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Nonintrusive velocity measurements using femtosecond laser electronic excitation tagging (FLEET) were conducted at 1 kHz for both air and nitrogen flows in a Mach 6 Ludwieg tube under ~600 Pa static pressure conditions. The signal from air and nitrogen were compared in freestream conditions and with a blunt cone model (7 deg half-angle). The long-laser time series operation relative to the facility operation time allowed the characterization of the mean velocity over the entire tunnel operation. The measurement uncertainty for both air and nitrogen FLEET were thoroughly examined. Our finding shows that the measured freestream velocity differed by ~1-2% from that of the compressible flow calculations. Additionally, the velocity near the leading bow shock was measured at six and three locations in nitrogen and air, respectively, to resolve the large velocity gradient at several locations along the shock layer. The velocity results for the freestream and bow shock were averaged in space and time. [ABSTRACT FROM AUTHOR]

The prediction of flowfield evolution can provide valuable reference information for the development of hypersonic technology. Flowfield prediction with the introduction of deep learning techniques is a promising method to provide future flowfield evolution in scramjet isolators. A multipath flowfield prediction model has been proposed to achieve flowfield prediction based on wall pressure sequence. The prediction model is mainly constructed with a convolutional neural network. An experimental dataset was built with supersonic experimental data under different evolution laws in an isolator. The flowfield prediction model is trained and validated using independent experimental data. The proposed model's prediction performance under different prediction spans is discussed in depth. The results demonstrate that the predicted flowfield is in good agreement with the ground truth, and the background wave and shock train structure are basically restored, even when the shock train leading edge changes intermittently. The influence of pressure sequence length on the proposed model's prediction performance is also analyzed. [ABSTRACT FROM AUTHOR]

Femtosecond laser electronic excitation tagging (FLEET) is a new non-seed molecular tagging velocity measurement method. Fluorescent light excited by femtosecond laser is used as tagging filament, and will disturb local temperature which may affect the flow field measurement precision. In this paper, the Rayleigh scattered of nanosecond laser is used to measure the ambient air temperature changing. The feasibility of this measurement method is proved. The key data processing of low signal to noise ratio (SNR) images including the filament segmentation, noise filtering, skeleton extraction of filament position is tested. The preliminary test on the heating process between 1 us and 800 us is demonstrated. The limitations of this measurement method due to the gas media condition changing are discussed briefly. [ABSTRACT FROM AUTHOR]