Journal of Military Science and Technology

The Forced vibration analysis of sandwich plates with an auxetic honeycomb core and laminated composite three-phase skin layers resting on Kerr foundation

2025-05-30T06:54:53+00:00

This paper introduces isogeometric analysis (IGA) based on higher-order shear deformation theory (HSDT) for vibration analysis of sandwich plates resting on Kerr foundation (KF). The sandwich plates include the ultra-light of an auxetic honeycomb core layer and are reinforced by two laminated composite three-phase skin layers. The governing equation is derived from Hamilton's principle. The Newmark-β method is applied to solve the forced vibration of the sandwich plates by the code in Matlab software. The accuracy of the proposed method is verified through comparative examples. Additionally, the effects of geometrical dimensions, material properties, and foundation stiffness on the forced vibration of sandwich plates are studied in detail.

Theoretical method for determining aerodynamic drag coefficient based on similar bullet samples

2025-05-30T06:54:50+00:00

When a bullet moves in the air, due to the mutual impact between the bullet and air elements, aerodynamic drag is generated, reducing the bullet's range. The drag value depends on many factors and is determined through testing. In calculating exterior ballistics, the drag value is often estimated through the bullet shape coefficient. That leads to many errors compared to reality. With the desire to determine exterior ballistics problem to optimize the design, the paper presents a theoretical method to determine the drag value using the finite element method through a similar bullet type that has been tested. The paper has calculated and determined the exterior ballistic parameters of the newly designed bullet for design calculations. The calculation results deviate no more than 5% from the test results.

Synthesis and thermal characterization of γ-Glycidoxypropyltrimethoxysilane modified phenol-formaldehyde resin

2025-05-26T05:17:02+00:00

This study investigates the synthesis of γ-Glycidoxypropyltrimethoxysilane (KH-560) modified phenol-formaldehyde (PF) resin and its thermal properties. The modification aimed to improve the thermal stability of the PF resin by forming covalent linkages through KH-560. The successful incorporation of KH-560 into the PF resin was confirmed by Fourier Transform Infrared Spectroscopy (FTIR) and proton Nuclear Magnetic Resonance (¹H NMR) analyses. Thermal characterization using Differential Scanning Calorimetry (DSC) revealed that the KH-560 modification led to an increase in the curing temperature and a enhanced exothermic curing reaction compared to the unmodified resin. Thermogravimetric Analysis (TGA) demonstrated an improvement in the thermal stability of the modified PF resin, with higher decomposition temperatures and slower decomposition rates compared to the unmodified resin. These findings confirm that KH-560 is an effective modifier for enhancing the thermal properties of PF resin.

Research on the fabrication and evaluation of certain properties of the multi-metallic catalyst supported on silica TeKL-25

2025-05-30T06:54:47+00:00

This paper presents the research results on the synthesis of the multi-metallic catalyst material TeKL-25 based on a silica support. The characteristics of the synthesized material were analyzed using advanced techniques such as Field Emission Scanning Electron Microscopy (FE-SEM), Brunauer-Emmett-Teller (BET) surface area analysis, X-ray Diffraction (XRD), and Energy Dispersive X-ray Spectroscopy (EDX). The synthesized TeKL-25 material exhibits a tightly bonded structure, a rough surface, relatively uniform particle distribution, a BET surface area of 292.716 m²/g, and a mesoporous structure.

The study on the ability to treat picric acid in water using the indirect photoelectrocatalytic oxidation method with a WO3/Cu electrode

2025-05-30T06:54:43+00:00

The article presents the results of a study on the fabrication and evaluation of certain properties, morphology, crystal structure of the WO₃ coating on a Cu substrate. Simultaneously, the article introduces the preliminary results of evaluating the ability to treat picric acid in water using the indirect electrochemical oxidation method with a WO₃/Cu electrode and HOCl as the oxidizing agent. The research results show that the WO₃ coating on Cu substrate has a hexagonal crystal structure, a density of 6.422 g/cm³, an absorption intensity of 0.8 a.u (λ = 420 nm), and an energy bandgap of approximately 2.8 eV. With an applied potential of +0.8 V (vs. Ag/AgCl) to the WO₃/Cu electrode, the indirect photoelectrochemical oxidation method using the WO₃/Cu electrode and HOCl as the oxidizing agent achieves a 95% treatment efficiency for TNP at a concentration of 80 mg/L after 105 minutes at room temperature.

Method for improving accuracy of trajectory correction using lateral pulse jets

2025-05-26T05:18:14+00:00

The paper presents methods for trajectory correction using lateral pulse jets on flying devices using global navigation systems (GNSS). In the control algorithm, the activation time between two consecutive pulses, instead of being a given constant value, is determined according to the characteristics of the flying device and trajectory parameters. Results of simulation and comparison between cases show that the proposed method of trajectory correction is better than the remaining methods because it requires the smallest number of pulses within the threshold of impact point deviation.

Determination of winding destroy limit area for a power series of amorphous core transformers

2025-05-26T05:17:20+00:00

The amorphous steel core transformer features a unique structure with a rectangular winding, resulting in an uneven distribution of electromagnetic force compared to the circular winding of silicon core transformers. Consequently, evaluating the electromagnetic added value and identifying the destructive areas of the winding is crucial. In this paper, the combination of Matlab and the finite element technique is developed to calculate the magnetic field, short-circuit current, and electromagnetic force for a 3-phase amorphous transformers (22/0.4 kV) during short-circuit faults. The findings establish a relationship between the electromagnetic force value during a short circuit and the winding radius, as well as identify the areas where the winding damage is confined based on the transformer power sequence. These research results assist designers, manufacturers, and transformer operators in determining the appropriate harmful conditions of short-circuit electromagnetic force for the windings.

The design of wideband leakage canceller for FMCW radars

2025-05-26T05:16:45+00:00

The Frequency Modulated Continuous Wave (FMCW) radar is widely applied in various fields such as military, transportation, and aviation. However, in an FMCW radar system that uses a single antenna for both transmission and reception, leakage signals from the transmitter can enter the receiver due to the imperfect isolation of the circulator and the impedance mismatch of the antenna. This issue can degrade system performance, distort the reflected signal from the target, and affect the detection capability of small targets in the close range. This paper proposes an effective wideband leakage cancellation method utilizing an RF-domain canceller structure combined with a discrete phase control mechanism for narrow sub-bands. The system is designed to automatically adjust the phase shift to effectively cancel the leakage signal across the entire frequency band. Simulation results on ADS Keysight software demonstrate that the proposed leakage canceller achieves over 40 dB cancellation within a 500 MHz bandwidth, which is 4.5 times higher than the conventional method.

Bearing fault diagnosis by machine learning and deep learning-based models: A comparative study applying for HUST bearing dataset

2025-05-26T05:17:57+00:00

Diagnosing bearing faults is essential for ensuring the reliability and operational safety of mechanical and electronic systems. This paper presents a comparative analysis of different machine learning-based models for classifying bearing fault conditions, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, One-Dimensional Convolutional Neural Networks (1D-CNN), Two-Dimensional Convolutional Neural Networks (2D-CNN), and Transformer model. These models are applied to the HUST bearing dataset and evaluated based on their ability to accurately classify defects from vibration signal data. The results indicate that 1D-CNN, 2D-CNN, and Transformer model exhibit superior performance in bearing fault diagnosis. 1D-CNN attained 99.8% accuracy on the training set and 99.83% on the test set, followed by 2D-CNN with 99.1% and 99.3%, respectively. The Transformer model also performed well, reaching 99.7% accuracy within 1 hour of training, similar to 1D-CNN (1 hour) and 2D-CNN (0.8 hours). In contrast, LSTM and SVM exhibited lower accuracy and significantly longer training times, with LSTM requiring 11.5 hours and SVM 8 hours. These findings suggest that 1D-CNN, 2D-CNN, and the Transformer model are highly effective approaches for bearing fault diagnosis, with the Transformer model achieving performance and training efficiency comparable to CNN-based models.

Design and manufacture of the microwave receiver block of X-band phase array radar

2025-05-26T05:17:38+00:00

The article introduces a method designing and manufacturing a microwave receiver for an X-band phased array radar station on a purpose-built vessel. The design method uses new generation components, based on survey results, functional analysis, and calculation of parameters of the original block that directly affect the formation of the block's function. The microwave receiver block works according to the principle of super-external deviation, mixing frequency twice; its task is to filter and convert microwave pulses into intermediate frequency pulses 2 to the intermediate frequency amplifier.

Laboratory assessment of thermal transmission in camouflage materials using the SR-5000N spectroradiometer

2025-05-30T06:54:52+00:00

Recent domestic research on camouflage requires standardized evaluation methods and techniques to scientifically and reliably assess camouflage materials, such as fabrics and camouflage nets. This poses significant challenges due to the high level of confidentiality in the field and the limitations of experimental equipment. Drawing on several standards published by advanced nations, this study proposes a method for determining the thermal radiation transmission ratio through camouflage materials in a laboratory setting using the SR-5000N device. The proposed method offers several advantages, including rapid execution, accuracy, reliability, and broad spectral coverage, encompassing the two primary spectral regions of thermal camouflage: the mid-wave infrared (MWIR) and the long-wave infrared (LWIR) ranges. Independent field evaluations have validated the method’s effectiveness, demonstrating its practicality in saving time and resources for assessing the performance of thermal camouflage.

High-order cumulants of wolfram metal in anharmonic EXAFS theory calculated using the correlated Einstein model

2025-05-30T06:54:06+00:00

The anharmonic extended X-ray absorption fine structure (EXAFS) cumulants of wolfram metal (W) in expansion to the 4^th order have been calculated under the influence of thermal disorder. The temperature-dependent EXAFS cumulants were calculated explicitly and simply from a theoretical model developed based on the anharmonic effective potential and classical statistical theory within the correlated Einstein model. The thermodynamic parameters of W have been considered the influence of the nearest neighbor atoms on the backscattering and absorbing atoms. The obtained numerical results of W at temperatures from 0 to 800 K fit with those obtained from the experimental EXAFS data and the anharmonic correlated Debye (ACD) model at various temperatures. These results indicate that the present theoretical model can helpfully analyze experimental EXAFS signals of W and other similar metals.

Algorithm design and development for solving the target engagement problem on subsurface platforms

2025-05-30T06:54:48+00:00

Previous target engagement systems on subsurface platforms were electromechanical computers with highly complex mechanical structures, in which problems were idealized with predetermined parameters. With the current trend of digitalization, digital computers with high computational capabilities have replaced traditional electromechanical systems and are now installed and utilized on subsurface platforms. Based on theoretical studies of the AIUS (Automatic Information and Control System) on naval submarines, this paper presents research on the development of algorithms and software for target engagement using the established theoretical foundations. The proposed algorithm and software are then implemented and tested on a digital computer platform to evaluate their performance. The results demonstrate that the algorithm, when tested on a digital computer, achieves results comparable to those of electromechanical systems, with faster processing speed and the ability to adapt to continuous target movement.

Monitoring cattle behavior using deep learning: An LSTM-based approach with accelerometer data

2025-05-30T06:54:45+00:00

Behavior data analysis is a crucial factor in the early detection of cow health issues, thereby optimizing farming processes and improving productivity in large-scale farms. Accelerometers, attached to the neck or legs of cows, collect movement data, providing a foundation for analyzing animal behavior. Previous studies have proposed cow behavior classification systems based on accelerometer data combined with machine learning algorithms. However, with the advancement of deep learning, the application of Long Short-Term Memory (LSTM) networks can significantly enhance classification performance. In this study, we utilize an LSTM network to classify four primary cow behaviors: Eating, Lying, Standing, and Walking. The LSTM model effectively processes time-series data by retaining essential information while filtering out unnecessary data. Experimental results demonstrate that the model achieves high classification performance, with an average accuracy of approximately 90% across all behaviors, outperforming traditional machine learning algorithms. This research can be implemented in smart farms, integrating with IoT technology to automate livestock monitoring and management efficiently.