Lithium-ion batteries have become the first choice for electric vehicle power batteries and energy storage power plants due to their good output characteristics and high energy density. Taking the lithium battery as t...
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To further enhance the impact resistance of honeycomb sandwich beams, an auxetic honeycomb core is proposed by introducing rhombic structure into the concave hexagonal honeycomb, thus obtaining a new auxetic honeycomb...
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To further enhance the impact resistance of honeycomb sandwich beams, an auxetic honeycomb core is proposed by introducing rhombic structure into the concave hexagonal honeycomb, thus obtaining a new auxetic honeycomb core sandwich beam (NDH). The bending resistance of NDH is investigated through numerical simulations. The analysis confirms that the NDH has better bending resistance and energy-absorbing properties than the internally concave hexagonal honeycomb sandwich beam (REH). Furthermore, the stable plastic deformation and high energy absorption is closely related to the bearing position and structural parameters. When the punch is directly above the new honeycomb cell (T-position), NDH shows better bearing capacity. In addition, increasing the panel thickness is conducive to enhancing the bending resistance of sandwich beam. The panel thickness arrangement with t(f)/t(b) > 1 has better bending resistance than other arrangements under the same total front and rear panel thickness. The composite proportional assessment method (COPRAS) is further used to set affect weights of structure parameters on its bending resistance. It is proved that the bending resistance of NDH may be rapidly enhanced by thickening new honeycomb cells. This research provides some perspectives and methods for the future research on the impact resistance for sandwich panels with novel honeycomb core.
To investigate the protective capacity and mechanism of polyurea-coated auxetic honeycomb sandwich structures under impact loading. The mechanical properties of a sandwich panel under the action of a cylindrical punch...
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To investigate the protective capacity and mechanism of polyurea-coated auxetic honeycomb sandwich structures under impact loading. The mechanical properties of a sandwich panel under the action of a cylindrical punch is numerically simulated by LS-DYNA. Firstly, the impact of different coating positions on the protective properties of sandwich panels is analyzed and concluded which coating method is most effective. Then, the mechanism of the impact resistance of sandwich panels strengthened by the thickness, grading and orientation of the auxetic honeycomb cores is discussed by outline volume analysis. Finally, a multi-objective optimization of the sandwich panel's internal concave hexagonal structural parameters with the best impact resistance is carried out. It is shown that the coating of polyurea elastomers can effectively increase the impact protection performance of sandwich panels. The best impact resistance is achieved by the back side coated sandwich panel (type B). Increasing the wall thickness of auxetic honeycomb and the thickness of the upper graded honeycomb core can effectively increase the impact resistance of the type B sandwich panel. Optimized type B sandwich panel has a 6.2% reduction in maximum deflection (D) compared to the unoptimized version. The findings of this research provide a reference for the study and design of polyurea-coated sandwich panels.
The SEIARN model is often used to predict infectious diseases, but the parameter estimation of the SEIARN model has always been one of the difficulties in this research. Traditional statistical and empirical estimatio...
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The SEIARN model is often used to predict infectious diseases, but the parameter estimation of the SEIARN model has always been one of the difficulties in this research. Traditional statistical and empirical estimation methods have certain limitations. To this end, we designed an SSA‐based SEIARN parameter adaptive model, optimized parameters through SSA, and used historical epidemic data in hebei, Tianjin, and Beijing for simulation. Experiments finally proved the feasibility of the algorithm.
A new scintillator-based fast ion loss detector (FILD) has been devised for the HL-2A tokamak, with the objective of discerning the temporal evolution of energy and pitch angle among lost fast ions. A code named FILD ...
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A new scintillator-based fast ion loss detector (FILD) has been devised for the HL-2A tokamak, with the objective of discerning the temporal evolution of energy and pitch angle among lost fast ions. A code named FILD Simulation (FSC) has been developed to assist in the design of the detector head and the interpretation of experimental data. By optimising the geometric design, the resolutions of energy and pitch angle are optimised to 5 keV and 3 degrees, respectively. A branched imaging fiber bundle is used to relay the fluorescence image on the scintillator to two distinct instruments. One of these is a high-speed camera, while the other is a silicon photomultiplier tube (SiPM) array with a sampling rate of up to 1 MSamples/s. This configuration enables the attainment of superior temporal and spatial resolution, as well as high photon sensitivity.
Agile-satellite mission planning is a crucial issue in the construction of satellite constellations. The large scale of remote sensing missions and the high complexity of constraints in agile-satellite mission plannin...
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Agile-satellite mission planning is a crucial issue in the construction of satellite constellations. The large scale of remote sensing missions and the high complexity of constraints in agile-satellite mission planning pose challenges in the search for an optimal solution. To tackle the issue, a dynamic destroy deep-reinforcement learning (D3RL) model is designed to facilitate subsequent optimization operations via adaptive destruction to the existing solutions. Specifically, we first perform a clustering and embedding operation to reconstruct tasks into a clustering graph, thereby improving data utilization. Secondly, the D3RL model is established based on graph attention networks (GATs) to enhance the search efficiency for optimal solutions. Moreover, we present two applications of the D3RL model for intensive scenes: the deep-reinforcement learning (DRL) method and the D3RL-based large-neighborhood search method (DRL-LNS). Experimental simulation results illustrate that the D3RL-based approaches outperform the competition in terms of solutions' quality and computational efficiency, particularly in more challenging large-scale scenarios. DRL-LNS outperforms ALNS with an average scheduling rate improvement of approximately 11% in Area instances. In contrast, the DRL approach performs better in World scenarios, with an average scheduling rate that is around 8% higher than that of ALNS.
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