飞行器生成式模型气动设计研究进展与展望

doi:10.7527/S1000-6893.2025.31679

Abstract

Abstract:

As one of the fastest-growing directions in deep learning， the generative model has achieved remarkable success in realms such as computer vision and has also introduced novel paradigms and methodologies for research endeavors within the scientific fields like aerodynamics. This paper focuses on the latest research advancements of generative models in the field of aircraft aerodynamic configuration design， and systematically summarizes the relevant research achievements in recent years. Firstly， a representation-generation-evaluation framework for generative aerodynamic configuration design of aircraft is established. Subsequently， the key technologies and current development progress involved in aerodynamic configuration design are examined and discussed from the perspectives of aerodynamic configuration representation， the development of generative aerodynamic configuration design models， and methods for evaluating design quality. Additionally， a brief overview of aerodynamic data construction methods and typical datasets is provided， serving as a data foundation for generative aerodynamic design. Lastly， the future key development directions in the field of generative aerodynamic configuration design are discussed， including exploration of hybrid generative model architectures， construction of large models and domain-specific agents for aerodynamic design， establishment of a comprehensive evaluation system for generative aerodynamic design quality， and integration of domain knowledge into generative aerodynamic design models.

Key words: aerodynamic configuration design, generative model, aerodynamic configuration representation, aerodynamics performance evaluation, deep learning

CLC Number:

Jie LIN, Zhigong TANG, Weiqi QIAN, Yueqing WANG, Peng ZHANG, Weixia XU, Jie LIU. Research progress and prospects of aircraft aerodynamic design based on generative models[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631679.

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模型名称	研究对象	工况约束	目标约束	布局表示形式	生成式模型
PcDGAN^［128］	翼型	单一工况	单维约束	几何参数化	GAN
Viquerat等^［120］	翼型	单一工况	单维约束	Bézier参数化	DRL
Dussauge等^［121］	翼型	单一工况	多维约束	几何参数化	DRL
CVAE-GAN^［116］	超临界翼型	单一工况	单维约束	几何参数化	CVAE-GAN
CcDPM^［34］	翼型，火箭	多个工况	多维约束	几何参数化	DPM
CWGAN-GP+MMoE^［49］	导弹	单一工况	多维约束	几何参数化	GAN
Range-GAN^［39］	飞机	单一工况	单维约束	隐士参数化	GAN
Shu等^［40］	飞机	单一工况	单维约束	点云	GAN
Zhang等^［72］	飞机	单一工况	单维约束	SDF	VAE

数据集名称	对象类型	数据格式	样本数量
UIUC数据集^［196］	二维翼型	几何坐标	1 600
Nobari 等^［128］	二维翼型	几何坐标	38 802
AFBench^［117］	二维翼型	几何坐标	200 000
ShapeNet^［198］	三维布局	点云	51 300
PartNet^［199］	三维布局	点云	573 585
ModelNet^［52］	三维布局	体素	127 915

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Research progress and prospects of aircraft aerodynamic design based on generative models

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