Reported herein are the experimental data for Marangoni condensation of steam-ethanol on a vertical plane surface. To clarify the effect of vapor pressure on Marangoni condensation, the heat flux and the vapor-to-surface temperature difference (surface subcooling) were measured for steam-ethanol mixtures over a wide range of compositions at vapor pressures of 84.5, 47.36, and 31.16 kPa. Seven condensation modes, smooth film, drop, filmdrop, streak, drop-streak, wavy-streak, and drop with tail, were observed in this experiment. The experimental results showed that heat transfer coefficients of vapor mixtures of different compositions increased with vapor pressure. The effect of vapor pressure in enhancing the condensation heat transfer coefficient was less for pure steam and extremely high compositions (22 and 51%) than for low and middle compositions (0.5,1, 5, and 10%). The maximum heat flux and heat transfer coefficient in the condensation characteristic curves were 2:37 MW=m 2 and 0:15 MW=m 2 K, respectively, for a vapor velocity of 2:0 m=s, and appeared at an ethanol vapor mass fraction of approximately 1% and a pressure of 84.53 kPa. The condensation heat transfer was enhanced approximately 7.5 times compared with pure steam. Nomenclature A = area, m 2 c = ethanol mass fraction, % h = heat transfer coefficient, kW=m 2 K M = mass flow rate, kg=s M L = mass flow rate of liquid, kg=s M V = mass flow rate of vapor, kg=s, M V vA sec V P = pressure, kPa q = heat flux, kW=m 2 r = latent heat, kJ=kg, r mix r e c r s 1 c T L = average temperature of liquid, K, T L T V T sur =2 T = temperature, K v = velocity of vapor, m=s X = direction of heat flux x = liquid mole fraction of ethanol y = vapor mole fraction of ethanol T = surface subcooling, K = thickness, m = thermal conductivity, kW=mK = density, kg=m 3 = surface tension, interfacial tension, N=m Subscripts crest = crest of condensate film e = ethanol L = liquid mix = mixture s = steam sat = saturated state sec = cross section sur = surface V = vapor valley = valley of condensate film W = water 1, 2, 3, 4, 5 = different layers along X direction Superscripts A, B, C = different values of pressure a, b, c = different testing positions in the copper plate av = average mix = mixture 0 = calculated results of universal quasi-chemical functional-group activity coefficients
An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames’. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.
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