A Fast Calculation Method of Gas Infrared Radiation Characteristics at High Temperature based on Radial Basis Function Neural Network

被引：0

作者：

Meng, Xiaying ^{[1
]}

Du, Jun ^{[1
]}

Wang, Biao ^{[1
]}

Zhang, Yutao ^{[1
]}

Gu, Dandan ^{[1
]}

Qiu, Jian ^{[2
]}

机构：

[1] Sci & Technol Electromagnet Scattering Lab, Shanghai, Peoples R China

[2] Aero Engine Corp China, Commercial Aircraft Engine, Shanghai, Peoples R China

来源：

2021 OPTOELECTRONICS GLOBAL CONFERENCE (OGC 2021) | 2021年

基金：

中国国家自然科学基金;

关键词：

gas infrared radiation; radial basis function neural network; fast algorithm; absorption coefficient;

D O I：

10.1109/OGC52961.2021.9654421

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The radiation characteristics of high temperature gas are of great significance for infrared target detection. In this paper, a calculation method of high temperature gas radiation characteristics with high calculation efficiency is proposed. The calculation model of CO2 absorption coefficient at 2001-2450 cm(-1) band is established by using radial basis function neural network (RBFNN). In RBFNN model, the most accurate line by line (LBL) method is utilized to generate training samples. The input parameters are gas temperature, pressure and component concentration, and the output parameter is absorption coefficient. The results show that, compared with LBL method, RBFNN model has higher accuracy and is insensitive to input. After RBFNN training, RBFNN has higher computational efficiency while ensuring computational accuracy.

引用

页码：82 / 86

页数：5

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