罗成,万军,丁晨,孙永生.基于热网络模型的导弹贮存温度预计方法[J].装备环境工程,2017,14(6):89-92. LUO Cheng,WAN Jun,DING Chen,SUN Yong-sheng.Predicting Method of Missile Storage Temperature Based on Thermal Network Model[J].Equipment Environmental Engineering,2017,14(6):89-92.
基于热网络模型的导弹贮存温度预计方法
Predicting Method of Missile Storage Temperature Based on Thermal Network Model
投稿时间:2016-12-14  修订日期:2017-06-15
DOI:10.7643/ issn.1672-9242.2017.06.017
中文关键词:  温度预计  贮存温度  热网络  高温极值
英文关键词:temperature prediction  storage temperature  thermal network  extreme high temperature
基金项目:
作者单位
罗成 中航工业综合技术研究所,北京 100028 
万军 中国人民解放军第95989部队,北京 100076 
丁晨 中航工业综合技术研究所,北京 100028 
孙永生 中国人民解放军第95989部队,北京 100076 
AuthorInstitution
LUO Cheng China Aero-Polytechnology Establishment, Beijing, 100028, China 
WAN Jun Troops 96989 of PLA, Beijing 100076, China 
DING Chen China Aero-Polytechnology Establishment, Beijing, 100028, China 
SUN Yong-sheng Troops 96989 of PLA, Beijing 100076, China 
摘要点击次数:
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中文摘要:
      目的 提供一种预计空空导弹的外场贮存温度的方法,以确定空空导弹的环境适应性要求。方法 基于热网络模型,预计不同环境条件下导弹的贮存温度与日高温极值。考虑各种环境因素对导弹热环境的综合影响,建立导弹内外各节点的能量守恒方程。将实测数据代入方程,利用最小二乘法拟合方程系数建立预计模型,将实测结果与模型的预计结果进行对比以验证模型的准确性。结果 热网络模型预计的温度结果和实测结果较为吻合,95%置信度下实测数据与预计的日高温极值误差均值为0.3 ℃。结论 该方法对舱内温度的预计结果较好,可用以确定空空导弹的环境适应性要求。
英文摘要:
      Objective To provide a method for predicting missile storage temperature and determine requirement on environmental suitability of air-to-air missiles. Methods Based on thermal network model, storage temperature and extreme high temperature of missiles under different environmental conditions were predicted. Energy conservation equation for each internal and external node of a missile was established by considering influences of different environmental factors on the thermal condition of missile. Then, the prediction model was built by applying the measured data to the equations and obtaining the coefficients using the least square method. At last, the measured result and predicted result were compared to verify the veracity of the model. Results The temperature result predict for the thermal network model is basically in line with the measured result. The mean error between data measured at 9% of confidence coefficient and extreme high temperature predicted is 0.3 ℃. Conclusion This method could predict the temperature in cabin properly, and can be used to determine the environmental adaptability of air to air missiles.
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