基于去噪扩散模型的翼型快速反设计研究

程明; 何磊; 蔺佳哲; 黄铭基; 张显才; 赵暾

doi:10.13224/j.cnki.jasp.20240425

基于去噪扩散模型的翼型快速反设计研究

doi: 10.13224/j.cnki.jasp.20240425

中国空气动力研究与发展中心计算空气动力研究所，四川绵阳 621000

详细信息

作者简介:
程明（1995-），男，助理研究员，硕士，从事机器学习与气动建模研究。E-mail：chengming_20@163.com

通讯作者:
何磊（1988-），男，副研究员、硕士生导师，博士，从事机器学习、气动数据建模与智能应用研究。E-mail：helei_email@163.com

中图分类号: V211.3
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出版历程
- 收稿日期: 2024-06-27
- 网络出版日期: 2026-01-24

Research on fast inverse design of airfoil based on denoising diffusion model

Computational Aerodynamics Institute，China Aerodynamics Research and Development Center，Mianyang Sichuan 621000，China

摘要

摘要:
为了实现翼型的快速反设计，利用去噪扩散模型强大的数据特征学习与样本生成能力，开展了基于去噪扩散模型的翼型快速反设计方法研究，直接以翼型坐标作为翼型的外形描述方式，实现气动性能条件控制下的翼型快速反设计。首先基于UIUC翼型库构建基础翼型集；以基础翼型集作为输入，训练无条件扩散模型；再基于无条件扩散模型生成大量新翼型，建立补充翼型集，增加数据的多样性；然后使用Xfoil翼型分析软件计算翼型气动性能；以翼型和气动性能作为输入，训练条件扩散模型，最终实现基于此模型的快速翼型反设计。实验结果表明：基于去噪扩散模型的翼型快速反设计方法的单个翼型设计仅需1.25 s，与标签翼型的气动性能偏差基本控制在了6%以内，同时生成翼型具有多样性，为创新设计更加高效的翼型提供了条件。
- 翼型反设计 /
- 翼型坐标 /
- 气动性能 /
- 去噪扩散模型 /
- 生成式模型
Abstract:
A fast inverse design method of airfoil based on denoising diffusion probability model was established, which directly used coordinates as the description mode of airfoil, and utilized the powerful data feature learning and sample generation ability of denoising diffusion probability model to realize fast inverse design of airfoil under aerodynamic performance control. The research was carried out by the following steps: firstly, the basic airfoil data set was constructed based on UIUC airfoil database; using the basic airfoil data set as input, the unconditional diffusion model was trained; then, based on the unconditional diffusion model, a large number of new airfoils were generated, and an extended data set was established to increase the diversity of data; then the aerodynamic performance of airfoil was calculated by Xfoil airfoil analysis software. Taking airfoil and aerodynamic performance as inputs, the conditional diffusion model was trained, and the rapid airfoil reverse design based on this model was finally realized. The experimental results showed that the design of a single airfoil based on the denoising diffusion model took only 1.25 s, and the aerodynamic performance deviation from the tag airfoil was controlled within 6%. At the same time, the generated airfoil shapes were diverse, helping to provide conditions for more efficient airfoil design.
- inverse design of airfoil /
- airfoil coordinates /
- aerodynamic performance /
- denoising diffusion probability model /
- generative models

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图 1 去噪扩散模型原理

Figure 1. Principle of denoising diffusion model

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图 2 UDDPM的噪声估计网络结构

Figure 2. Noise estimation network structure of UDDPM

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图 3 CDDPM噪声估计网络结构

Figure 3. CDDPM noise estimation network structure

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图 4 本文翼型反设计方法流程

Figure 4. Flow of airfoil reverse design method in this paper

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图 5 部分翼型空间分布图

Figure 5. Spatial distribution map of partial airfoil

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图 6 翼型去噪生成过程

Figure 6. Generation process of airfoil denoising

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图 7 翼型集气动性能分布

Figure 7. Aerodynamic performance distribution of airfoil set

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图 8 两种方案训练损失曲线

Figure 8. Training loss curves of the two schemes

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图 9 3组气动指标对应的翼型设计结果

Figure 9. Airfoil design results corresponding to three groups of aerodynamic indexes

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图 10 设计翼型气动误差分析

Figure 10. Aerodynamic error analysis of design airfoil

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表 1 计算机配置

Table 1. Computer configuration

项目	内容
CPU	i9-10940x
GPU	RTX3090
操作系统	Windows 11
CUDA	11.3

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表 2 模型误差统计

Table 2. Model error statistics

类型	数据集	L_RMSE	L_MRE/%
方案1	训练集	0.00267	5.64
方案1	测试集	0.00313	6.23
方案2	训练集	0.00194	4.86
方案2	测试集	0.00222	5.14

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表 3 3组气动设计指标

Table 3. Three groups of aerodynamic design indexes

气动设计指标	气动系数	攻角/（°）
气动设计指标	气动系数	−2	−1	0	1	2
设计案例1	C_L	−0.0835	0.0075	0.0839	0.1538	0.2173
设计案例1	C_D	0.0367	0.0367	0.0385	0.0411	0.0448
设计案例2	C_L	−0.0867	0.0081	0.0942	0.1742	0.2485
设计案例2	C_D	0.0324	0.0322	0.0327	0.0338	0.0357
设计案例3	C_L	−0.0845	−0.0092	0.0639	0.1341	0.1984
设计案例3	C_D	0.0337	0.0336	0.0340	0.0350	0.0367

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