Wideband RCS reduction based on a simple chessboard metasurface

To avoid being detected by radar, it is necessary to reduce stealthy military platforms' radar cross section (RCS). The operation of overlaying the metasurface (MS) on the targets is a good solution. A simple chessboard MS structure that can achieve low RCS over a large bandwidth is proposed. Only one unit cell is used to construct the MS. First, the unit cell working in 0.5 and 1−λ modes is designed to achieve a stable phase difference of 180° for y- and x-polarized waves. Then, the unit cells and rotated ones are used to form a chessboard structure with different distributions. The compared results show that the chessboard MS with 2 × 2 quadrants can facilitate the widest 10 dB RCS reduction band of 111% and the largest RCS reduction. The proposed structure exhibits excellent RCS reduction even when irradiated by y- and x-polarized waves at an oblique incidence of 30°

Thermochemical non-equilibrium flow characteristics of high Mach number inlet in a wide operation range

The high-temperature non-equilibrium effect is a novel and significant issue in the flows over a high Mach number (above Mach 8) air-breathing vehicle. Thus, this study attempts to inves-tigate the high-temperature non-equilibrium flows of a curved compression two-dimensional scram -jet inlet at Mach 8 to 12 utilizing the two-dimensional non-equilibrium RANS calculations. Notably, the thermochemical non-equilibrium gas model can predict the actual high-temperature flows, and the numerical results of the other four thermochemical gas models are only used for com-parative analysis. Firstly, the thermochemical non-equilibrium flow fields and work performance of the inlet at Mach 8 to 12 are analyzed. Then, the influences of high-temperature non-equilibrium effects on the starting characteristics of the inlet are investigated. The results reveal that a large sep-aration bubble caused by the cowl shock/lower wall boundary layer interaction appears upstream of the shoulder, at Mach 8. The separation zone size is smaller, and its location is closer to the down-stream area while the thermal process changes from frozen to non-equilibrium and then to equilib-rium. With the increase of inflow Mach number, the thermochemical non-equilibrium effects in the whole inlet flow field gradually strengthen, so their influences on the overall work performance of the high Mach number inlet are more obvious. The vibrational relaxation or thermal non -equilibrium effects can yield more visible influences on the inlet performance than the chemical non-equilibrium reactions. The inlet in the thermochemical non-equilibrium flow can restart more easily than that in the thermochemical frozen flow. This work should provide a basis for the design and starting ability prediction of the high Mach number inlet in the wide operation range. (c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/)

Influence of high temperature non-equilibrium effects on Mach 12 scramjet inlet

To better understand the high temperature non-equilibrium effects of a high Mach number (above Mach 8) scramjet inlet, the thermal and chemical non-equilibrium flow of Mach 12 two-dimensional inlet is numerically analyzed by using the thermochemical non-equilibrium gas model including a two-temperature model and air chemical reactions. The thermal equilibrium flow is simulated by using the models of thermally perfect gas and chemical non-equilibrium gas. It is found that the thermal non-equilibrium effects are relatively strong near the cowl shock and in the outer layer of the boundary layer, and gradually weaken in the downstream zone of the cowl shock. The chemical non-equilibrium effects and thermal equilibrium flows mainly exist in the high-temperature region of the boundary layer. Compared with the chemical non-equilibrium gas, the oxygen dissociation reaction near the lower wall for the thermochemical non-equilibrium gas is weaker in the external compression section; It is stronger due to fully excited vibration energy, in the internal compression section. Compared with the other models, the inlet compression ability for the thermochemical non-equilibrium gas is higher, the coefficient of mass flow and total pressure recovery for it are lower. Hence, the high temperature non-equilibrium effects can not be ignored in the design of the high Mach number scramjet inlet

Numerical study of high temperature non-equilibrium effects of double-wedge in hypervelocity flow

The high temperature non-equilibrium effects of shock wave interaction and shock wave/boundary layer interaction are important issues for hypervelocity flows. The models of thermochemical non-equilibrium gas (TCNEG), thermal non-equilibrium chemical frozen gas (TNCFG), chemical non-equilibrium gas (CNEG), and thermally perfect gas are used to simulate the double-wedge flows with a total enthalpy of 8 MJ/kg in this study. The unsteady two-temperature Naiver-Stokes equations in the laminar and turbulence flows are solved using the finite volume method. For laminar flow, the shock structures and the heat flux peak for TCNEG model at 170 mu s are agreed better with the experiment result compared to reference studies. There are different size vortices in the separation zones, which causes the distributions of the wall heat flux oscillate irregularly. The thermal non-equilibrium effects are the most intense near the attached shock and detached shock, and the degree of oxygen dissociation is the strongest in the subsonic zone near the slip-line. For turbulence flow, the shock structures for the four models are close to Edney's IV interaction. The separation shock position for the TNCFG model is the most upstream, and that for the CNEG model is quite different from the TCNEG model. The intensity of the reflected shocks on the back wedge and its nearby shock interaction largely determine the peak values of the heat flux for the four models

AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion

To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like architecture and incorporates key refinements to enhance network architecture and loss functions. Regarding the network architecture, the proposed two-branch adjacent feature fusion module, called AFSF, expands the number of channels to fuse the feature channels of several adjacent convolutional layers in the first half of the AFSFusion, enhancing its ability to extract, transmit, and modulate feature information. We replace the original rectified linear unit (ReLU) with leaky ReLU to alleviate the problem of gradient disappearance and add a channel shuffling operation at the end of AFSF to facilitate information interaction capability between features. Concerning loss functions, we propose an adaptive weight adjustment (AWA) strategy to assign weight values to the corresponding pixels of the infrared (IR) and visible images in the fused images, according to the VGG16 gradient feature response of the IR and visible images. This strategy efficiently handles different scene contents. After normalization, the weight values are used as weighting coefficients for the two sets of images. The weighting coefficients are applied to three loss items simultaneously: mean square error (MSE), structural similarity (SSIM), and total variation (TV), resulting in clearer objects and richer texture detail in the fused images. We conducted a series of experiments on several benchmark databases, and the results demonstrate the effectiveness of the proposed network architecture and the superiority of the proposed network compared to other state-of-the-art fusion methods. It ranks first in several objective metrics, showing the best performance and exhibiting sharper and richer edges of specific targets, which is more in line with human visual perception. The remarkable enhancement in performance is ascribed to the proposed AFSF module and AWA strategy, enabling balanced feature extraction, fusion, and modulation of image features throughout the process

An Ultra-Wideband MIMO Bowl-Shaped Monopole Antenna with Sturdy and Simple Construction

Anultra-wideband (UWB) bowl-shaped monopole antenna with a sturdy, simple, and lightweight structure is proposed, and then is used to compose the 3 × 3 multiple input multiple output (MIMO) antenna. The wide bandwidth is determined by the outline of the monopole, which has a quarter wavelength and high-order modes. The inner part of the bowl-shaped monopole is removed for a light weight. The simulated and measured results show that an ultra-wide band of 2.3–8.1 GHz (5.8 GHz, 111.5%) and a high isolation of greater than 20 dB between the antenna elements of the MIMO antenna can be achieved

Research on coal and rock recognition model based on improved 1DCNN

With the acceleration of intelligent construction of coal mines, efficient recognition of coal and rock has become a technical problem to be solved urgently in intelligent coal mining. The existing coal and rock recognition methods under complex coal mine geological conditions have problems of low precision, poor universality and are difficult to apply in engineering. In order to solve the above problems, a coal and rock recognition model based on improved 1-dimensional convolutional neural network (1DCNN) is proposed. Based on the 1DCNN, a plurality of continuous convolution layers are used for extracting one-dimensional vibration signal features. The global average pool (GAP) layer is used for replacing the full connection layer. The model training parameters are reduced, and computing resources are saved. At the same time, a cosine annealing attenuation method with a linear hot start is adopted for optimizing the learning rate. Therefore, the model training is prevented from falling into a local minimum region, and the training quality is improved. In order to intuitively describe the feature extraction process and classification capability of the improved 1DCNN model for coal and rock cutting vibration data, the t-distributed stochastic neighbor embedding (t-SNE) manifold learning algorithm is used to visually analyze the feature learning process of the model. The results show that the improved 1DCNN model can realize the recognition of coal and rock cutting states well through feature learning layer by layer. Based on the measured vibration data obtained in the process of coal and rock cutting of the MG 650/1590-WD shearer in a mine in Shaanxi province, the model is trained and the result shows that the accuracy of the improved 1DCNN model is 99.91% on the training set and 99.32% on the test set. The model can be directly used to classify the original vibration signals of the shearer in coal and rock cutting, and can effectively identify the cutting state of coal and rock. Compared with traditional machine learning, ensemble learning and the unmodified 1DCNN model, the improved 1DCNN model has obvious advantages. The average recognition accuracy rate reaches 99.56%. The calculation cost is greatly saved, and the model recognition speed is improved