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

This paper addresses the poor stability of near-ground flexible capture nets under uncertainty in the ambient wind. The capture net considered is a near-ground system for intercepting small low-altitude targets such as small UAVs or individuals; given its small scale (a 3.2 m net) and short deployment time (about 2 s), the wind can be treated as a quasi-steady flow during a single deployment, and the relevant uncertainty is characterized through the variability of the near-ground ambient mean wind speed. Focusing on the factors influencing the flight attitude of capture nets, a performance evaluation index model for flexible capture nets is established, enabling quantitative assessment of net performance, and a near-ground flexible capture net dynamics model is constructed. The Morris-Sobol method is employed to perform global sensitivity analysis on the factors affecting the effective range of flexible capture nets, revealing that environmental wind speed, ballistic mass, launch angle, and cord diameter are the primary factors influencing capture net performance. The Pareto-optimal non-dominated solution set is obtained using a Gaussian Process Regression (GPR) surrogate model solved via the Non-dominated Sorting Genetic Algorithm II (NSGA-II), followed by objective decision-making based on the Utopia point method. The robust design optimum is determined as m_b = 22.34 g, d = 0.370 mm, and theta = 28.9 deg. Finally, high-fidelity numerical simulations are conducted for verification. Compared with the empirical design parameters, the mean effective range increases by 9.02% (statistically significant), and the standard deviation of the deterministic design is 45.71% higher than that of the robust design.