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 and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.

As the most important measuring device in aerodynamic testing, the wind tunnel balance is used to measure the magnitude, direction, and point of the aerodynamic loads (forces and moments) acting on the test model. The accuracy of the measurement is directly related to the static calibration of the wind tunnel balance, which establishes the mapping relationship between the balance output signals and aerodynamic loads on the calibration equipment. This paper explores the possibility of improving the calibration performance of the strain-gauge balance in the calibration system AiBCS, developed by Institute of Mechanics of Chinese Academy of Science, using the convolutional neural network (CNN). The applicable conditions, validity, and reliability of CNN in the balance calibration are discussed and evaluated. Results obtained by the CNN-based calibration method and the traditional polynomial fitting method are analyzed and compared. It turns out that the CNN-based calibration method can effectively reduce the load interference between various balance components, yielding a significantly improved performance. Consequently, the deep-learning technology shows great application potential in calibrating wind tunnel balance.

Hypersonic high-enthalpy wind tunnels have been a challenge to ground tests in aerospace research area for decades and its test flow uniformity is one of the most important parameters for evaluating wind tunnel performances. Regarding to the performance requirement, theories and methods for designing hypersonic flow nozzles at high enthalpy conditions are quite difficult, but very interesting topics, especially when air molecule dissociations take place in wind tunnel test gas reservoirs. In this paper, fundamental theories and important methods for nozzle designs are briefly reviewed with the emphasis on two-dimensional axisymmetric nozzles for hypersonic high-enthalpy wind tunnels, including the Method of Characteristics (MOC), the graphic design method, the Sivells method, the theory for boundary correction, and the CFD-based design optimization methods. These theories and methods had been proposed based on several physical issues, respectively, which play important roles in nozzle flow expansion processes. These issues cover the expansion wave generation and reflection, the boundary layer development, the real gas effect of hypersonic high-enthalpy flows. Difficulties arising from applications of these methods in hypersonic high-enthalpy nozzle design are discussed in detail and the state of the art of the nozzle design technologies that have reached for decades is summarized with some brief comments. Finally, the prospect for the hypersonic nozzle design methods, and its numerical and experimental verifications are provided with from authors’ viewpoint for readers’ reference.