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All Journal JOIV : International Journal on Informatics Visualization Mekanika: Majalah Ilmiah Mekanika
Defianti, Hanni
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Computer Science & IT

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Geometry Representation Effectiveness in Improving Airfoil Aerodynamic Coefficient Prediction with Convolutional Neural Network Zikri, Arizal Akbar; Defianti, Hanni; Hidayat, Wahyu; Purqon, Acep
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1577

Abstract

Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and heat transfer. Each airfoil has different aerodynamic coefficients. Obtaining the aerodynamic coefficients is a must to optimize the airfoil design. Engineers use various methods to get the airfoil aerodynamic coefficients. A prediction method is an approximation approach that effectively reduces time and cost. This article uses convolutional neural networks (CNN) to get approximation values of those coefficients. In CNN, we collect 8920 aerodynamic coefficients for 223 NACA 4 as labels in datasets by using XFOIL at  and  with varying angles of attacks starting  to  with increment of . The simulation results are compared to the experiment using E387 airfoil for validation. Then, airfoil geometries as part of input datasets were transformed into Grayscale and RGB images using the signed distance function (SDF) and mesh algorithm. Each airfoil representation was trained using an 80% dataset and tested using a 20% dataset with Adam as an optimizer to generate each prediction model using modified LeNet-5. We use three different layer depths in modified LeNet-5 to obtain the optimal layer number. There is no remarkable improvement when varying the depth layers, so four layers are used instead. Simulation results show that using an SDF with Fast Marching Method on CNN predicts the most effective for the airfoil’s lift, drag, and pitch moment coefficient with varying angles of attack simultaneously. One can extend the method by using SDF to recognize different flow conditions.
Predicting the Drag Coefficient Characteristics of Ocean Bottom Unit (OBU) Float Array Model for Early Warning Tsunami Systems Using Computational Fluid Dynamics (CFD) Method Kusuma, Yudiawan Fajar; Hariz, Ilham; Defianti, Hanni; Hakim, Buddin Al; Putra, Arfis Maydino F.
Mekanika: Majalah Ilmiah Mekanika Vol 22, No 2 (2023): MEKANIKA: Majalah Ilmiah Mekanika
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/mekanika.v22i2.75079

Abstract

The early tsunami warning system encompasses several complex components, one of which is the Ocean Bottom Unit (OBU) floater. This paper discusses the performance of various types of floater arrays for tsunami early warning systems using Computational Fluid Dynamics (CFD) simulations. The study focuses on coefficients, especially the drag coefficient, and the influence of the number of float arrangements on the flow pattern around the buoy or Ocean Bottom Unit (OBU) array. Among the five numerical simulation models, the six-couple floater has the highest drag and lowest lift coefficients, while the single floater has the lowest drag coefficient. The percentage of difference in drag coefficient between single floater and couple series floater is quite significant, reaching up to 50%. The moment coefficient is also affected by the number of floaters, with a series of five couple floaters having the highest moment coefficient at a Reynolds number (Re) of 2 × 106. The results indicate that the flow pattern becomes more complex as the number of floater arrays increases, which leads to more vortices between the floater, resulting in increased turbulence and drag coefficient.
Co-Authors Hakim, Buddin Al Hariz, Ilham Kusuma, Yudiawan Fajar Purqon, Acep Putra, Arfis Maydino F. Wahyu Hidayat Zikri, Arizal Akbar