Flow Analyses of Integrated Liquid Fuel RAMJET Propulsion System

A CFD study is performed to check the seamless flow behaviour of the Liquid Fuel Ramjet Propulsion system which includes, air intakes, combustor and nozzle. Resolving both supersonic and subsonic flow scales in the same domain makes the simulations complex. Addition of combustion with stiff chemistry makes the simulations more difficult. CFD simulations are carried out using commercially available CFD software.  Liquid fuel is injected as discrete phase and the flow turbulence is modelled using Realizable k-ε turbulence model. Jet-A + air combustion has been simulated using combined finite rate / eddy dissipation model. Finite rate chemistry was modelled using three step chemistry which was obtained from the published literature. Flow structures such as oblique shocks, normal shocks and combustion are observed

Machine learning Based Bearing Fault Classification Using Higher Order Spectral Analysis

In the defense sector, where mission success often hinges on the reliability of complex mechanical systems, the health of bearings within aircraft, naval vessels, ground vehicles, missile systems, drones, and robotic platforms is paramount. Different signal processing techniques along with Higher Order Spectral Analysis (HOSA) have been used in literature for the fault diagnosis of bearings. Bispectral analysis offers a valuable means of finding higher-order statistical associations within signals, thus proving to detect the nonlinearities among Gaussian and non-Gaussian data. Their resilience to noise and capacity to unveil concealed information render them advantageous across a range of applications. Therefore, this research proposesa novel approach of utilizing the features extracted directly from the Bispectrum for classifying the bearing faults, departing from the common practice in other literature where the Bispectrum is treated as an image for fault classification. In this work vibration signalsare used to detect the bearing faults. The features from the non-redundant region and diagonal slice of the Bispectrum are used to capture the statistical and higher-order spectral characteristics of the vibration signal. A set of sixteen machine learning models, viz., Decision Trees, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine, is employed to classify the bearing faults. The evaluation process involves a robust 10-fold cross-validation technique. The results reveal that the Decision Tree algorithm outperformed all others, achieving a remarkable accuracy rate of 100 %. The naive Bayes algorithm also demonstrated the least performance, with an accuracy score of 99.68 %. The results obtained from these algorithms have been compared with those achieved using Convolutional Neural Network (CNN), revealing that the training time of these algorithms is significantly shorter in comparison to CNN

Numerical and Experimental Analysis of the Influence of Projectile Impact Angle on Armour Plate Protection Capability

This research manuscript describes the process of degradation of an Armox 600 armour plate during the impact of a 5.56×45 mm SS109 projectile. The creation process of the numerical FEM model of the projectile is presented. The projectile impact angle is set between 15° and 90°, and this phenomenon is investigated via numerical and experimental approaches. The experiment is conducted under the same conditions as the numerical approach to validate the FEM model. The experiments are conducted using a high-speed camera. This research manuscript presents the influence of the projectile impact angle on the degradation of the armour plate and its protection capability for different angles. The results demonstrate the dependence of the transferred energy on the armour plate, speed of the particles after impact, and trace dimensions on the armour plate for different impact angles

Urban Operation Threat Assessment after a Multistage Radiological Dispersive Device Attack

The urban environment may be a relevant setting for special military operations. Due to the options offered by urban infrastructure, this environment can be an essential catalyst for the proliferation of local asymmetric actions. These actions are triggered by extremist groups offering resistance to regular troops. Improvised weapons such as radiological dispersive devices (RDD) can be used to provoke even more threatening situations by increasing the risks of operations. This study is directed, via computer simulation using the Hot Spot Health Physics code, to a hypothetical context where a multistage RDD (RDD-M) is triggered in two non-simultaneous phases. This non-linear triggering causes overlapping contamination and impacts the coping strategy and the projections of variations in the size of the potentially affected population. In this study, the primary consideration is the contamination carried out at such a level that the association between human exposure and deterministic effects is feasible. The exposure to high doses of radiation at short distances about the triggering location of the device. The simulated data show that the threats are leveraged, and the environmental variables have a high value when assessing the criticality of the situation and establishing effective countermeasures

Elliptical Multi Orbital Truncated Flexible Patch Antenna Using PDMS Substrate for Sub 6 GHz Applications

This paper presents an elliptical-shaped multi-orbital truncated patch antenna applicable forsub-6 GHz bands. The sub-6 GHz bands cover 5.8 GHz high-speed wireless communication. The antenna is designed on a Poly Dimethylsiloxane (PDMS) substrate. PDMS is used for designing the proposed antenna. It is a flexible substrate with a dielectric constant εr value of 2.7 and a loss tangent tan δ value of 0.022. The substrate dimension is 20×15×1.6 mm3in which a patch is created with the size of 12.5×13 mm2. The proposed antenna resonates at 6.2 GHz showing a reflection coefficient (s11) value of -37 dB. The impedance bandwidth of the antenna is 1.225 GHz in the range of 5.5-6.725 GHz frequency, and the maximum peak gain is about 3.5 dB. The proposed antenna is simulated, fabricated, and experimentally tested

Identification of Hand Tremor Levels in Shooting Activities Under Different Shooting Positions Using a Low Cost and Portable System

The accuracy level is important in shooting activities and depends on many factors, such as hand tremors as body vibration and shooting position. Achieving high accuracy in different shooters is challenging, especially in the case of different shooting positions. However, there is a lack of information about the influence of shooting positions and experiences on a shooter’s body vibration and accuracy levels. Thus, this study aims to develop a portable and low-cost hand tremor measurement device (as a function of body vibration) to identify the influence of hand movement on shooting accuracy. For this purpose, low-cost accelerometer sensors and a microcontroller were used as the measurement kit. Three different shooting positions (squatting, standing, and prone) were analyzed. The shooters were classified into novice and expert groups. Each group had five participants with standard fire guns and accelerometer kits. These participants were asked to shoot the target to get their best accuracy. Besides, the hand tremor level data from the self-developed kit were recorded to investigate the hand tremors. The results show that the novice participants have more hand tremors in all shooting positions. There are significant differences between the squatting, standing, and prone positions in hand tremors for novice and expert participants. In the expert group, the prone and squatting positions have the least vibration level, indicated by the least acceleration (0.01 - 0.04 m/s2 for the expert group and 0.02 - 0.11 m/s2 for the novice group). The best accuracy for all positions is also obtained from expert shooters. It can be concluded that different shooting positions are related to the body vibrations. The expert shooters have a lower body vibration than the novice participants. The hand tremor levels may influence the accuracy level since different shooting positions and experiences have different vibration and accuracy level

Military Decision Support with Actor and Critic Reinforcement Learning Agents

While the recent advanced military operational concept requires an intelligent support of command and control, Reinforcement Learning (RL) has not been actively studied in the military domain. This study points out the limitations of RL for military applications from literature review and aims at improving the understanding of RL for military decision support under the limitations. Most of all, the black box characteristic of Deep RL makes the internal process difficult to understand in addition to complex simulation tools. A scalable weapon selection RL framework is built which can be solved either by a tabular form or a neural network form. The transition of the Deep Q-Network (DQN) solution to the tabular form makes it easier to compare the result to the Q-learning solution. Furthermore, rather than using one or two RL models selectively as before, RL models are divided as an actor and a critic, and systematically compared. A random agent, Q-learning and DQN agents as a critic, a Policy Gradient (PG) agent as an actor, Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) agents as an actor-critic approach are designed, trained, and tested. The performance results show that the trained DQN and PPO agents are the best decision supporter candidates for the weapon selection RL framework

Analytic Model of Capture Probability for Salvo of Two Electromagnetic Launched Anti Torpedo Torpedoes

To pre-estimate the capture probability of electromagnetic coil salvo two anti-torpedo torpedoes (ATTs), an analytic model is proposed. Based on the analysis of the influencing factors, including the target position dispersion of the incoming torpedo, the entry point’s dispersion of the electromagnetic launched ATT, the underwater tracking speed and heading error of the ATT, and the differences between the salvo and single launch mode, a single integral analytic model of the capture probability is established. The course errors of the ATTs are comprehensively calculated utilizing search model, and the search boundaries of the two ATTs are calculated using the geometric method, taking the optimal searching course of the virtual single launch ATT as the desired searching course of the parallel salvo of the two ATTs. The calculation results of the proposed analytic model are consistent with the statistical results of the Monte Carlo approach through simulation comparison and analysis. The proposed analytic model’s consequences for decision-making and effect assessment are discussed

An Efficient Visual Tracking System Based on Extreme Learning Machine in the Defence and Military Sector

Visual tracking is the capacity to estimate or forecast a target object’s location in each frame of a video after specifying its starting position. Visual tracking is of essential relevance in defence and military operations. The military can use it to improve situational awareness, improve precise targeting, acquire intelligence in real-time, and efficiently respond to a variety of threats and circumstances. In the past, object tracking systems have relied mostly on algorithms based on deep learning techniques and these tracking algorithms are lacking in both accuracy and speed. In this research, an Extreme Learning Machine-based visual tracking system has been proposed that incorporates properties like high accuracy, low training time, and less network computing complexity as compared to existing deep learning-based tracking algorithms. The Haar wavelet transform is utilized in the recommended technique for feature learning, while the extreme learning machine is utilised for classification and recognition. A benchmark dataset object tracking benchmark-2013 has been used to carry out the experiments. The experiment values indicated that the proposed technique has accomplished enhanced performance over another tracking model. Additionally, we tested the proposed method’s accuracy and robustness regarding certain visual characteristics: Illumination variation, occlusion, deformation, out-of-plane rotation, background clutters, and in-plane rotation. The findings of the simulation revealed that the objects in videos have been 84% accurately tracked by the suggested method

A Drone Based Image Dataset Generation Methodology for Single Image Super Resolution

The advancements in drone technologies, digital imaging, computer vision techniques, and the liberalized laws related to drone flying have opened up drone-based applications such as the delivery of supplies, search and rescue, aerial surveillance, and so on. The drones, especially the nano/micro/small drones, may be mounted with only low-resolution camera(s) due to their maximum takeoff weight limitations. The low-resolution images generated by the cameras, if used for landing, can result in faulty detection unless the photos are taken from a very close distance to the point of interest. Detection and recognition of the point(s) of interest as early as possible is required to ensure sufficient response time for safe maneuvering. Hence, the images are to be captured at greater heights or distances from the point(s) of interest, and obtaining the high-resolution images from the captured low-resolution images is crucial. The High Resolution (HR) and the Low Resolution (LR) image pairs for training super-resolution models in the works presented in literature are generated using two different cameras or the HR images are captured by the camera and LR images are generated by degrading the HR images. As both methods are not appropriate for small/micro/nano category drones, we propose a novel method based on Ground Sampling Distance (GSD) to capture the LR and HR images. In this paper, we have presented the designed methodology for the creation of a dataset using drone-mounted cameras covering a broad spectrum of views of the target(s) suitable for training and testing of the Single Image Super-Resolution (SISR) models. We also present a methodology for selecting an appropriate target for imaging that enables the visual quality assessment of the developed super-resolution model