This study conducted a Computational Fluid Dynamics (CFD) analysis of supersonic flow over a flat plate to explore the flow dynamics and associated aerodynamic phenomena in high-speed environments. MacCormack's technique was employed for simulation, which revealed critical features such as boundary layer development, shock wave formation, and variations in pressure and temperature distributions. The findings indicated the complex interactions between these elements and their impact on the aerodynamic performance of a flat plate under supersonic conditions. Specifically, the study provided insights into heat transfer and aerodynamic friction through an examination of boundary layer behaviour, as well as the effects of shock waves on pressure and temperature gradients. Convergence and stability checks demonstrated the robustness of the numerical method, while comparisons with theoretical and experimental data affirmed the accuracy of the results. These insights are valuable for engineering applications, especially in aerospace, where supersonic flows are prevalent. The outcomes from this study can inform the design of supersonic aircraft and high-speed vehicles, contributing to improved aerodynamic efficiency and reduced drag. This research serves as a reference point for future investigations in supersonic flow dynamics and related engineering domains. The future scope for this research encompasses various directions, including exploring advanced numerical methods to enhance accuracy and stability, investigating complex turbulence modelling like Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS), and applying CFD to more complex geometries such as airfoils or wings. Additionally, future research could delve into variable freestream conditions and design optimization to achieve more efficient supersonic applications. Experimental validation through wind tunnel tests and other real-world data collection is also crucial for ensuring the reliability of CFD results. These efforts will expand our understanding of supersonic aerodynamics and contribute to the development of more effective and efficient high-speed vehicles. .

The finite element analysis (FEA) of laminated composite plates, particularly using First-Order Shear Deformation Theory (FSDT), plays a crucial role in developing lightweight and high-strength composite materials for aerospace applications. This technique allows engineers to simulate the behaviour of composite plates under various conditions, providing insights into stress, strain, deformation, and ultimate strength. In this study, the FEA approach has been implemented through a MATLAB code, demonstrating the capability to model complex composite plate behaviour and conduct detailed optimization. The code integrates orthotropic material properties, automatic mesh generation, boundary conditions, load distributions, stiffness matrix assembly, and stress/strain analysis. This comprehensive approach facilitates a thorough understanding of composite materials and their optimal configurations. The optimization aspect focuses on adjusting ply orientation angles to determine the best configuration for maximum strength and minimal weight. By addressing shear deformation, the code is suitable for thicker composite plates, making it highly applicable to aerospace components such as wings, fuselages, and structural frames. Additionally, the inclusion of failure analysis, specifically the Tsai-Wu failure criterion, enhances the reliability and safety of composite structures. The ability to predict potential failure points is critical in aerospace applications, where safety is paramount. Overall, this FEA approach provides a robust framework for evaluating and optimizing composite structures, enabling engineers to develop lightweight and high-strength materials that meet the demanding requirements of the aerospace industry..

This study delves into the optimization of power utilization within sensor networks through the utilization of the ML-LEACH-C algorithm. Sensor networks are integral components of modern technological infrastructure, facilitating data collection and transmission in various fields ranging from environmental monitoring to industrial automation. However, the efficient management of power resources within these networks remains a critical challenge due to the resource-constrained nature of sensor nodes. In response to this challenge, the ML-LEACH-C algorithm is proposed as a solution to optimize power consumption while maintaining network performance. This algorithm integrates machine learning techniques with the well-established LEACH-C protocol to dynamically adapt network parameters based on environmental conditions and node characteristics. By leveraging machine learning algorithms, the ML-LEACH-C algorithm can predict network behaviour and optimize power usage accordingly, thereby prolonging the lifespan of sensor nodes and enhancing overall network efficiency. Through extensive simulations and empirical evaluations, the efficacy of the ML-LEACH-C algorithm in power optimization is demonstrated. Results indicate significant improvements in power consumption, network lifetime, and data transmission efficiency compared to traditional approaches. Furthermore, the scalability and adaptability of the proposed algorithm make it suitable for deployment in various sensor network applications, ranging from small-scale environmental monitoring to large-scale industrial deployments. Overall, this research contributes to the advancement of sensor network technology by providing a novel approach to power optimization through the integration of machine learning and protocol optimization techniques..

Software Development goes through a number of phases. These phases together make a Life Cycle of Software Development. It is estimated that more that there are more than 100 billion lines of code in production in the world. As much as 80% of it is unstructured and not well documented. Maintenance can lessen these problems. Maintainability is the ability to keep the system up to date after deploy to the customer site. We studied a number of software maintainability measurement metrics and also new proposed techniques. In our research we focused on how to measure the software. After considering these factors we can conclude that how much software is maintainable This means that how the maintenance cost can be reduced and how much efforts will be required to reduce the cost. So, we will use a fuzzy logic to implement these factors. We found that fuzzy logic can be used to model uncertainty for these factors. Fuzzy logic is a way to deal with reasoning that is approximate rather than precise. Then by fuzzy logic we measured the maintainability. In our research, we considered experimental data. First, we applied these factors on data and then by fuzzy logic we measured the maintainability. This work based on Rule Base consists of number of rules. Rule structure is like “If this and/or If this than this. .

This collection of studies explores diverse aspects of supply chain management (SCM) across various industries and regions. Utilizing methodologies such as integer linear mathematical models, bi-level optimization, mixed-integer linear programming, and robust optimization strategies, these studies address challenges in procurement, logistics, sustainability, and cost reduction. Findings emphasize the importance of integrated SCM approaches to enhance efficiency, reduce costs, and mitigate risks. This paper explores the optimization of transportation and distribution costs using a greedy algorithm. The study demonstrates how this approach efficiently reduces logistics expenses by prioritizing cost-effective routes and delivery schedules, ultimately enhancing overall supply chain efficiency and minimizing operational costs..