Surrogate-assisted robust design optimization of flexible capture nets in stochastic near-ground wind fields

Area of Science:

• Aerospace Engineering
• Mechanical Engineering
• Robotics

Background:

• Near-ground flexible capture nets face stability issues due to ambient wind uncertainty.
• These nets are crucial for intercepting small, low-altitude targets like UAVs.
• Wind variability poses a significant challenge to the reliable operation of these systems.

Purpose of the Study:

• To develop a robust design for near-ground flexible capture nets.
• To quantitatively assess and improve net performance under wind uncertainty.
• To identify key design factors influencing capture net effectiveness.

Main Methods:

• Constructed a dynamics model for near-ground flexible capture nets.
• Employed the Morris-Sobol method for global sensitivity analysis.
• Utilized Gaussian Process Regression (GPR) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimization.
• Applied the Utopia point method for objective decision-making.

Main Results:

• Identified environmental wind speed, ballistic mass, launch angle, and cord diameter as primary influencing factors.
• Determined the robust design optimum parameters: ballistic mass (22.34 g), cord diameter (0.370 mm), and launch angle (28.9°).
• Achieved a statistically significant 9.02% increase in mean effective range compared to empirical designs.

Conclusions:

• The robust design significantly enhances the effective range of flexible capture nets.
• The optimized design demonstrates a substantial reduction in performance variability.
• This research provides a validated methodology for improving the reliability of capture net systems.