Mapping irrigated and rainfed wheat areas using high spatial–temporal resolution data generated by Moderate Resolution Imaging Spectroradiometer and Landsat

The detailed area and spatial distribution of irrigated and rainfed wheat can help forecast wheat yield and study water use efficiency. However, the similar spectral characteristics of irrigated and rainfed wheat make it difficult to separate them with low-spatial resolution or several high-spatial resolution images on the high heterogeneity of the southern Loess Plateau. To solve this challenge, this study used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM) to generate time series of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) at a 30-m resolution by fusing Moderate Resolution Imaging Spectroradiometer and Landsat data. Then, the phenological feature extracted from the predicted NDVI is combined with an auxiliary dataset to classify irrigated and rainfed wheat using the support vector machine classifier. An overall classification accuracy of 93.7% and a Kappa coefficient of 0.91 are achieved. Compared with corresponding high-resolution Google Earth images, the spatial distribution of the classification was consistent with actual land cover. This study demonstrates that the classification approach could classify irrigated and rainfed wheat in high heterogeneity regions and crops with smaller spectral characteristic differences. Moreover, it could be implemented across larger geographic regions</p

Numerical investigation of mode transition and hysteresis in a cavity-based dual-mode scramjet combustor

The effect of ethylene fuel equivalence ratio (ER) variation directions on combustion states in a dual-mode scramjet combustor was numerically investigated. The combustor employed transverse wall fuel injectors and downstream cavity flameholders without pilot fuel, which are fundamental components in many practical combustors. The isolator inflow Mach number was 3.1, and static pressure, stagnation pressure and stagnation temperature were 53 kPa, 2622 kPa and 1656 K, respectively. The ER was regulated abruptly in a piecewise constant manner, from 0.10 to 1.02, and then back to 0.10. A 3-D URANS method with a recognized two-step kinetics model was adopted. Results exhibited two combustion hysteresis loops, which indicated that different types of combustion mode transitions could result in hysteresis. The first was a hysteretic phenomenon between separated and shock-free scramjet modes based on steady quasi-one-dimensional combustor flow assumptions, and the second was between two different patterns of separated scramjet modes. Hysteresis mechanisms are elucidated from the viewpoint of combustion flow structures. The first hysteresis was attributed to flame stabilization mode transitions between the cavity shear-layer stabilized mode and the jet-wake stabilized mode, along with the transition hysteresis of a pre-combustion shock train's establishment and vanishment. The flame stabilization locations were greatly influenced by the flow separation states ahead of the fuel injectors, and the flow separations were in return determined by the flame distributions. The second hysteresis was attributed to transitions between weak-oscillation mode and intensive-oscillation mode with the transition hysteresis of shock reflection amount increase and decrease of the pre-combustion shock train structure, which were both in the jet-wake stabilized location. Flame in the low-speed region beside the separation bubbles ahead of the fuel injectors provided heat and hot radicals for downstream flame stabilization, and the pre-injector flame intensity greatly influenced the combustion oscillation states. (C) 2019 Elsevier Masson SAS. All rights reserved

Hysteresis of Shock Train Movement in the Isolator with a Ramp

Inside the dual-mode scramjet engine, the shock train will move to a new location when the backpressure changes. Few works focus on the response of the shock train position to the backpressure cyclic variation. This work aims to investigate the behavior of the shock train under such backpressure conditions. Experiments were carried out in a Mach 3 direct-connect facility. The isolator is equipped with a ramp that is used to improve the isolator performance. The static pressures along the wall centerlines were measured. The schlieren imaging was used to provide flow visualization. The results show that a significant hysteresis occurs in the shock train position during the backpressure cyclic variation process. It is found that a large-scale subsonic wake flow region forms behind the rampwhen the shock train reaches the ramp trailing edge. The capability of the ramp to retain the existence of the wake flow determines the occurrence of the hysteresis. The effects of the ramp height and width on the hysteresis were examined. Based on the experimental data, the oscillation characteristics were discussed by using wavelet analysis and cross-spectrum analysis. The coupled oscillation between the shock train oscillation and the backpressure oscillation was observed

Frictional Pressure Drop for Gas–Liquid Two-Phase Flow in Coiled Tubing

Coiled tubing (CT) is widely used in drilling, workover, completion, fracturing and stimulation in the field of oil and gas exploration and development. During CT operation, the tubing will present a gas–liquid two-phase flow state. The prediction of frictional pressure drop for fluid in the tube is an important part of hydraulic design, and its accuracy directly affects the success of the CT technique. In this study, we analyzed the effects of the gas void fraction, curvature ratio and fluid inlet velocity on frictional pressure drop in CT, numerically. Experimental data verified simulated results. Flow friction sensitivity analysis shows the frictional pressure drop reaches its peak at a gas void fraction of 0.8. The frictional pressure gradient increases with the increase in curvature ratio. As the strength of secondary flow increases with the increase in inlet velocity, the increased trend of gas–liquid two-phase flow friction is aggravated. The correlation of friction factor for gas–liquid two-phase flow in coiled tubing is developed by regression analysis of simulation results. The research results can support high quality CT hydraulics design, through which the success of CT operations can be guaranteed

Numerical Investigation of Flow Characteristics for Gas&ndash;Liquid Two&ndash;Phase Flow in Coiled Tubing

Coiled tubing (CT) is widely used for horizontal well fracturing, squeeze cementing, and sand and solid washing in the oil and gas industry. During CT operation, a gas&ndash;liquid two-phase flow state appears in the tubing. Due to the secondary flow, this state produces a more extensive flow-friction pressure loss, which limits its application. It is crucial to understand the gas&ndash;liquid flow behavior in a spiral tube for frictional pressure drop predictions in the CT technique. In this study, we numerically investigated the velocity distribution and phase distribution of a gas&ndash;liquid flow in CT. A comparison of experimental data and simulated results show that the maximum average error is 2.14%, verifying the accuracy of the numerical model. The gas and liquid velocities decrease first and then rise along the axial direction due to the effect of gravity. Due to the difference in the gas and liquid viscosity, i.e., the flow resistance of the gas and liquid is different, the gas&ndash;liquid slip velocity ratio is always greater than 1. The liquid velocity exhibits a D-shaped step distribution at different cross-sections of spiral tubing. The secondary-flow intensity, caused by radial velocity, increases along the tubing. Due to the secondary-flow effect, the zone of the maximum cross-section velocity is off-center and closer to the outside of the tube. However, under the combined action of centrifugal force and the density difference between gas and liquid, the variation in the gas void fraction along the tubing is relatively stable. These research results are helpful in understanding the complex flow behavior of gas&ndash;liquid two-phase flow in CT

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity

Southwestern China experienced a period of severe drought from September 2009 to May 2010. It led to widespread decline in the enhanced vegetation index (EVI) and gross primary productivity (GPP) in the springtime of 2010 (March to May). However, this study observed a spatial inconsistency between drought-impacted vegetation decline and the precipitation deficit. Significant aerosol loads that correspond to inconsistent areas were also observed during the drought period. After analyzing both MODIS GPP/NPP Collection 5 (C5) and the newer Collection 5.5 (C55) data, a large area observed to be experiencing GPP decline in the eastern part of the study area proved to be unreliable. Based on EVI data, after atmospherically contaminated data were screened from analysis, approximately 20% of the study area exhibited browning whereas 33% displayed no change or greening and the remaining area (approximately 47%) lacked sufficient data to document changing conditions. Correlation analysis showed that fire occurrences, aerosol optical depth, and precipitation anomalies during the two drought periods (from September to February and from March to May) all contributed to a decrease in GPP. C55 data remains vulnerable to aerosol contamination due to a much higher correlation coefficient with aerosol optical depth compared to C5 data. In the future, users of remotely sensed data should be cautious of and take into account impacts related to atmospheric contamination, even during drought periods. (C) 2014 Elsevier B.V. All rights reserved

Multisource Remote Sensing Monitoring and Analysis of the Driving Forces of Vegetation Restoration in the Mu Us Sandy Land

The Mu Us Sandy Land is a key region of man-made desert control and farmland to forest (grass) return in China. Despite global change and the strong influence of human activities, the vegetation in this region has been significantly improved and restored. In this study, multisource remote sensing data and multiple indicators were used to quantitatively monitor and evaluate the vegetation restoration status in this area. The driving factors were also analysed. The results show that in the past 20 years, nearly the entire Mu Us Sandy Land significantly and substantively recovered. The regional fractional vegetation cover increased, with an average annual growth rate of 0.59% and obvious spatial heterogeneity. The nine most important driving factors could comprehensively account for 58.38% of the spatial distribution of the vegetation coverage. Factors such as land use and land cover, the aridity index, and gross domestic product had the most significant impact, followed by precipitation and temperature. The results confirmed that the vegetation was restored and improved in the Mu Us Sandy Land and determined the main driving factors, which is helpful for vegetation restoration and ecological improvement on sandy land similar to the Mu Us Sandy Land

Surface Defect Detection of Hot Rolled Steel Based on Attention Mechanism and Dilated Convolution for Industrial Robots

In the manufacturing process of industrial robots, the defect detection of raw materials includes two types of tasks, which makes the defect detection guarantee its accuracy. It also makes the defect detection task challenging in practical work. In analyzing the disadvantages of the existing defect detection task methods, such as low precision and low generalization ability, a detection method on the basis of attention mechanism and dilated convolution module is proposed. In order to effectively extract features, a two-stage detection framework is chosen by applying Resnet50 as the pre-training network of our model. With this foundation, the attention mechanism and dilated convolution are utilized. With the attention mechanism, the network can focus on the features of effective regions and suppress the invalid regions during detection. With dilated convolution, the receptive field of the model can be increased without increasing the calculation amount of the model. As a result, it can achieve a larger receptive field, which will obtain more dense data and improve the detection effect of small target defects. Finally, great experiments are conducted on the NEU-DET dataset. Compared with the baseline network, the proposed method in this paper achieves 81.79% mAP, which are better results