摘要
为解决目前绝大多数神经网络冰形预测方法只能针对特定翼型且不具备面向多翼型特征的普适性的问题,采用基于多模态融合的深度神经网络方法,以翼型截面图像与结冰工况参数作为输入,以二维冰形曲线傅里叶级数拟合参数作为输出,建立深度神经网络预测模型,实现了对任意对称翼型结冰特征的预测能力。结果表明:提出的模型可以准确地预测任意对称翼型几何特征条件下的结冰外形,冰形面积与最大冰厚等冰形主要参数预测误差均保持在10%以下。
A deep neural network method based on multimodal fusion was adopted to solve the problem that most current neural network ice prediction methods can only target specific airfoils and do not have the universality of multi-airfoil features.This method used the airfoil cross-section image and the icing condition parameters as inputs,and the two-dimensional ice curve Fourier series fitting parameters as outputs.This deep neural network prediction model realized the prediction ability of the ice characteristics of any symmetric airfoil.The results showed that the proposed model can accurately predict the ice shape under the geometric characteristics of any symmetrical airfoil.The prediction error of the main parameters of the ice shape,such as the ice area and the maximum ice thickness,was kept below 10%.
作者
屈经国
王强
彭博
易贤
QU Jingguo;WANG Qiang;PENG Bo;YI Xian(School of Computer Science,Southwest Petroleum University,Chengdu 610500,China;Key Laboratory of Icing and Anti/De-icing,China Aerodynamics Research and Development Center,Mianyang Sichuan 621000,China;State Key Laboratory of Aerodynamics,China Aerodynamics Research and Development Center,Mianyang Sichuan 621000,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2024年第1期49-56,共8页
Journal of Aerospace Power
基金
国家自然科学基金重点基金(12132019)
国家重大科技专项(J2019-Ⅲ-0010-0054)
国家自然科学面上基金(12172372)。
关键词
对称翼型
结冰预测
多模态融合
深度神经网络
傅里叶级数
symmetric airfoils
icing prediction
multimodal fusion
deep neural networks
Fourier series
作者简介
屈经国(1996−),男,硕士生,主要从事深度学习与结冰预测方面的研究。E-mail:xvyn@qq.com;通信作者:易贤(1977−),男,研究员,博士,主要从事航空宇航科学与技术方面的研究。E-mail:yixian_2000@163.com。
