Investigation of Simple Shape Descriptors for NACA 4 Digit Airfoils

Year 2024, , 570 - 579, 23.12.2024

Haydar Tuna , Özcan Yırtıcı

https://doi.org/10.19113/sdufenbed.1570399

Abstract

This study aims to define new simple shape descriptors to analyze airfoils. The ImageJ platform is used to calculate twelve different shape descriptors such as area, convex hull, contour temperature and solidity by performing image processing. One of the most important findings is that an increase in the thickness of an airfoil leads to corresponding increases in its area, perimeter, area of minimum enclosing area, and convex hull area. Another noteworthy discovery is that the values derived from these basic features, either increasing or decreasing. Simple shape features in the study are not used independently, as they do not possess distinct characteristics that set them apart from one another. Machine learning and deep learning applications can achieve greater success when these features are combined with other elements. The combination of these features with other shape attributes, such as chain code histograms, shape signatures, and central moments, can enhance the success of machine learning and deep learning applications.

Keywords

Shape representation, Image processing, Airfoil

NACA 4 Kanat Profillerinin Basit Şekil Temsil Yöntemleriyle İncelenmesi

Abstract

Bu çalışma, kanat profillerini analiz etmek için yeni basit şekil belirteçlerini tanımlamayı amaçlamaktadır. ImageJ platformu, görüntü işleme gerçekleştirerek alan, dışbükey gövde, kontur sıcaklığı ve katılık gibi on iki farklı şekil tanımlayıcısını hesaplamak için kullanılır. En önemli bulgulardan biri, bir kanat profilinin kalınlığındaki artışın, alanında, çevresinde, minimum çevreleyen alan alanında ve dışbükey gövde alanında karşılık gelen artışlara yol açmasıdır. Dikkat çekici bir diğer keşif ise, bu temel özelliklerden türetilen değerlerin artması veya azalmasıdır. Çalışmada basit şekil özellikleri, onları birbirinden ayıran belirgin özelliklere sahip olmadıkları için bağımsız olarak kullanılmamaktadır. Makine öğrenimi ve derin öğrenme uygulamaları, bu özellikler diğer öğelerle birleştirildiğinde daha büyük başarı elde edebilir. Bu özelliklerin zincir kod histogramları, şekil imzaları ve merkezi momentler gibi diğer şekil nitelikleriyle birleştirilmesi, makine öğrenimi ve derin öğrenme uygulamalarının başarısını artırabilir.

Keywords

Şekil temsili, Görüntü işleme, Kanat profili

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