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

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Title: A Robust Aerodynamic Design Optimization Methodology for UAV Airfoils Based on Stochastic Surrogate Model and PPO-Clip Algorithm
Authors: Yiyu Wang, Yuxin Huo, Zhilong Zhong, Renxing Ji, Yang Chen, Bo Wang, Xiaoping Ma
Source: Drones, Vol 9, Iss 9, p 607 (2025)
Publisher Information: MDPI AG, 2025.
Publication Year: 2025
Collection: LCC:Motor vehicles. Aeronautics. Astronautics
Subject Terms: Unmanned Aerial Vehicles, airfoil optimization, Robust Aerodynamic Design Optimization, stochastic surrogate model, intelligent algorithms, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Description: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2504-446X
Relation: https://www.mdpi.com/2504-446X/9/9/607; https://doaj.org/toc/2504-446X
DOI: 10.3390/drones9090607
Access URL: https://doaj.org/article/29cea49a5ab9452ca6808ef18b62a609
Accession Number: edsdoj.29cea49a5ab9452ca6808ef18b62a609
Database: Directory of Open Access Journals
Description
Abstract: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.
ISSN:2504446X
DOI:10.3390/drones9090607