4th World Conference on Mechanical Engineering

This paper presents a novel sensor-based air quality purifier, "Airborne Sensor-based Total Air Quality Enhancement" (AirSTAQE), designed to address carbon monoxide (CO) pollution in urban environments. Conventional air quality monitoring systems often struggle to integrate realtime monitoring and effective purification for CO, a toxic gas primarily emitted from vehicles that poses acute health risks. The proposed AirSTAQE system combines advanced CO detection using artificial sequestration with efficient air purification techniques. Additionally, it integrates alternative energy sources to extend operational longevity. The system utilizes a state-of-the-art gas sensor (MQ-7) along with temperature and humidity sensors to comprehensively measure CO levels, environmental temperature, and humidity. Notably, AirSTAQE demonstrates swift responsiveness by triggering purification when CO concentrations exceed hazardous levels (≥4.00 ppm), ensuring low latency and proactive mitigation at the edge layer. Experimental validation underscores the system's effectiveness, achieving an impressive 97.5% efficiency in removing CO. It reduces trapped CO from an initial inflow concentration of 4.00 ppm to an outflow concentration of 0.1 ppm post-purification. This study signifies a substantial advancement in air quality enhancement, offering a comprehensive solution to the pressing issue of CO pollution in urban areas. By synergizing cutting-edge sensor technologies, innovative CO detection mechanisms, and efficient purification processes, AirSTAQE sets a new standard for combating air pollution and safeguarding public health.

Sandwich structures are increasingly used in high-performance engineering application because of their high specific stiffness, strength, environmental resistance and thermal insulation characteristics. However, poor fire resistance is a critical problem for the use of sandwich structures in engineering applications. Many studies of different approaches of enhancing flame retardant of sandwich composite material were presented and different thermal models that allow the simulation of the fire resistance of composite materials was made. The Gibson thermal model was chosen to simulate a case using ABAQUS software. The sheet of aluminum is used as a fire retardant of the vinyl ester/glass composite. However, the aluminum is high conductive of heat so air gaps are inserted between the sandwich material and the aluminum to decrease the thermal conductivity of heat, then increase the fire resistant of material. As results, the costumer can choose the appropriate thickness of aluminum and air depends on the time needed before the softening of the material.

Hardware validation has long been constrained by prolonged cycles, delaying time-to-market, increasing costs, and limiting adaptability. Traditional validation approaches remain insufficient for addressing the complexities of modern hardware systems, particularly in semiconductors, automotive, healthcare, robotics, and aerospace. In this study, limitations of existing methodologies are analyzed, and an Agile prototyping framework is introduced to enhance efficiency, flexibility, and early defect detection. By integrating rapid prototyping, iterative feedback loops, and emerging technologies such as AI-driven automation, quantum-enabled simulation, and blockchainbased traceability, validation cycles are streamlined. Case studies demonstrate the framework’s effectiveness, utilizing modular design, digital twins, and concurrent validation to reduce development time and improve reliability. The findings highlight accelerated iteration cycles, improved stakeholder collaboration, and enhanced product quality, offering a scalable, adaptive approach for hardware development in dynamic industries.

This study optimizes injection molding parameters for three polypropylene (PP)-based materials: virgin vPP30GF, recycled rPP30GF, and a hybrid composite hPP12GF12T with 12% glass fiber and 12% talc. The work uses Taguchi's robust design methodology to discover and quantify the most important process variables injection temperature, injection pressure, and packing pressure on tensile strength, flexural strength, and notched impact resistance. An L4 orthogonal array for each material allowed systematic examination of essential injection molding parameters in fewer experimental runs. Mechanical qualities were tested using ISO 527 (tensile), ISO 178 (flexural), and ISO 180 (impact) standards. The statistical contribution of each factor was further analysed using an ANOVA framework to identify the main influences on each composite system. Results show that mechanical performance parameters change greatly between vPP30GF, rPP30GF, and hPP12GF12T. Packing pressure had the greatest effect on tensile strength and impact resistance in vPP30GF. Recycled polymers are more sensitive to temperature, as rPP30GF's tensile and impact performance was most affected by injection temperature. Injection temperature and packing pressure dominated tensile and flexural strength in the hybrid composite, while injection pressure crucially affected impact behavior. These findings encourage industries to tailor injection molding conditions to each composite type rather of using universal settings. The study shows that Taguchi and ANOVA parameter optimization can improve virgin, recycled, and hybrid polypropylene composite performance, enabling data-driven, resource- efficient, and high-quality production.

Accuracy of prediction of aerodynamic flow around the airfoils is essential in aircraft design and optimization. Traditional methods in the domain of Computational Fluid Dynamics (CFD) for simulating flows deliver high accuracy solutions but are time-intensive and computationally expensive. In this paper, we study Graph Neural Networks (GNNs) as a data-driven surrogate model approach to simulate the airfoil flow. GNNs are inherently suited for processing unstructured mesh-based data, this makes them well suited for complex aerodynamic domains. A GraphSAGE-based model is utilized to aggregate information from neighboring nodes and predict the flow characteristics. The model is trained and validated using the opensource AirfRANS dataset, which comprises high-fidelity CFD simulations of NACA 4- and 5-digit series airfoils in subsonic regime for incompressible, steady-state flows. By applying graph-based learning, the GNNs effectively generalize to unseen airfoil shapes and create velocity and pressure fields with near-CFD accuracy with reduced computational costs. The accuracy and generalization capabilities of the model are evaluated, highlighting its potential as a fast and reliable alternative for rapid aerodynamic analysis and optimization.

This study investigates the thermal and hydrodynamic characteristics of five-row parabolic and flat surface toroidal solar collectors through numerical analysis. The research focuses on understanding the complex flow patterns and heat transfer mechanisms in multi-row configurations using computational fluid dynamics (CFD) simulations with ANSYS-Fluent software. The investigation examines the effects of different surface geometries on system performance, analyzing heat transfer patterns and flow distribution across the five-row arrangement. Temperature distribution analysis reveals the thermal interactions between consecutive rows and their impact on overall system efficiency. The study particularly emphasizes the relationship between surface geometry and flow uniformity, demonstrating how different surface designs affect thermal stratification and heat transfer characteristics. Results show that the five-row configuration presents unique flow dynamics and thermal patterns that significantly influence collector performance. This comprehensive analysis provides valuable insights for optimizing the design of multi-row solar thermal systems for large-scale applications.