Quantitative estimation of corrosion rate in 3C steels under seawater environment

An artificial neural network method is proposed to correlate the relationship between the corrosion rate of 3C steels with seawater environment factors. The predictions with the unseen test data are in good agreement with experimental values. Further, the developed model used to simulate the combined effect of environmental factors (temperature, dissolved oxygen, salinity, pH values, and oxidation-reduction potential) on the corrosion rate. 3D mappings remarkably reveal the complex interrelationship between the input environmental parameters on the output corrosion rate. The quantitative estimation of corrosion by virtual addition/subtraction of environmental factors individually to a hypothetical system helps to understand the impact of each parameter

Experimental Investigation on Evaluation of Thermal Performance of Solar Heating System Using Al2O3 Nanofluid

Over the years, solar collecting systems have gained interest in renewable energy. This study investigated improving the efficiency of the working fluid in thermal solar systems by using nanofluids with three concentrations of alumina, 0.1, 0.3, and 0.5 wt%. The UV-vis absorbance, electronic conductivity, and thermal transfer properties of the nanofluids were analyzed, and the thermal changes with exposure to solar radiation in an experimental collector system were measured by pyranometer. The electronic conductivity, thermal conductivity, and UV-vis absorbance increased with the alumina concentration. Moreover, the temperatures of the nanofluids increased more under solar irradiation than that of distilled water. This implies that the alumina nanofluids absorb solar energy more efficiently than water. The findings of this study suggest that the use of both alumina nanofluids and nanoparticles will improve the efficiency of thermal solar power systems

Torque Analysis of Scotch Yoke Type Hydraulic Actuator

At present, hydraulic actuators have been strongly highlighted in the shipbuilding industrial applications. The actuators are classified into three types according to their operation, such as scotch yoke, rack and pinion, rod and crank type. However, hydraulic actuators are more difficult than others electric motors because of nonlinear flow-pressure characteristics and many cases of torque between moving parts. In this paper, in order to obtain the data of the valve locking torque value of the scotch yoke, the pressure was applied at a pressure of 6~16 MPa and analysis by depending on cylinder diameter and yoke length using ANSYS tool. According to the results of generating torque value, it is understood that the torque increases as the cylinder diameter increases and the moment arm becomes longer. It can be also seen that the all average error rates of experimental values and program analysis values are only under the 3%

Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively

Corrigendum to “Quantitative estimation of corrosion rate in 3C steels under seawater environment” [J Mater Res Technol vol. 11 (March–April 2021) 681–686]

The authors regret to inform you that there are two corresponding authors for this article (B.B. Panigrahi and N. S. Reddy). Corresponding authors: 1. B.B. Panigrahi, 040-23016555, [email protected] 2. N. S. Reddy, 055-7721669, [email protected]

Innovative flat-plate solar collector (FPC) with coloured water flowing through a transparent tube

Of all types of solar collector, the flat-plate collector (FPC) has the lowest performance, but is the most widely used because of its low cost and easy maintenance. To effectively collect solar light in the conventional FPCs, metal tubes with a high thermal conductivity are installed under an absorption plate. However, in this study, in order to take advantage of the sunlight absorption capacity of coloured water flowing through a tube, a transparent tube was installed on the absorbing plate. The resulting new FPC suggested in this study is a direct absorption solar collector (DASC). To investigate its performance as a function of the colours of the working fluid, four colours of water were supplied to the FPC: transparent (pure water), red, violet and black. From the experimental results, the new FPC suggested in this study was found to have about 5% higher performance than those of the conventional types of FPC, which means that the new concept of FPC can profitably replace the conventional FPCs. © 2019 The Royal Society of Chemistry.1