基于改进BP神经网络的风洞天平静态校准研究

郜明川; 闵夫; 解真东; 杨彦广

doi:10.11729/syltlx20230149

基于改进BP神经网络的风洞天平静态校准研究

doi: 10.11729/syltlx20230149

郜明川^1,,
闵夫^{2, 3},
解真东^{2, 3},
杨彦广^{2, 3, ,}

1.
昆明理工大学信息工程与自动化学院，昆明　650500
2.
中国空气动力研究与发展中心，绵阳　621000
3.
中国空气动力研究与发展中心跨流域空气动力学重点实验室，绵阳　621000

详细信息

作者简介:
郜明川：（1997—），男，吉林长春人，硕士研究生。研究方向：智能数据分析与决策支持。E-mail：gmc670522173@163.com

通讯作者:
E-mail：yyyyyyggg@sina.com

中图分类号: TP183
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- 文章访问数: 18
- HTML全文浏览量: 7
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-02
- 修回日期: 2023-12-21
- 录用日期: 2023-12-27
- 网络出版日期: 2024-04-15

Research on static calibration of wind tunnel balances based on improved BP neural network

GAO Mingchuan^1
,,
MIN Fu^{2, 3},
XIE Zhendong^{2, 3},
YANG Yanguang^{2, 3
, ,}

1.
Kunming University of Science and Technology, School of Information Engineering and Automation, Kunming　650500, China
2.
Hypervelocity Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang　621000, China
3.
Laboratory of Aerodynamics in Multiple Flow Regimes of China Aerodynamics Research and Development Center, Mianyang　621000, China

摘要

摘要: 针对风洞天平静态校准传统校准模型非线性误差较大的问题，采用BP神经网络建立了天平校准模型。三分量天平的BP神经网络模型为典型三层神经网络（“3–7–3”结构）；BP神经网络模型校准精准度满足天平静态校准合格指标，轴向力和俯仰力矩分量校准性能优于传统模型，法向力分量校准性能则略低于传统模型。针对BP神经网络存在的不足，采用经混合策略改进的蝴蝶算法优化BP神经网络的初始权值和阈值，优化后的BP神经网络收敛精度和收敛速度得到提高。使用三分量应变天平校准数据进行了仿真实验，以天平输出信号值和天平加载载荷值作为输入和输出构建BP神经网络。传统校准模型、BP神经网络校准模型、蝴蝶算法优化BP神经网络校准模型的仿真实验结果对比表明：使用优化BP神经网络模型拟合天平校准公式，其校准性能比传统校准模型提高70%～90%，可有效消除传统校准模型非线性误差，显著提高天平静态校准精准度。
- 风洞天平 /
- 静态校准 /
- BP神经网络 /
- 蝴蝶算法 /
- 非线性拟合
Abstract: Addressing the issue of relatively large nonlinear errors in traditional calibration models for static calibration of the wind tunnel balance, researchers established a balance calibration model using the BP neural network. The BP neural network model for the three-component balance is a typical three-layer neural network, specifically manifested as a “3–7–3” structure. The precision of the BP neural network model meets the qualified criteria for static balance calibration. Its calibration performance in axial force and pitching moment components surpasses that of the traditional model, although it is slightly inferior in the normal force component. To compensate for the deficiencies of the BP neural network, an improved Butterfly Optimization Algorithm with a hybrid strategy is introduced to optimize the initial weights and thresholds. The optimized BP neural network exhibits enhanced convergence accuracy and speed. The present study utilizes the calibration data of the three-component strain gauge balance from simulation experiments, with the balance output signal values and loading load values as inputs and outputs for constructing the BP neural network. A comparison is made among between the simulation results of the traditional calibration model, the BP neural network calibration model, and the Butterfly Optimized Algorithm-BP neural network calibration model. The results indicate that the optimized BP neural network model fitting the balance calibration formula improved calibration performance by 70% – 90% compared to the traditional calibration model. It effectively eliminates the nonlinear errors of the traditional calibration model and significantly improves the precision of static balance calibration.
- wind tunnel balance /
- static calibration /
- BP neural network /
- Butterfly Optimized Algorithm (BOA) /
- nonlinear fitting

HTML全文

图 1 “3–5–3”结构BP神经网络

Figure 1. “3–5–3” structure BP neural network

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图 2 改进后的BOA搜索原理

Figure 2. Search principle of the improved BOA

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图 3 神经网络拟合原理

Figure 3. Principle of neural network fitting

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图 4 TSBOA–BP模型训练流程图

Figure 4. TSBOA–BP model training flowchart

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图 5 天平三分量不同BP神经网络模型校准结果对比

Figure 5. Comparison of calibration results of different BP neural network models for three components of the balance

下载: 全尺寸图片幻灯片

图 6 传统校准模型和神经网络校准模型校准结果对比

Figure 6. Comparison of calibration results between traditional balance calibration model and neural network calibration model

下载: 全尺寸图片幻灯片

表 1 传统校准模型的校准数据

Table 1. Calibration data of traditional calibration models

指标	X	Y	M_Z
RMSE	1.900 g	0.465 g	84.661 g·mm
精准度	0.155% FS	0.034% FS	0.303% FS

下载: 导出CSV

表 2 神经网络模型的校准数据

Table 2. Calibration data of neural network models

分量	指标	BP	BOA–BP	TSBOA–BP
X
	RMSE	0.689 g	0.453 g	0.257 g
	准度	0.057% FS	0.038% FS	0.021% FS
Y
	RMSE	0.502 g	0.326 g	0.159 g
	准度	0.036% FS	0.023% FS	0.011% FS
M_Z
	RMSE	35.217 g·mm	29.405 g·mm	19.962 g·mm
	准度	0.126% FS	0.11% FS	0.071% FS

下载: 导出CSV

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