The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified.

中文翻译：

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智能测力系统在冲击隧道中的应用

力测量系统（FMS）的惯性振动对飞机的力测量结果有很大的影响，特别是在高焓冲动设施（如冲击隧道）中进行的某些测试中。在冲击隧道中进行力测试时，由于惯性力，FMS的低频振动及其运动无法通过数字滤波解决，因为惯性力是由冲击隧道启动过程中的冲击流引起的。因此，本文着重于FMS性能的动态特性。提出了一种新的方法，即脉冲FMS的基于深度学习的单矢量动态自校准（基于DL的SV-DSC），以提高冲击隧道中气动力的测量精度。在这项研究中，使用深度学习技术来训练FMS的动态模型。应用具有简单结构的卷积神经网络描述动力学模型，从而从冲击隧道的测试结果中消除了低频振动信号。通过验证在冲击隧道中测得的力测试结果，当前训练的模型可以实现FMS平衡信号的智能处理。基于这种新的动态校准方法，力数据处理的可靠性和准确性得到了很好的验证。通过验证在冲击隧道中测得的力测试结果，当前训练的模型可以实现FMS平衡信号的智能处理。基于这种新的动态校准方法，力数据处理的可靠性和准确性得到了很好的验证。通过验证在冲击隧道中测得的力测试结果，当前训练的模型可以实现FMS平衡信号的智能处理。基于这种新的动态校准方法，力数据处理的可靠性和准确性得到了很好的验证。