A Robust Aerodynamic Design Optimization Methodology for UAV Airfoils Based on Stochastic Surrogate Model and PPO-Clip Algorithm

Unmanned Aerial Vehicles (UAVs) are widely used in meteorology and logistics due to their unique advantages nowadays. During their lifecycle, uncertainties—such as flight condition variations—can significantly affect both design and performance, making Robust Aerodynamic Design Optimization (RADO) e...

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Bibliographic Details
Published in:Drones (Basel) Vol. 9; no. 9; p. 607
Main Authors: Wang, Yiyu, Huo, Yuxin, Zhong, Zhilong, Ji, Renxing, Chen, Yang, Wang, Bo, Ma, Xiaoping
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2025
Subjects:
Aerodynamics
Aircraft
airfoil optimization
Airfoils
Algorithms
Artificial intelligence
Design and construction
Design optimization
Drone aircraft
Efficiency
Hypercubes
intelligent algorithms
Latin hypercube sampling
Mach number
Machine learning
Mathematical optimization
Methodology
Optimization algorithms
Random variables
Robust Aerodynamic Design Optimization
Robustness
stochastic surrogate model
Systems stability
Turbulence models
Uncertainty analysis
Unmanned Aerial Vehicles
China
ISSN:2504-446X, 2504-446X
Online Access:Get full text
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Summary:Unmanned Aerial Vehicles (UAVs) are widely used in meteorology and logistics due to their unique advantages nowadays. During their lifecycle, uncertainties—such as flight condition variations—can significantly affect both design and performance, making Robust Aerodynamic Design Optimization (RADO) essential. However, existing RADO methodologies face high computational cost of uncertainty analysis and inefficiency of conventional optimization algorithms. To address these challenges, this paper proposed a novel RADO methodology integrating a Stochastic Kriging (SK) surrogate model with the PPO-Clip reinforcement learning algorithm, targeting atmospheric uncertainties encountered by turbojet-powered UAVs in transonic cruise. The SK surrogate model, constructed via Maximin Latin Hypercube Sampling and refined using the Expected Improvement infill criterion, enabled efficient uncertainty quantification. Based on the trained surrogate model, a PPO-Clip-based RADO framework with tailored reward and state transition functions was established. Applied to the RAE2822 airfoil under Mach number perturbations, the methodology demonstrated superior reliability and efficiency compared with L-BFGS-B and PSO algorithms.
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content type line 14
ISSN:2504-446X
2504-446X
DOI:10.3390/drones9090607