Muhammad Rizwan Shad

Bio: Muhammad Rizwan Shad is an academic researcher from University of Central Punjab. The author has contributed to research in topics: Rotary inertia & Nonlinear system. The author has an hindex of 2, co-authored 9 publications receiving 45 citations. Previous affiliations of Muhammad Rizwan Shad include University of Toulouse & Intelligence and National Security Alliance.

Recent Developments in Coatings for Orthopedic Metallic Implants

Abstract: Titanium, stainless steel, and CoCrMo alloys are the most widely used biomaterials for orthopedic applications. The most common causes of orthopedic implant failure after implantation are infections, inflammatory response, least corrosion resistance, mismatch in elastic modulus, stress shielding, and excessive wear. To address the problems associated with implant materials, different modifications related to design, materials, and surface have been developed. Among the different methods, coating is an effective method to improve the performance of implant materials. In this article, a comprehensive review of recent studies has been carried out to summarize the impact of coating materials on metallic implants. The antibacterial characteristics, biodegradability, biocompatibility, corrosion behavior, and mechanical properties for performance evaluation are briefly summarized. Different effective coating techniques, coating materials, and additives have been summarized. The results are useful to produce the coating with optimized properties.

Performance Analysis and Comparison of a Concentrated Photovoltaic System with Different Phase Change Materials

Abstract: In this work, temperature regulation and electrical output of a concentrated photovoltaic system coupled with a phase change material (CPVPCM) system is investigated and compared with a single sun crystalline photovoltaic (PV) system. A fully coupled thermal-optical-electrical model has been developed in-house to conduct the simulation studies for actual weather conditions of Doha (Qatar) and selected phase change materials (PCMs). The selected PCMs are lauric acid, RT47, S-series salt, STL47, ClimSelTM C48, RT54, RT60, RT62, and RT64. An optical concentration ratio of 20× is considered on a 15 mm wide crystalline silicon cell. The temperature evolution, thermal energy storage and electrical output of the CPVPCM system are obtained for 48-hour simulations with representative weather conditions for each month of a typical meteorological year (TMY). Results and overall thermal and electrical efficiency are compared for each PCM. In brief, the CPVPCM system with S-series salt performs better than all other PCM with an overall efficiency of 54.4%. Furthermore, this system consistently produces more power than a PV system with an equal footprint (1 m2) for each month of the TMY.

Validating global horizontal irradiance estimated by McClear model under clear-sky and all-sky conditions in Pakistan

Abstract: The availability of accurate and reliable solar radiation data is very important to explore solar energy potential for commercial power plants. This study presents the validation of global horizontal irradiance (GHI) from McClear model under clear-sky and all-sky conditions against measured data for complex topography of Pakistan. The McClear GHI shows good agreement with measured GHI under clear-sky conditions for nine stations and good correlation was achieved with correlation coefficient ranging from 0.972 to 0.992. The relative mean bias error (rMBE) and relative root mean square error (rRMSE) using GHI data having temporal resolution of 10 minutes was ranging from −2.3 to 6.4% and 3.6 to 7.8% respectively. A seasonal statistical analysis shows variation of errors during seasons, significant variation of errors was observed for monthly statistical analysis, the overall errors for all stations were maximum in January and minimum in June. Cloud fraction (CF) from three state-of-the-art datasets (ERA-5, MERRA-2 and MODIS) was incorporated into McClear clear-sky GHI (GHIMC,clear-sky) to derive commercially resourceful all-sky GHI data (GHIMC,all-sky) using a noval approach. The rMBE of GHIMC,all-sky using CF from ERA-5, MERRA-2 and MODIS ranges from 0.1 to 8.4%, 0.1 to 14.1% and 0.2 to 16.8% respectively. The application of ERA-5 cloud fraction shows good correlation with correlation coefficient ranging from 0.871 to 0.966 and gave best results as rMBE for all stations is less than 4% except one station, the long-term GHIMC,all-sky data can be used for the solar resource assessment of Pakistan in the absence of measured data.

Enhancing corrosion resistance of Al 5050 alloy based on surface roughness and its fabrication methods; an experimental investigation

Abstract: The aim of this study is to investigate the effect of roughness fabrication methods on the corrosion resistance of Al-5050 alloy. The surfaces having roughness (0.45–6.2 μm) are generated by polishing, machining, and sandblasting. The surfaces are tested in 3.5wt%NaCl aqua solution with the voltage from −0.2 to 1.0 V with a scan rate of 0.1mv/s. Tafel curves are plotted to determine the corrosion current density (Icorr) and the corrosion potential (Ecorr). In addition to this, corrosion behavior is analyzed by the Electrochemical Impedance Spectroscopy (EIS). Before and after corrosion experiments, surface morphology of the samples is analyzed by scanning electron microscope (SEM) equipped with energy dispersive spectroscopy. Also, X-ray Diffraction (XRD) analysis is carried out to investigate the corrosion products present on the surface of the Al-5050 alloy. The results indicate that the corrosion rate is increased with an increase in the surface roughness. The machined max surface shows 21.9% higher corrosion rate compared to the polished surface. Sandblasted surface due to protective layer and homogeneity in surface roughness experiences less corrosion rate than the machined max surface. The larger and deeper grooves in the machined surface lead to severe pitting and higher corrosion. The type of surface roughness plays a significant role in corrosion behavior and cathodic and anodic kinetics.

A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Abstract: Owing to the high nucleation site density and relatively robust behavior, sintered coated surfaces are of great interest for thermal management via pool boiling in many industries/applications such as desalination, electronics cooling, petrochemical, and power sector. The coated surfaces have been extensively used to improve the performance of the pool boiling process over the years. Regardless of a large amount of experimental data on the pool boiling of coated surfaces, no accurate mathematical/empirical approaches have been developed to estimate the heat transfer coefficient of these surfaces. The present study develops an AI-based method to estimate the pool boiling heat transfer coefficient for coated porous surfaces. The proposed AI method can handle the complex nature of the coating characteristics such as porosity, coating thickness, and particle size. Via using deep neural networks, the proposed method is applicable for highly wetting fluids (dielectric liquids), refrigerants, and low-wetting liquid (water). Correlation matrix analysis confirms that porosity, coating thickness, particle size, wall superheat, and surface inclination as well as the thermophysical properties of the working fluids are the best independent variables to estimate the considered parameter. Different deep neural networks are designed and evaluated to find the optimized model in terms of its predictive accuracy by experimental data (373 points). The best model with an input layer, three hidden layers, and an output layer (11–30–15–1–1) was able to predict the heat transfer coefficient with overall R2 = 0.976 and (mean absolute error) MAE% = 5.74. The proposed approach is simple and can be employed to optimize the sintered coated surfaces for different cooling applications.