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Airfoil Shape Optimization Using Surrogate model & QIO

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Challenges
  • Aerodynamic optimization requires repeated evaluation of drag coefficient (Cd)
  • CFD or experimental evaluations are computationally expensive
  • Design space defined by multiple control parameters with tight bounds (±2.5%)
  • Optimization landscape is non-convex and sensitive to initialization

Results

High compute cost with slow and unstable convergence in classical workflows.

Airfoil Optimization Performance

  • Final Fitness (Cd):
    • QIO: 0.014341
    • GA: 0.014342
  • Function Evaluations:
    • QIO: 990
    • GA: 2053

‍

Design Outcome

  • Optimized airfoil shape shows improved surface characteristics
  • QIO achieves comparable or better performance with significantly lower compute
  • → ~2× fewer evaluations with QIO

‍

Validation on Benchmark Function

Tested on Goldstein–Price function (non-convex benchmark):

  • Global Minimum:
    • Reference: 3.0
    • QIO: 2.99995
    • GA: 2.99989
  • Design Variable Accuracy:
    • QIO closer to optimal solution

Accelerating Aerodynamic Optimization with Surrogate modelling & QIO

Prediction Accuracy

High-Fidelity Surrogate (R² = 0.996)

Compute Efficiency
52% Reduction in Evaluations (990 vs 2053)
Optimization Quality
Near-Global Optimum Convergence (2.99995)
Go Beyond Classical Limits.
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