Papers by Rudi P Wijayanto
Applied sciences, Mar 4, 2020
To properly conduct a micro-siting of an orthopter-type vertical axis wind turbine (O-VAWT) in th... more To properly conduct a micro-siting of an orthopter-type vertical axis wind turbine (O-VAWT) in the built environment, this study investigated the effects of horizontal shear flow on the power performance characteristics of an O-VAWT by performing wind tunnel experiments and computational fluid dynamics (CFD) simulations. A uniform flow and two types of shear flow (advancing side faster shear flow (ASF-SF) and retreating side faster shear flow (RSF-SF)) were employed as the approaching flow to the O-VAWT. The ASF-SF had a higher velocity on the advancing side of the rotor. The RSF-SF had a higher velocity on the retreating side of the rotor. For each type of shear flow, three shear strengths (Γ = 0.28, 0.40 and 0.51) were set. In the ASF-SF cases, the power coefficients (C p) were significantly higher than the uniform flow case at all tip speed ratios (λ) and increased with Γ. In the RSF-SF cases, C P increased with Γ. However, when Γ = 0.28, the C P was lower than the uniform flow case at all λ. When Γ = 0.51, the C P was higher than the uniform flow case except at low λ; however, it was lower than the ASF-SF case with Γ = 0.28. The causes of the features of C P were discussed through the analysis of the variation of blade torque coefficient, its rotor-revolution component and its blade-rotation component with azimuthal angle by using the CFD results for flow fields (i.e., horizontal velocity vectors, pressure and vorticity). These results indicate that a location where ASF-SFs with high Γ values dominantly occur is ideal for installing the O-VAWT.
JURNAL NASIONAL TEKNIK ELEKTRO
COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT ... more COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.
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Papers by Rudi P Wijayanto