The maglev flying wind tunnel is a novel conceptual aerodynamic testing facility operating on the "object-moving and wind-static" principle, which poses new challenges for the localization and evaluation of moving sound sources in acoustic testing. In response, this paper proposes a fast time-domain beamforming (FTBF) algorithm suitable for moving sound source localization and evaluation based on its operational characteristics. Simulation studies on the localization and evaluation of typical moving sound sources, along with research on parameter influence, were conducted and experimentally validated. The results indicate that: (1) The FTBF algorithm improves computational speed by approximately 45 times compared to the conventional time-domain beamforming (CTBF) algorithm; (2) Regarding the issue that "except for simple harmonic sound sources of the same frequency, simple harmonic sound sources of different frequencies and white noise sound sources are affected by low-frequency sound sources (or components), resulting in low resolution in time-domain sound source localization," transforming the reconstructed time-domain sound pressure into the frequency domain and analyzing it by frequency band can significantly enhance the accuracy of sound source localization and evaluation; (3) As the moving speed of the sound source increases, the resolution of sound source imaging decreases at positions opposite to the direction of sound source motion and deviating from the center of the array; (4) Experimental results show that the self-noise of the moving platform interferes with the noise measurement of the test object, and this interference intensifies with increasing speed, even overwhelming the target signal. This study provides an efficient and accurate analytical method for acoustic measurements in moving test environments such as maglev wind tunnels, contributing positively to improving the reliability and engineering application value of complex aeroacoustic testing.

The aerodynamic force measurement technology during the acceleration phase of the motion model is one of the key technical challenges in the maglev flight wind tunnel. The aerodynamic force measurement balance is subjected to various strong interferences such as inertial forces in the acceleration section, which masks the measured aerodynamic force values. In order to restore the aerodynamic force signal under the harsh conditions of strong interference and low signal-to-noise ratio, this study proposed an aerodynamic force interference stripping algorithm based on deep learning. Firstly, the short-time Fourier transform was applied to the balance signal to determine the spectral characteristics of each interference, which was used as the input of the algorithm model. Then, an "encoder-decoder" architecture model was constructed to extract features from the complex signals measured by the balance and accurately reconstruct the desired aerodynamic force signal. After a comprehensive evaluation on the test set, the proposed algorithm demonstrated excellent performance in aerodynamic interference stripping, achieving a corresponding reconstruction accuracy of approximately 92.7% for the drag, lift, and pitching moment components. This study provides strong support for the aerodynamic force measurement in the future test environment of the maglev flight wind tunnel.