This experiment measured the instantaneous temperature and velocity field synchronously in nonisothermal turbulent boundary layer in a rotating straight channel with a parallel-array hot-wire probe. the Reynolds number based on the bulk mean velocity (U) and hydraulic diameter (D) is 19000, and the rotation numbers are 0, 0.07, 0.14, 0.21 and 0.28. The mean velocity u and mean temperature T as well as their fluctuating quantity u' and T' were measured at three streamwise locations (x/D = 4.06, 5.31, 6.56). A method for temperature-changing calibration with constant temperature hot-wire anemometers was proposed. it achieved the calibration in operational temperature range (15.5 °C-50 °C) of the hot-wire via a home-made heating section. The measurement system can obtain the velocity and temperature in a non-isothermal turbulent boundary layer at rotating conditions. The result analysis mainly contains the dimensionless mean temperature, temperature fluctuation as well as its skewness and flatness and streamwise turbulent heat flux. For the trailing side, the rotation effect is more obvious, and makes the dimensionless temperature profiles lower than that under static conditions. The dimensionless streamwise heat flux shows a linear decrease trend in the boundary layer. It is hoped that this research can improve our understanding of the flow and heat transfer mechanism in the internal cooling passages of turbine rotor blades.
A novel recognition method is proposed to relieve the heavy requirement of training samples in the radar High Resolution Range Profile (HRRP) target recognition. Firstly, the statistical characteristics of HRRP’s frequency spectrum amplitude (FSA) are analyzed. Then a Linear Gaussian
Mixture Dynamic Model (LGMDM) is proposed to describe the stationarity and multi-modality of the FSA. Afterwards, the Expectation Maximization (EM) algorithm is derived for model parameter estimation. Finally, experimental results show the proposed method can achieve satisfactory recognition
accuracy and rejection performance with only a few training samples.
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