Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.
The dissolution and diffusion of intermediate product polysulfide in lithium-sulfur battery (Li-S) between electrodes pose great challenge for the further application of Li-S batteries. Herein, we first prepared nano SiO2 blending polyetherimide (PEI) separator modified with acetylene black/polyvinylpyrrolidone (PVP) coating layer. Joint effect of acetylene black and PVP on polysulfide contribute to outstanding cycle performance. The Li-S cell with CP55 separator presents superior cycle performance (650 mAh g−1) than the Li-S cells with the Celgard 2320 and PEI-4 separator after 200 cycles. In addition, the SiO2 blending PEI separator modified with acetylene black/PVP coating layer for Li-S cells shows higher ionic conductivity (2.02 mS cm−1), excellent compatibility with lithium and better thermal stability. This work presents a convenient and economical strategy to alleviate polysulfide dissolution and the CP55 separator is a promising candidate for the further application of Li-S batteries.
Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.
As one of the most promising energy storage systems, the application of lithium sulfur battery is hindered by low ionic conductivity, poor thermal stability, and a serious shuttle effect of polysulfide species of commercial polyolefin separators. Herein, a kind of novel porous polyetherimide (PEI) based separator (IAPEI-4) with excellent heat resistance and electrochemical properties was prepared. Briefly, by grafting amino group (–NH2) onto the surface of SiO2, highly dispersed amino-functionalized SiO2 (A-SiO2) were synthesized as fillers to blend PEI matrix. Based on physical characterizations of the representative IAPEI-4 separator, abundant micropores and mesopores exist in the separator, and A-SiO2 nanoparticles are uniformly distributed in the PEI matrix. The ameliorated Li+ transport channels and the uniform distribution of A-SiO2 nanoparticles endow IAPEI-4 separator with higher ionic conductivity (1.60 mS cm−1) and Li+ transfer number (0.63). Besides, the IAPEI-4 separator exhibits good electrochemical stability and excellent thermal stability (no shrinkage at 200°C). The Li-S cells with the IAPEI separator also show greatly enhanced electrochemical performances. After 100 cycles, its specific discharge capacity can reach 695.4 mAh g−1 at 0.2 C and its capacity retention rate is 84.8%. This simple and effective separator modification method can provide a general strategy to achieve high-performance Li-S batteries.
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