Search

The slow regression rate induced by the high pyrolysis difficulty has limited the application and development of hydroxyl-terminated polybutadiene (HTPB)-based fuels in hybrid rocket propulsion. Nickel oxide (NiO) shows the possibility of increasing the regression rate of HTPB-based fuels by catalyzing the pyrolysis process of the polymer matrix in our previous investigation; hence, this paper studies the NiO particles in the thermal decomposition and combustion of HTPB fuel grains. The DSC/TG test shows that NiO can intensely decrease the thermal stability of HTPB, and the catalytic effect of NiO is mainly reflected in the final decomposition stages of polybutadiene components. 5 wt% NiO enhances the regression rate by 19.4% and 13.7% under an oxygen mass flux of 50 kg/m2s and 150 kg/m2s, respectively. Further investigation shows that NiO particles will also cause the reduction of combustion heat and the agglomeration at the regressing surface while catalyzing the pyrolysis process, improving the thermal conductivity, and promoting the radiative heat transfer of the HTPB-based fuels; thus, more NiO additive (5 wt% < [NiO] ≤ 10 wt%) does not lead to a faster regression rate in HTPB-based fuels. This study demonstrates the catalytic effect of NiO on the polymer matrix for HTPB-based fuels, showing the attractive application prospects of this additive in HTPB-containing fuel grains. [ABSTRACT FROM AUTHOR]

Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety. [ABSTRACT FROM AUTHOR]

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic. [ABSTRACT FROM AUTHOR]

The low regression rates for hydroxyl-terminated polybutadiene (HTPB)-based solid fuels and poor mechanical properties for the alternative paraffin-based liquefying fuels make today hybrid rocket engines far from the outstanding accomplishments of solid motors and liquid engines. In this paper, a survey is conducted of several innovative methods under test to improve solid fuel properties, which include self-disintegration fuel structure (SDFS)/paraffin fuels, paraffin fuels with better mechanical properties, high thermal conductivity fuels and porous layer combustion fuels. In particular, concerning HTPB, new results about diverse insert and low-energy polymer particles enhancing the combustion properties of HTPB are presented. Compared to pure HTPB, regression rate can be increased up to 21% by adding particles of polymers such as 5% polyethylene or 10% oleamide. Concerning paraffin, new results about self-disintegrating composite fuels incorporating Magnesium particles (MgP) point out that 15% 1 μm- or 100 μm-MgP formulations increase regression rates by 163.2% or 82.1% respectively, at 335 kg/m2·s oxygen flux, compared to pure paraffin. Overall, composite solid fuels featuring self-disintegration structure appear the most promising innovative technique, since they allow separating the matrix regression from the combustion of the filler grains. Yet, the investigated methods are at their initial stage. Substantial work of refinement in this paper is for producing solid fuels to fulfill the needs of hybrid rocket propulsion. [ABSTRACT FROM AUTHOR]