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This study presents a Computational Fluid Dynamics (CFD)–based analysis of fluid flow characteristics in advanced mechanical systems. Numerical simulations are conducted to evaluate velocity distribution, pressure variation, turbulence behavior, and overall flow performance under realistic operating conditions. The results demonstrate the effectiveness of CFD as a reliable tool for predicting complex flow phenomena and supporting design optimization of high-performance mechanical systems. Understanding fluid flow behavior is essential for improving the efficiency and reliability of advanced mechanical systems. CFD provides a powerful numerical framework to analyze complex flow phenomena that are difficult to capture through experimental methods alone. The study employs a finite volume–based CFD solver with appropriate turbulence modeling to simulate fluid flow within the mechanical system. Structured and unstructured meshes, validated boundary conditions, and convergence criteria ensure numerical stability and accuracy. The simulation results reveal stable flow development with well-defined velocity profiles and localized pressure drops near geometric constraints.
This study presents the design and performance analysis of an energy-efficient mechanical system for industrial applications, integrating optimized mechanical design and intelligent energy management strategies. Experimental and numerical results demonstrate significant reductions in energy consumption and improvements in efficiency, thermal stability, and operational reliability compared to conventional systems. Energy efficiency is a critical requirement in modern industrial mechanical systems due to rising energy costs and sustainability demands. This work focuses on developing and evaluating an optimized mechanical system that minimizes energy losses without compromising performance. The methodology involves system design optimization, prototype development, and sensor-based data collection under varying load conditions. Performance metrics such as power consumption, efficiency, temperature, and vibration are analyzed and compared with a baseline system. Numerical and experimental analysis shows up to ~26% reduction in energy consumption and ~18–19% improvement in system efficiency.
This study presents the development of a smart mechatronic system for automated mechanical operations by integrating sensors, control systems, actuators, and IoT-based communication. The proposed framework enables real-time monitoring, intelligent decision-making, and precise control of mechanical processes. Experimental and numerical evaluations demonstrate reliable system performance, low latency communication, and improved automation efficiency, validating the effectiveness of the developed smart mechatronic architecture for modern industrial applications. Smart mechatronic systems play a vital role in modern automation by integrating mechanical components with electronics, control, and intelligent software. Such systems enable precise, efficient, and autonomous mechanical operations across advanced industrial environments. The proposed methodology integrates multi-sensor data acquisition, embedded control algorithms, actuators, and IoT communication within a unified smart mechatronic framework. Real-time data processing and wireless connectivity enable adaptive control, remote monitoring, and efficient automated mechanical operations.
This study investigates a temperature control system for mechanical engineering applications through both experiments and numerical analysis. A 3D CFD-based computer model was made to look at how heat transfer and fluid flow work, and experiments were done in a controlled lab setting. Key thermal factors, like the rate of heat transfer, the spread of temperature, and the thermal efficiency, were checked and confirmed. The numerical predictions matched up very well with the actual data, with differences that were within the allowed limits of engineering. Modern mechanical systems that have to endure a lot of heat need adequate thermal control to ensure they are reliable, work well, and save energy. This work investigates thermal behavior using a coupled numerical–experimental framework to improve prediction accuracy and design effectiveness. The methodology combines CFD-based numerical simulations with experimental testing under identical operating conditions to analyze heat transfer performance. Numerical results are validated using experimental temperature and heat transfer data through error and correlation analysis.
This study presents an artificial intelligence–based framework for optimizing manufacturing systems to enhance productivity, reduce defects, and improve energy efficiency. Machine learning and optimization algorithms are applied to real production data, and numerical results demonstrate that advanced AI models significantly outperform traditional methods in overall manufacturing performance. Modern manufacturing systems face increasing complexity due to dynamic demand, resource constraints, and operational uncertainty. Artificial intelligence techniques offer intelligent, adaptive, and data-driven solutions to overcome the limitations of conventional optimization methods. The proposed methodology integrates data collection, preprocessing, AI-based modeling, and optimization using machine learning and metaheuristic algorithms. The framework predicts system performance and generates optimal production decisions through a closed-loop, data-driven approach. Numerical results show that AI-driven optimization increases production rate from 120 to 155 units/hr while reducing defect rate from 4.5% to 2.1% and energy consumption from 520 to 430 kWh.
The growing demand for clean, efficient energy solutions has accelerated the adoption of sustainable energy systems in mechanical engineering applications. This study presents a comprehensive performance evaluation framework integrating thermodynamic and exergy analysis with environmental, economic, and multi-criteria assessment. Solar thermal, wind, biomass CHP, and hybrid renewable systems are analyzed using modeling, simulation, and validated numerical methods. Results demonstrate that hybrid renewable systems offer superior efficiency, reduced environmental impact, and enhanced sustainability, making them highly suitable for modern mechanical engineering applications. Sustainable energy systems are increasingly essential in mechanical engineering to address energy efficiency, environmental impact, and resource limitations. Performance evaluation using energy, exergy, environmental, and economic metrics is critical for identifying optimal system configurations. This study adopts a structured methodology that includes system modeling, thermodynamic and exergy analysis, environmental and economic assessment, and multi-criteria decision-making.