Parametric Optimization of VLM Panel Discretization Using Bio-Inspired Crayfish and Aquila Algorithms Coupled with...

Area of Science:

• Computational Fluid Dynamics (CFD)
• Aerospace Engineering
• Optimization Algorithms

Background:

• Accurate aerodynamic predictions are vital in early aircraft design stages.
• The Vortex Lattice Method (VLM) offers computational efficiency but requires optimized panel discretization for accuracy.
• Current discretization strategies are often heuristic, lacking systematic optimization.

Purpose of the Study:

• To develop and evaluate a bio-inspired optimization framework for VLM panel discretization.
• To enhance the predictive accuracy of VLM for aircraft wing geometries.
• To compare the performance of bio-inspired optimization algorithms against a Genetic Algorithm.

Main Methods:

• Grid-convergence analysis to ensure solution independence.
• Hybrid Response Surface Methodology (HRSM) integrating Box-Behnken and Central Composite designs for factor space exploration.
• Ensemble Machine-Learning surrogate models coupled with Crayfish, Aquila, and Genetic Algorithms (GA) for optimization.

Main Results:

• Optimally clustered discretization improved aerodynamic prediction accuracy by approximately 33% compared to uniform distribution.
• Trailing-edge clustering was dominant at low angles of attack (up to 90% variance), while tip clustering influenced higher angles (>30%).
• Aquila algorithm showed higher solution consistency, while Crayfish algorithm exhibited faster convergence with greater dispersion.

Conclusions:

• Bio-inspired optimization significantly enhances VLM aerodynamic prediction accuracy, especially at low angles of attack.
• Adaptive discretization strategies are crucial for reliable VLM analyses across different flight conditions.
• The study reveals a multimodal optimization landscape and the trade-offs between convergence speed and solution consistency.