To investigate the influence mechanism of liquid filling ratio on the blast resistance of liquid-filled cylindrical shells, this study conducts a systematic examination of the dynamic response of partially filled cylindrical shell structures under near-field underwater explosions through integrated experimental and numerical simulation approaches. The results reveal that the structural response demonstrates three distinct phases: shock wave action stage, stable stage, and bubble pulsation action stage, with the primary deformation occurring during the bubble pulsation action stage. A significant positive correlation exists between liquid filling ratio and blast resistance, evidenced by a 52.7% reduction in central deflection when the filling ratio increases from 0% to 95%. While the internal peak pressure during the shockwave phase increases progressively with elevated filling ratios, the maximum internal pressure during bubble pulsation occurs at the 90% filling ratio. The plasticity of cylindrical shells under explosive loading decreases proportionally with increasing filling ratio, exhibiting reduction trends consistent with central deflection. These findings provide theoretical foundations and engineering insights for blast-resistant design and damage assessment of underwater protective structures.
To investigate the distribution pattern of shock waves in relation to soil damage characteristics under the action of air shock waves generated by ground explosions of TNT charges, static ground explosion tests with 10 kg and 20 kg TNT charges were carried out, and the characteristics of air shock wave overpressure distribution after the explosion of charge were obtained. A simulation model for the pressure field of ground explosion shock waves was established, and its reliability was verified with experimental results.The research findings indicate that a frustum-shaped crater is formed on the soil surface after the charge explosion . The increase in the explosive charge from 10 kg to 20 kg results in an increase in the diameter of the crater from 1.5 m to 1.9 m, an increase in the air shockwave at 5 m from 0.186 MPa to 0.366 7 MPa, and an increase in the reflective pressure on the surface of the wall at 5 m from 0.604 MPa to 1.485 MPa. The shape of the explosive charge has a significant impact on the near-field pressure distribution. The ground reflection effect substantially enhances the pressure values in the near-ground pressure field, with the pressure in the near zone exhibiting a convex profile that gradually degenerates into an ellipsoidal shape at a distance of 5 m from the explosion center. The wall reflection pressure of the shock wave decreases with height, following a quadratic function attenuation pattern.
To solve the problem of low reaction rate and incomplete reaction of metal fuels, a core-shell structured composite energetic material, AP@TiH2, consisting of ammonium perchlorate (AP) particles coated with titanium hydride (TiH2) powder, was prepared by the solvent-nonsolvent method. The microstructure distribution and ignition combustion performance of this composite material were studied by characterization technique and laser ignition experiments. The results show that Ti is uniformly attached to the surface of massive ammonium perchlorate particles, and the AP@TiH2 composite material has brighter flame, larger reaction area, shorter combustion time and faster reaction rate. The addition of AP@TiH2 to solid propellants can effectively improve the ignition characteristics of the propellants, and improve the combustion performance. Compared with the traditional mechanical mixing methods, the temperature rise rate of AP@TiH2/HTPB propellant prepared by the solvent-nonsolvent method is faster, the average temperature of stable combustion stage is higher, the ignition delay time is reduced by about 10%, and the linear combustion rate is increased by about 11%. The coating structure reduced the mass and heat transfer distance between the fuel and oxidizer, and effectively improved the reaction rate and reaction completeness of the fuel. This fine control method is expected to provide a new way for the effective utilization of solid propellant energy.
Products of thermite reaction are widely used in the fields of metal demolition and ammunition destruction due to their ability to rapidly heat target plates and perform external work. To study the ablation perforation performance of aluminum/polytetrafluoroethylene/iron oxide/copper oxide (Al/PTFE/Fe2O3/CuO) fluorothermite on steel plates, the reaction performances of different combinations of thermite mixtures were comparatively analyzed. The ablation perforation test was carried out and the relationship between the combustion performance and perforation performance of the thermite mixture was discussed. The results indicate that the actual combustion temperatures of the five formulations of thermite range from 2 200 K to 2 400 K; thermite mixtures with lower Al/PTFE content exhibit stronger flames, higher combustion temperatures, and pressures; meanwhile, the presence of Al/Fe2O3 helps to lower the reaction initiation temperature; thermite formulations possessing the aforementioned characteristics demonstrate better penetration effects on steel plates; the pressure rise rate and combustion temperature of the thermite are likely the primary factors influencing the perforation performance; a lower residual carbon content can prevent the formation of high-melting-point carbides in the stagnation zone of the steel plate, which is conducive to the melting of the steel plate.
As the crucial protective structure for coastal engineering works such as ports and wharves, breakwaters are typically constructed by piling up rock blocks. In response to the issues of damage, fragmentation, and cracking that occur under the high-speed impact of blasting loads, multi-condition blasting tests have been conducted to analyze the destructive effects of grouped charges with various explosive quantities at different positions on the breakwater structure. Based on the blasting test data of breakwaters, the accuracy of the material parameters in the numerical model was validated, a full-section breakwater numerical model was constructed, and the damage data of the breakwater under different charge positions, quantities, and burial depths were obtained. The research indicates both the charge quantity and detonation position significantly influence the destruction effects on the breakwater; internal charges, due to the efficient release of energy, exert a prominent destructive effect on the vertical structure of the breakwater, with a damage range being significantly larger than that in the cases of charges placed on the slope and the top. Through multi-condition numerical simulations, the revised calculation formulas for crater depth and destruction diameter were proposed. The research results provide theoretical support for the precision blasting design and damage assessment of large-scale block pile structures.
To address urban traffic congestion and the high demands for traffic collaboration, this study proposes a communication cooperation model-multi-agent deep deterministic policy gradient (CCM-MADDPG) algorithm based on a communication cooperation module (CCM). The core of this algorithm lies in the design of the CCM, which explores the deep-level relationships among neighboring agents through an embedding layer and an information extraction module. It dynamically weights and fuses neighbor information with an attention mechanism to avoid redundancy and information averaging. By embedding the CCM into the Actor network, agents integrate neighborhood information for decision-making, leveraging the locality principle to alleviate joint action space challenges while enhancing system stability and collaborative efficiency. Experiments demonstrate that the CCM-MADDPG significantly outperforms baseline models, such as Qmix and FRAP, on both synthetic and real-world dataset, exhibiting exceptional scalability and adaptability in complex road networks. Specifically, in the Manhattan road network, it reduces average delay by approximately 28.8% and 26.3% compared with Qmix and FRAP, respectively. The ablation studies confirm the necessity of CCM's information extraction and attention mechanisms. This algorithm provides an efficient solution for multi-agent collaborative control, with dual potential for urban traffic optimization and applications. Its core contribution resides in the successful integration of the CCM design with the MADDPG framework.
In the context of modern military operations, the network collaboration capability of distributed unmanned combat clusters is of vital importance. Focusing on such cluster collaborative networks, an inductive graph reinforcement learning framework is established, and a heterogeneous network disintegration method integrating inductive graph representation learning and deep reinforcement learning techniques is proposed. The inductive graph representation learning empowers the method with the ability to rapidly generate disintegration effects on the strategies of emerging networks, and by modeling network disintegration as a Markov decision process and combining it with deep reinforcement learning for solving, the disintegration efficacy is significantly improved while reducing the complexity of the problem. Experiments show that the inductive approach is able to transfer among networks of different scales and achieves a better balance between disintegration effectiveness and time consumption; especially in scenarios where node-attack cost constraints are introduced, it exhibits excellent disintegration efficacy and strategy scalability. This research has significant military application value in reducing the complexity of battlefield cognition and improving the timeliness of dynamic decision-making.
In the modern battlefield, airborne multifunctional radars are capable of flexibly switching operating modes according to combat requirements, performing various tasks such as airspace scanning and target tracking. The dynamic switches of the operating modes not only serve as a critical basis for assessing threat levels but also pose challenges for electronic reconnaissance. To enhance the recognition efficiency of the operating modes of airborne multifunctional radars, this paper proposes a recognition algorithm based on integrated deep learning. The algorithm integrates multiple deep learning models and dynamically adjusts their weights based on the prediction accuracy of each model. On the premise of ensuring optimal overall performance, the strategy of "sacrificing the minor for the major" is adopted to enhance the predictive accuracy and stability of the integrated model. The simulation results demonstrate that this integrated deep learning method based on the strategy of "sacrificing the minor for the major" significantly enhances recognition efficiency, providing an effective solution for the precise identification of operating modes of airborne multifunctional radars.
As one of the key technologies of multi-agent unmanned systems, collaborative control has long been trapped in the problem of how to solve the influence of external disturbances.This study addresses the consistency control of multi-agent unmanned systems in the presence of input disturbances and introduces an event-triggered single-network adaptive dynamic programming approach. The proposed control strategy mitigates input disturbance signals by integrating the coupling gain with the optimal solution of the system cost function. Additionally, this research establishes the event-triggered conditions for updating the control strategy and rigorously proves the stability of the consistency state error in the multi-agent nonlinear systems under the event-triggered mechanism. Furthermore, a single-network adaptive dynamic programming algorithm is developed to solve the coupled Hamilton-Jacobi-Bellman (HJB) equation. Leveraging the Lyapunov stability theory, the uniform boundedness of the neural network weight estimation error is validated.Finally, the simulation results demonstrate the effectiveness of the proposed method which provides a reference for enhancing the collaborative capabilities and efficiency of unmanned systems.
A multi-objective path optimization problem is proposed in order to assist decision-makers to make balanced decisions based on comprehensively considering the flight energy consumption of unmanned aerial vehicles (UAVs) and the timeliness of information from sensor nodes in complex battlefield environments. By tightly coupling multiple sub-problems, such as clustering of ground sensor nodes, optimization of the data upload sequence of nodes within the cluster, waypoint allocation of multiple UAVs, and path optimization of UAVs, a multi-objective multi-UAV clustering location-routing problem (MOMAC-LRP) optimization model that minimizes the total flight time of multiple UAVs and the age of sensor node information is constructed. Additionally, an improved non-dominated sorting genetic algorithmⅡwith task allocation design is proposed to solve this problem, and a CPLEX solver based on non-uniform aggregation weights is designed as a comparison scheme. The simulation results show that the optimized path set generated by the proposed algorithm can more reasonably balance the relationship between different indicators while having performance no lower than that of CPLEX, so it has a higher reference value, providing new ideas for improving the scientificity and effectiveness of decision making.
To address the escalating demands for integrated low-altitude communications and sensing, this paper investigates the distributed integrated communication and sensing beamforming problem for low-altitude airspace based on a cell-free MIMO architecture. Assuming access point (AP) equipped with uniform planar array (UPA), the study optimizes distributed beamforming vectors to concurrently provide communication services to low-altitude and terrestrial users while actively sensing low-altitude targets. The distributed beamforming problem is modeled as a signal-to-noise ratio (SNR) maximization problem for sensing, subject to constraints on the communication signal-to-interference-plus-noise ratio (SINR).This problem is a non-convex optimization problem. Then, an approximate optimal solution method based on semidefinite relaxation (SDR) is proposed. The simulation results demonstrate that the proposed method can achieve a communication SINR comparable to that of the optimal beamforming design scheme. Moreover, the sensing SNR performance significantly outperforms that of prior comparative schemes designed solely for communication or sensing beams.
The traditional methods for evaluating the effectiveness of manned-unmanned collaborative combat systems suffer from issues such as over-reliance on single indicators, insufficient objectivity and accuracy, non-intuitive evaluation results, and low credibility. These limitations make it challenging to comprehensively reflect the complex interconnected relationships among various elements within the manned-unmanned combat system and address the uncertainties inherent in the combat process. In order to solve these problems, this paper puts forward a method for evaluating the effectiveness of manned-unmanned collaborative combat based on the Bayesian network. Based on an analysis of effectiveness evaluation criteria, an evaluation index system for manned-unmanned collaborative combat effectiveness is constructed, targeting six types of typical collaborative combat missions. Furthermore, a Bayesian network model is established to assess the effectiveness of manned-unmanned collaborative combat. The simulation results show that this model has certain unique advantages for the effectiveness evaluation of manned-unmanned collaborative combat in uncertain battlefield environment.
At present, most of the manned and unmanned maritime forces rely on subjective decision-making for organization and deployment. In response to the issue of limited and relatively weak systematic modeling and optimization applications for force allocation, this study, starting from the operational and tactical scales and based on the PREA (planning, readiness, execution, and assessment) loop operational theory, comprehensively considers the characteristics of both manned and unmanned maritime forces, and proposes a methodology for constructing a force allocation model for collaborative command of manned and unmanned forces, primarily from three aspects: operational resource modeling, operational requirement analysis, and operational capability employment. By analyzing typical maritime combat simulation cases and employing the multi-objective quantum particle swarm optimization algorithm for rapid model optimization and iteration, this study provides operational resource allocation schemes that align with practical application scenarios. The results demonstrate that the proposed model exhibits good practicality and can serve as a reference for combat force deployment (CFD).
