamic Similarity Principles

Geometric, kinematic, and dynamic similarity are rules that let engineers test small-scale models (like lab blasts or simulations) and confidently predict how full-size mining blasts will behave.

🎯 Learning Objectives

  • Calculate geometric scale ratios for blast design models
  • Analyze kinematic similarity conditions using dimensionless numbers (e.g., Froude, Reynolds, and Pi-blast numbers)
  • Apply dynamic similarity criteria to validate scaled laboratory blasting experiments
  • Design a dynamically similar small-scale blast test using explosive energy, rock strength, and confinement parameters
  • Explain why violating dynamic similarity leads to erroneous fragmentation or flyrock predictions

📖 Why This Matters

In mining, testing full-scale blast designs is expensive, unsafe, and environmentally restricted. Engineers rely on scaled-down physical models (e.g., 1:50 concrete analogs) and numerical simulations—but these only work if similarity principles are rigorously applied. A single mismatch in dynamic scaling—like ignoring gravity’s role in crater formation—can cause overprediction of throw distance by 300%, leading to hazardous flyrock or poor muck pile geometry. This lesson bridges lab-scale insight to safe, efficient production blasting.

📘 Core Principles

Geometric similarity is foundational: every length (burden, spacing, hole diameter) must scale linearly by factor λ (e.g., λ = L_model / L_prototype). Kinematic similarity extends this to motion: velocities scale as √λ (due to gravity dominance), times as √λ, and accelerations remain invariant under Froude scaling. Dynamic similarity is the most demanding—it requires all dimensionless groups to match. For blasting, the key groups are the Froude number (Fr = v/√(gL)), the Pi-blast number (Π_B = ρ_r v_e² / σ_c, linking explosive energy density to rock compressive strength), and the confinement number (Cn = σ_confinement / σ_c). When all are matched, stress wave propagation, fracture initiation, and fragment size distribution become scalable.

📐 Pi-Blast Number for Dynamic Similarity

The Pi-blast number (Π_B) quantifies the ratio of explosive energy density to rock strength—critical for predicting fracture intensity and fragment size. Matching Π_B between model and prototype ensures dynamically similar breakage mechanics.

Pi-Blast Number

Π_B = (ρ_exp × v_e²) / σ_c

Dimensionless metric for explosive-rock energy coupling; matching Π_B ensures scalable fracture mechanics.

Variables:
SymbolNameUnitDescription
ρ_exp Explosive density kg/m³ Density of the energetic material
v_e Detonation velocity m/s Speed of detonation front propagation
σ_c Unconfined compressive strength Pa Rock strength measured in uniaxial compression
Typical Ranges:
Hard granite (full-scale): 12 – 25
Weak shale (full-scale): 3 – 8

💡 Worked Example

Problem: A full-scale limestone quarry blast uses ANFO (v_e = 4200 m/s, ρ_explosive = 0.85 g/cm³) against rock with σ_c = 80 MPa. A 1:25 geometric scale model uses PETN (v_e = 7900 m/s, ρ_explosive = 1.76 g/cm³) in gypsum (σ_c = 12 MPa). Does the model satisfy dynamic similarity via Π_B?
1. Step 1: Compute prototype Π_B = ρ_exp × v_e² / σ_c = (850 kg/m³) × (4200 m/s)² / (80 × 10⁶ Pa) = (850 × 17,640,000) / 80,000,000 ≈ 18.7
2. Step 2: Compute model Π_B = (1760 kg/m³) × (7900 m/s)² / (12 × 10⁶ Pa) = (1760 × 62,410,000) / 12,000,000 ≈ 91.9
3. Step 3: Compare: 18.7 ≠ 91.9 → Not dynamically similar. To match, reduce model explosive energy density or increase confining stress.
Answer: The model Π_B (91.9) is over 4.9× higher than prototype (18.7); without correction, it will over-fragment and under-predict confinement effects. Adjusting charge mass or adding lateral confinement can restore similarity.

🏗️ Real-World Application

At BHP’s Olympic Dam copper mine (South Australia), scaled physical modeling of drawpoint caving blasts used 1:40 geometric models in cemented sandstone analogs. Engineers matched Π_B and Froude number by tuning explosive mass (reduced by λ³ = 1/64,000) and applying hydraulic confining pressure (scaled by λ = 1/40) to simulate in-situ stress. This validated the transition from bench blasting to sublevel caving fragmentation—reducing field trial iterations by 70% and avoiding $2.3M in unplanned ground support costs.

📋 Case Connection

📋 Bioethanol Fermentation Bioreactor Scale-Up with Inhibition Kinetics

Ethanol inhibition caused premature cessation at large scale despite matching nominal conditions

📚 References