Yonsei University · Engineering
Professor Muhammad Nasir Bashir's research lab specializes in sustainable materials and advanced energy systems, focusing on eco-friendly natural fiber composites, metal matrix nanocomposites via friction stir processing, and innovative energy solutions such as HHO-based combustion enhancement in internal combustion engines. The lab also investigates power quality improvement through active power filters and explores thermofluid dynamics in nanofluid systems for efficient heat transfer. These interdisciplinary efforts align with global sustainability goals, emphasizing renewable materials, energy efficiency, and reduced environmental impact.
Figures are computed from collected data and may differ slightly.
Nowadays, people are highly conscious of the environment, leading to rapid growth and progress in research and innovation in eco-friendly natural fiber composites (NFCs), which are also cost-effective. The sustainable development of biodegradable NFCs obtained from renewable sources is paving the way for the replacement of synthetic fiber composites. Furthermore, researchers are focusing on enhancing the mechanical performance of NFCs for various applications. Some renewable sources, such as ric
This work conducts an extensive survey that provides a complete overview of various control methodologies and stability considerations pertaining to Microgrids. Microgrids primarily function in a grid-connected manner, but they possess the capability to transition to standalone operation during emergency situations. Factors such as stability and operational control are of paramount importance in both modes of operation due to considerations such as frequency, voltage, optimal power transfer, and
The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting
Numerical simulation of magnetohydrodynamic radiative double-diffusive convective heat and mass transfer of a ternary hybrid nanofluid with thermophoretic particle deposition in an inclined annulus is studied. Both sides of cylinders are preserved at uniform temperatures, while the remaining sides are thermally insulated. The coupled nonlinear partial differential equations are solved using a finite difference approach. The detailed computational results of heat and mass transfer rate, temperatu
In contemporary power distribution systems, a substantial proportion of the loads not only utilize active power but also extract reactive power and harmonic currents from the AC source. The increased extraction of power components is a result of the widespread usage of fast-switching devices in utility systems. These devices contribute to an escalation in harmonic issues and problems related to reactive power. Furthermore, power quality disturbances have a range of negative repercussions on the
In striving for sustainable alternatives to gasoline, Oxyhydrogen (HHO) has emerged as a promising substitute for Internal Combustion Engines (ICEs). HHO blends not only improve engine efficiency but also reduce harmful emissions. On-site, HHO utilization in the engine, eradicates low energy density and storage challenges. The current study combined cutting-edge machine learning (ML) techniques like Artificial Neural Network (ANN) and Gradient-based optimization to effectively utilize HHO with g
The mechanical performance of FDM-printed high-performance polymers like carbon fibre-reinforced PEEK remains sensitive to process parameters, limiting their reliability in structural applications. The present study addresses this challenge by investigating the influence of printing speed, layer thickness, and infill density on tensile, flexural, and compressive strengths. Using Response Surface Methodology (RSM) and Taguchi analysis, predictive models were developed and statistically validated
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