Cyclic Loading on Composite Repair of Corroded Steel Pipelines

The study aims to determine how cyclic loading affects the structural integrity and lifespan of composite repair systems used to restore corroded steel pipelines. As specified in Annexure C of the ISO 24817 repair code, pipe specimens are machined to produce flaws with 80% wall loss. Testing under static and cyclic pressure loading is done as per ASTM D2992 and ASTM D2143. Static pressure loading is accomplished by continually pressurizing the pipe specimen, and burst pressure is assessed. Various Rc-ratios or levels of cyclic loading severity are used in cyclic pressure loading tests. Each case's number of cycles before failure is determined experimentally, and the service de-rating factor is assessed in accordance with ISO 24817. The 235 bar pressure was sustained by the static-loaded repaired pipe specimens with 80% wall loss, and the failure was catastrophic. At around 7000 cycles, the cyclically loaded repaired samples with 80% wall loss failed, and the failure manifests as debonding or a leak

Numerical Estimation of Convective Heat Transfer Coefficient and Heat Flux for a Supersonic Rocket Nozzle

Rocket nozzles are often cooled by passing liquid propellants through channels in the nozzle walls. Estimating heat transfer to the wall from the hot gases in the nozzle is essential in deciding on the coolant flow requirements. The present work examines the computational estimation of convection heat transfer to the nozzle walls for compressible turbulent flows. Computations were performed using the rhoPimpleFoam solver in OpenFOAM® with two different turbulence models. We simulate the supersonic flow over a flat plate and validate the heat flux calculation method and turbulence model characteristics. We compare two methods of calculating convection heat transfer in the context of the nozzle flow case presented by Back & Massier. We find that the realizablek-ε turbulence model works well in estimating the heat transfer coefficient

Determination of the Reserved Gap Between the Obturator Ring and the Breechblock in the Metallic Obturation Mechanism of a Large Caliber Gun Howitzer

A reserved gap between the obturator ring and the breechblock in the obturation mechanism of a large-caliber gun is required in the locked state of the gun, which is the main cause of gas leakage. In this study, the finite element analysis of the dynamic contact between the obturator ring and the breechblock and the computational fluid dynamics (CFD) analysis of the high-pressure gas flow through the gap between the obturator ring and the breechblock are conducted. The results show that the smaller the reserved gap is, the shorter the time period during which the contact pressure is zero after the obturator ring contacts with the breechblock will be under a low-bore pressure condition. The results also demonstrate that the leakage flow at the outlet of the gap and the gas flow in the external domain increase with the reserved gap size, and the gas flow in the external domain decays rapidly if the reserved gap is less than or equal to 0.02 mm under a high bore pressure condition. Based on the simulation results, the appropriate reserved gap value is determined and adopted in the studied gun, and good results are achieved in the firing tests

High Speed Coding Unit Depth Identification Based on Texture Image Information Using SVM

High-Efficiency Video Coding (HEVC/H.265) is a new video coding standard with half the bit rate of its predecessor, Advanced Video Coding (AVC/H.264). AVC/H.264 uses macroblocks, processing units between 4×4 and 16×16 pixels in size. H.265 uses Coding Tree Units (CTUs), a more complicated block structure that lets images be as large as 64×64 pixels. However, changing from macroblocks to coding tree units is essential for H.265 to become more efficient. Using the quadtree structure to divide the Coding Unit (CU) makes it harder for HEVC to find the optimal rate distortion. This paper presents a Support Vector Machine (SVM)-based method for finding the fastest coding unit division in intra-prediction HEVC without compromising compression efficiency. All partitions of CTU are assessed using five characteristics: Standard Deviation (SD), Root Mean Square Error (RMSE), Sub CU Complexity Difference (SCCD), Directional Complexity (DC), and Quantization Parameter (QP) to optimize the intra-prediction of HEVC in all intra-configurations. Simulations have been carried out to estimate the performance of the proposed machine learning-based algorithm using test sequences with different resolutions. Simulation results have shown that combining directional complexity and standard deviation gives a more accurate classification. SVM has been used to separate split-unsplit samples, and the standard rate-distortion optimization technique has been used to separate samples that are hard to separate. The results have shown a reduction of 67.44% in encoding time with a slight increase in bit rate

Comparison of Filtering Techniques for Transfer Alignment of Air Launched Tactical Guided Weapons

The transfer alignment technique is very useful for the accurate initialization and calibration of gyroscopes and accelerometers of INS for air-launched tactical guided weapon systems. In the present war scenario, the initialization of INS should be accurate and rapid to launch the tactical weapon against air or land targets within the shortest available time. A lot of development has been carried out by researchers for INS transfer alignment in the field of state estimation. The study and method presented in the research are relevant to aerial launch vehicles. However, to meet present guidance requirements within less time for initializing weapons, more appropriate transfer alignment algorithms are needed. This paper discusses the relative performance of Kalman Filter (KF), Extended KF, and Unscented KF for aligning the weapon INS using the data from Master INS. New developments, limitations, applicability, and design methods for the Kalman filters, became a key component in the transfer alignment of air-launched tactical missiles. These methodologies are extensively utilized in Navigation and Control systems; therefore, this research work will be an informative and perfect guide to existing and potential readers

Static Weapon Target Assignment Based on Battle Probabilities and Time Discounted Reward

Target-based weapon-target assignment (WTA) aims to minimize the total value of enemies. It means that maximizing the total reduced value of the enemies is the objective of the target-based WTA. The reward of an assignment is typically set as the reduction in the enemy’s value when an ally and an enemy have combat, and the value is calculated by multiplying the current value of the enemy by the probability of the enemy’s survival after the combat. However, allies may be assigned to enemies who are far away if the reward is calculated similarly. Additionally, a method of calculating battle probability that reflects the characteristics and deployment of enemies and allies is needed in order to apply it in the defense industry. In this paper, we propose a target-based static weapon-target assignment to solve these problems. First, we propose a method to calculate battle probabilities for one-to-one, one-to-many, many-to-one, and many-to-many combat. The probabilities are composed of 4 cases; ally-survival-enemy-survival, ally-survival-enemy-destroyed, ally-destroyed-enemy-survival, and ally-destroyed-enemy-destroyed. Then a time-discounted reward for assignment based on the battle probabilities is calculated to consider the time it takes to have combat. Finally, the tank combat simulation results are discussed. The performance of the proposed WTA algorithm is highlighted through an analysis of assignment results and a comparison of outcomes based on the application of time-discounted rewards

Direction of Arrival Estimation Using Underwater Acoustic Vector Sensor Array Towards Coastal Surveillance Applications

The objective of this paper is to present the performance of Direction of Arrival(DoA) estimation algorithms for underwater sound source localization using an acoustic Vector Sensor Array (VSA) that is developed by the National Institute of Ocean Technology, Chennai. Algorithms such as conventional beam forming, Multiple Signal Classification (MUSIC) with Eigen value decomposition, and MUSIC with Singular Value Decomposition (SVD) are used for estimation of DoA and performance study. An experiment has been conducted with the VSA at the Acoustic Test Facility of NIOT with the source transmission of 1 kHz to 5 kHz for different azimuth angles. The estimation of DoA using the above three algorithms and the comparison of the results on resolution and accuracy have been studied in detail in terms of the number of vector elements. Results reveal that the MUSIC method gives results with higher accuracy and resolution than the conventional method. The maximum deviation from the true angle in the conventional method is 4°; in MUSIC, it is 2°, whereas in MUSIC with SVD, it is 1°. While the standard MUSIC algorithm involves computing the eigenvectors of the covariance matrix, which can be computationally expensive, MUSIC with SVD provides a more efficient way to achieve better results. SVD enables straightforward computation of the signal subspace, making it more practical for real-time applications like coastal surveillance. Further to the laboratory experiment, the vector sensor system has been deployed in an open sea environment near the harbor and a known source experiment is carried out. The DoA estimated using MUSIC with SVD for the field data reveals that the results are in good comparison with the measured azimuth and elevation positions. The deviations in the field results are due to dynamic conditions of the ocean ,and more sea trials need to be carried out for further study

A C4 Software for Anti Drone System

Mini unmanned aerial vehicles (UAVs), commonly known as small drones, have seen outstanding advancements in recent years and have been used in a variety of fields. However, their potential misuse for illegal activities and the risks they pose to safety and privacy have raised concerns. To address these issues, we propose a Command, Control, Communications, and Computers (C4) software able to manage and control anti-drone systems. Our software solution includes an easy-to-use dashboard that processes and displays video data from surveillance sensors. It incorporates AI-powered functionalities, including object detection, target tracking, and classification of small drones. At the inference stage, the network models for drone detection and classification functionalities have achieved an accuracy exceeding 96 %. We have evaluated the effectiveness of our solution by deploying it in a no-fly zone, where it successfully identified and tracked drones in near real-time. The proposed control system provides unified information with which the entire anti-drone process can be managed starting from the detection of each threat

Deep Learning for Unearthing Emotions in Twitter A Hybrid Emotional Recognition Model

With the intensification of new classes of media such as Twitter, the Internet has become a primary route for individual and interpersonal messaging. Many individuals share their thoughts regarding news-related topics on Twitter, an established SNS network built on people’s relationships. It offers us with a Source of data from which we can dig people’s thoughts, which is useful for product reviews and community monitoring. A Hybrid Emotional Recognition Model (HERM) is proposed in this research. Hashtags are recognized as the tag for emotional cataloging based on gathered posts from Twitter. Meanwhile, emoji and the N-grams are dug and used to classify the gathered topic comments into four distinct sentiment groups using the distorted emotional models. Machine learning approaches are applied of categorizing the emotional information set, yielding an 92 % accuracy result. Furthermore, entities underlying emotions might be obtained using the deep learning model SENNA