Shaojun NIE, Yunpeng WANG
DOI Number: XXX-YYY-ZZZ
Conference number: HiSST2024 – 0075
The aerodynamic force measurement conducted within shock tunnels bear paramount technological significance in the field of high-temperature aerodynamics. When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently. After processed by the intelligent models, high-precision aerodynamic force signals are obtained. Furthermore, the intelligent model method is applied to force measurement with the conical standard model in shock tunnels. When compared with results from the force measurement database for the cone model, the relative deviation is less than 2%, validating the feasibility of applying deep learning methods in pulse-type shock tunnel balance force tests. The deep integration of deep learning with pulse tunnel force tests is of paramount significance in enhancing performance metrics for hypersonic aerodynamics tests. This exploratory research will also further propel the intelligent development of force measurement in shock tunnels.