基于随机配置网络的机载电子吊舱多工况热模型

doi:10.11949/0438-1157.20191082

摘要/Abstract

摘要：

机载电子吊舱是搭载多功能机载电子设备的主要平台，能够显著提升战机性能。然而，不断增加的电子设备功率和高度低气压力飞行环境会造成吊舱内恶劣的热环境，严重影响电子设备的可靠性。因此，十分有必要建立准确的电子吊舱热模型，用于预测不同飞行工况下的电子设备热响应。综合热网络分析思想和随机网络算法思想，提出一种基于随机配置网络的热模型建立方法，并通过采用冲压空气冷却系统的电子吊舱实验数据加以验证。为了建立准确的舱内温度响应模型，通过传热机制将高温贮存、高温工作、低温贮存、低温故障和低温工作工况下的实验数据分为3组并分别用于建立设备贮存热模型、设备工作热模型和综合热模型，利用热网络分析获取随机配置网络的有效输入，采用四折交叉验证和灰度图分析综合确定了3个热模型的超参数。建模结果表明：3个热模型的范围序列可统一为[1～40]，最大隐含层节点数可分别设为6、9、11，设备温度拟合效果较好，仅在边界条件约束下进行多工况全过程的电子设备温度预测，预测误差在3.512℃内。总体看来，该热建模方法从数据挖掘的角度较为简单、准确、快速地描述了电子设备热关系，可用于开展预期飞行环境下的机载电子吊舱温度预测，用于评估热管理系统的性能。

关键词: 机载电子吊舱, 热响应, 热网络, 随机配置网络, 热力学, 数学模拟, 算法

Abstract:

Avionics pod is a main carrier for multifunctional airborne electronic equipment, which effectively improves the performance of fighter. Increasing power of electronic equipment and low-pressure flight environment can exacerbate thermal environment in pod and can further affects the reliability of electronic equipment, so it is important to predict the thermal response of equipment under different flight conditions. In this paper, a thermal modeling method using thermal network analysis and stochastic configuration network is proposed and is further verified by experimental data of avionics pod with a ram air cooling system. The data of five conditions (high temperature storage, high temperature working, low temperature storage, low temperature accident and low temperature working) is divided into three groups according to heat transfer mechanism and is used to establish the storage thermal models, the working thermal models and the comprehensive thermal models, respectively. Thermal network analysis is used to obtain the input of network. Four-fold cross-validation and gray-scale analysis are used to determine hyper-parameters. The results show that the range sequence can be unified to [1—40] and the maximum number of hidden nodes of three thermal models can be set to 6, 9, and 11, respectively. The modeling results are positive, and the prediction error of electronic equipment temperature in the whole process of multi-conditions is within 3.512℃. Thus, the thermal modeling method that describes the thermal relationship of electronic equipment by data mining can be used to predict the avionics pod temperature in expected flight environment and evaluate the performance of thermal management system.

Key words: avionics pod, thermal response, thermal network, stochastic configuration network, thermodynamics, mathematical modeling, algorithm

中图分类号:

V 216.5⁺1

张洁, 庞丽萍, 曲洪权, 王天博. 基于随机配置网络的机载电子吊舱多工况热模型[J]. 化工学报, 2020, 71(S1): 441-447.

Jie ZHANG, Liping PANG, Hongquan QU, Tianbo WANG. Multi-condition thermal models of avionics pod using stochastic configuration network[J]. CIESC Journal, 2020, 71(S1): 441-447.

图/表 9

图1 实验装置1—环境模拟舱；2—模拟气源；3—机载电子吊舱；4—吊舱电子设备；5—吊舱环控系统；6—数据采集系统

Fig.1 Experimental system diagram

表1 实验工况条件设置

Table 1 Condition setting

实验工况	模拟舱设定温度/℃	吊舱设备状态	环控系统状态
高温贮存	70	关	关
高温工作	70	开	开
低温贮存	-55	关	关
低温故障	-55	开	关
低温工作	-55	开	开

图2 设备温度变化

Fig.2 Temperature curve

图3 随机配置网络结构

Fig.3 Structure of stochastic configuration network

图4 建模流程

Fig.4 Modeling process

图5 热模型超参数确定

Fig.5 Hyper-parameters determination of thermal models

图6 综合热模型建模结果

Fig.6 Result of comprehensive thermal model

表2 多工况全过程的预测结果

Table 2 Prediction result under whole process of multi-condition

指标	E₁	E₂	E₃	E₄	E₅
最大误差/℃	3.512	1.106	1.676	2.076	0.860
RMSE/℃	1.287	0.507	0.990	1.070	0.506

图7 多工况全过程的预测温度曲线

Fig.7 Prediction temperature curves under whole process of multi-condition

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