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All Journal Civil and Environmental Science Journal (CIVENSE) Makara Journal of Technology Journal of Sustainable Construction
Yawas, Danjuma Saleh
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering Transportation

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Journal : Makara Journal of Technology

 1 
Smart Materials for Noise and Vibration Damping in High-Speed Rail Systems: A Comparative Analysis Unegbu, Hyginus Chidiebere Onyekachi; Yawas, Danjuma Saleh; Dan-asabe, Bashar; Alabi, Abdulmumin Akoredeley
Makara Journal of Technology Vol. 28, No. 3
Publisher : UI Scholars Hub

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Abstract

Effective noise and vibration control remains a critical challenge in high-speed rail systems, directly influencing passenger comfort and the longevity of infrastructure. This study evaluated four advanced materials—piezoelectric materials, shape memory alloys (SMAs), magnetorheological (MR) fluids, and damping composites—focusing on their potential for mitigating noise and vibration in high-speed rail applications. A combination of experimental and simulation-based analyses was employed to assess these materials based on their noise reduction coefficient, vibration transmissibility ratio, thermal stability, and durability under varying environmental conditions. The findings revealed that damping composites and SMAs demonstrated superior performance, offering enhanced noise attenuation and vibration control compared with the other materials. Damping composites exhibited the highest noise reduction and stability across a wide frequency range, while SMAs demonstrated exceptional adaptive damping properties under fluctuating temperature conditions. In contrast, piezoelectric materials and MR fluids showed moderate performance, making them more suitable for secondary damping applications. This study identifies damping composites and SMAs as the most effective materials for primary noise and vibration control in high-speed rail systems. The findings provide valuable insights for material selection and integration in rail infrastructure, contributing to enhanced system performance, reduced maintenance costs, and compliance with stringent noise and vibration regulations.
Computational Fluid Dynamics (CFD) Modeling for Bio-Inspired Aerodynamic Optimization in Autonomous Drones Unegbu, Hyginus C.O.; Yawas, Danjuma Saleh
Makara Journal of Technology Vol. 29, No. 2
Publisher : UI Scholars Hub

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This study explores the aerodynamic benefits of bio-inspired design modifications for autonomous drones using advanced Computational Fluid Dynamics (CFD) simulations. Four bio-inspired configurations—leading-edge serrations, winglets, riblet surfaces, and curved wings—were assessed and compared against a baseline drone model to evaluate their impact on aerodynamic performance. The results indicated that all bio-inspired designs significantly enhanced lift, reduced drag, and improved overall aerodynamic efficiency. The leading-edge serration configuration achieved the highest performance gains, with a 33.6% increase in maximum lift coefficient (CL) and a 29.5% improvement in lift-to-drag ratio (CL/CD), primarily due to delayed flow separation and reduced turbulence. Winglets minimized wingtip vortices, leading to an 18.3% reduction in drag coefficient (CD) and improved lift efficiency. Riblet surfaces moderately decreased drag by streamlining boundary layer flow, while the curved wing design enhanced stability and manoeuvrability at high angles of incidence. These findings demonstrate the potential of bio-inspired designs to optimize drone performance, extending their operational range and adaptability across varying flight conditions. The study provides valuable insights for development of next-generation UAVs, offering a pathway to improved energy efficiency, flight stability, and versatility in diverse operational environments.
Co-Authors Alabi, A.A. Alabi, Abdulmumin Akoredeley Dan-asabe, Bashar Unegbu, H C O Unegbu, Hyginus Unegbu, Hyginus C. O. Unegbu, Hyginus C.O. Unegbu, Hyginus Chidiebere Onyekachi