Design and Optimization of the Wing Fence of a Lambda-Shaped Aircraft Model to Reduce the Rolling Moment Coefficient

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Moghimi Esfandabadi Mohammad Hossein; Javareshkian Mohammad Hassan

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Journal Paper

Paper Information

Journal: JOURNAL OF TECHNOLOGY IN AEROSPACE ENGINEERING Year:2024 | Volume:8 | Issue:2 Page(s): 13-25

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Information Journal Paper

Title

Design and Optimization of the Wing Fence of a Lambda-Shaped Aircraft Model to Reduce the Rolling Moment Coefficient

Author(s)

Moghimi Esfandabadi Mohammad Hossein | Javareshkian Mohammad Hassan | Issue Writer Certificate

Keywords

Aviation industry

Deterioration prediction

Deep Learning

Turbofan Engine

Supervised Learning

Artificial Neural Networks

Abstract

One of the main propositions of predictive maintenance is Prognostics and Health Management (PHM), which plays a special role in identifying, diagnosing, and predicting the health status of physical assets. To that end, one of the fundamental solutions is to assess the condition of the equipment in the aviation sector in order to provide maintenance plans by determining the trend of deterioration or destruction. In this study, a developed model of an artificial neural network was presented, focusing on the concept of deep learning and its comparison with other conventional methods in response to the limitations and uncertainties in conventional prediction methods in determining the deterioration process of the equipment. The comparative results revealed that the deep learning neural network method with a prediction accuracy of 94% had a high performance in determining the deterioration process in aircraft turbine engines compared to other conventional methods. The findings of this study can be used to predict the remaining useful life of aviation industry equipment as well as to provide appropriate maintenance programs.

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