فهرست مطالب s. hossain

A computational fluid dynamic (CFD) and machine learning approach is used to investigate heat transfer on NASA airfoils of type NACA 0012. Several different models have been developed to examine the effect of laminar flow, Spalart flow, and Allmaras flow on the NACA 0012 airfoil under varying aerodynamic conditions. Temperature conditions at high and low temperatures are discussed in this article for different airfoil modes, which are porous mode and non-porous mode. Specific parameters included permeability of 11.36 x 10-10 m2, porosity of 0.64, an inertia coefficient of 0.37, and a temperature range between 200 K and 400 K. The study revealed that a temperature increase can significantly increase lift-to-drag. Additionally, employing both a porous state and temperature differentials further contributes to enhancing the lift-to-drag coefficient. The neural network also successfully predicted outcomes when adjusting the temperature, particularly in scenarios with a greater number of cases. Nevertheless, this study assessed the accuracy of the system using a SMOTER model. It has been shown that the MSE, MAE, and R for the best performance validation of the testing case were 0.000314, 0.0008, and 0.998960, respectively, at K = 3. However, the study shows that epoch values greater than 2000 increase computational time and cost without improving accuracy. This indicates that the SMOTER model can be used to classify the testing case accurately; however, higher epoch values are not necessary for optimal performance.

Arsenic (As) contamination in the groundwater of Bangladesh is one of the major public health concerns. It has become a challenge to remove As from groundwater and a great deal of efforts employed in this regards with limited success. Cerium oxide is one of the important medias of arsenic removal techniques. Nine units of cerium-based arsenic technology were tested with seven different well waters in five hydro-geological areas in Bangladesh. Interestingly, the same technology showed variable results in terms of As removal performance from well water. Therefore, this study aimed to investigate the reasons behind the variant performance of the As removal technology. The studied wells were contaminated with a range of 283 to 873 μg/L of arsenic, 0.35 to 10.4 mg/L of iron, 0.29 to 6.83 mg/L of phosphate, 32.5 to 49.5 mg/L of silicate, 0.08 to 0.25 mg/L of sulfate and pH range was 7.11 to 7.65. The cerium-based As removal technology consistently produced As safe water from three wells containing more than 80% As (III) of total arsenic (As) and >3 mg/L of iron and reduced As concentration to below 50 μg/L consistently but failed at other four wells containing less than 75% As (III) of tAs and