循环神经网络在智能天平研究中的应用
APPLICATION OF RECURRENT NEURAL NETWORK IN RESEARCH OF INTELLIGENT WIND TUNNEL BALANCE
查看参考文献32篇
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文摘
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激波风洞地面试验对高超声速飞行器高焓气动特性研究至关重要,而高精度气动力测量是其中的关键技术.在脉冲型激波风洞中进行测力试验时,风洞起动时流场瞬间建立,对测力系统会产生较大的冲击.测力系统在瞬时冲击作用下受到激励,系统的惯性振动信号在短时间内无法快速衰减,天平的输出信号中会包含惯性振动干扰量,导致脉冲型风洞测力试验精准度的进一步提高遇到瓶颈.为了解决短试验时间内激波风洞快速准确测力问题,发展高精度的动态校准技术是提升受惯性干扰天平性能的关键方法.因此,本文采用循环神经网络对天平动态校准数据进行训练和智能处理,旨在消除输出动态信号中的振动干扰信号.本文对该方法进行了误差分析,验证了该方法的可靠性,并将该方法应用于激波风洞测力试验中,切实有效降低了惯性振动对天平输出信号的干扰影响.根据智能模型的样本验证分析,各分量载荷相对误差比较小,其中高频轴向力分量处理结果的相对误差约1%.在风洞试验数据验证中,也得到了比较理想的结果,同时与卷积神经网络模型处理的结果进行了对比分析. |
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其他语种文摘
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The shock tunnel ground test is vitally important to the research of the high-enthalpy aerodynamic characteristics of hypersonic vehicles,and the high-accuracy aerodynamic measurement is the key technology.When a force measurement test is conducted in an impulse shock tunnel,the flow field is established instantly after the starting process of shock tunnel,at this time,the great impact loads are acting on the force measurement system.The force measurement system is excited under the action of instantaneous impact,and the inertial vibration signal of the system cannot be rapidly attenuated during the short test time.The output signal of the balance will contain the interference due to the inertial vibration,which leads to a bottleneck in the further improvement of the accuracy of the transient force test.In order to improve the force measurement accuracy in the short-duration shock tunnel,the development of highaccuracy dynamic calibration technology is the key method to improve the performance of balance affected by inertial interference.Therefore,in this paper,recurrent neural network is used to train and intelligently process the balance dynamic calibration data,aiming to eliminate the vibration interference signals in the output dynamic signals.The error analysis of the current method is carried out,and the reliability of the current method is verified.The method is applied to the data processing of force test obtained in shock tunnel,and the effect of inertial vibration on the output signal of the balance is effectively reduced.According to the sample verification analysis of the intelligent model,the relative error of each component load is relatively small,where the case of high-frequency axial force component is about 1%.In the verification of wind tunnel force test data,the good results are also obtained,which are compared with those processed by the convolutional neural network model. |
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来源
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力学学报
,2021,53(8):2336-2344 【核心库】
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DOI
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10.6052/0459-1879-21-168
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关键词
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气动力测量
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风洞天平
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动态校准
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人工智能
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循环神经网络
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地址
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1.
中国科学院力学研究所, 高温气体动力学国家重点实验室, 北京, 100190
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中国科学院大学工程科学学院, 北京, 100049
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中国科学院大学人工智能学院, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0459-1879 |
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学科
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力学 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:7050810
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