• Smart Structures and Systems
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• 제20권6호
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• pp.669-682
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• 2017

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation.

• 한국가시화정보학회지
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• 제21권2호
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• pp.83-90
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• 2023

The shape of an airfoil is a critical factor in determining aerodynamic characteristics such as lift and drag. Aerodynamic properties of an airfoil have a decisive impact on the performance of various engineering applications, including airplane wings and wind turbine blades. Therefore, it is essential to analyze the aerodynamic characteristics of airfoils. Various analytical tools such as experiments, computational fluid dynamics, and Xfoil are used to perform these analyses, but each tool has its limitation. In this study, airfoil parameterization, image recognition, and artificial intelligence are combined to overcome these limitations. Image and coordinate data are collected from the UIUC airfoil database. Airfoil parameterization is performed by recognizing images from image data to build a database for deep learning. Trained model can predict the aerodynamic characteristics not only of airfoil images but also of sketches. The mean absolute error of untrained data is 0.0091.

• 한국추진공학회지
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• 제2권3호
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• pp.1-9
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• 1998

풍력에너지는 자연의 에너지를 이용하므로써 환경문제와 경제적 측면에서 다른 대체 에너지보다 훨씬 유리하여 세계 여러 나라에서 각광을 받고 있다. 경제적인 이유로 풍력발전을 위한 회전날개가 대형화 되고 있으며, 경량화를 위해 복합재 구조등의 첨단 항공기술이 적용되고 있는 추세이다. 본 연구에서는 500㎾급 중형 풍력 발전시스템을 개발함에 있어, 적합한 공력 성능을 갖는 경량화 복합재 회전날개의 개선 설계를 수행하였다. 회전날개의 경량화를 위해 기 설계된 쉘-스파 구조물을 쉘-스파-샌드위치 구조물로 설계를 수정하였고, 배선형 해석을 통해서 경량화에 따른 대변형 문제를 검토하였으며, 파괴응력보다 낮은 상태에서 발생되는 국부좌굴에 의한 구조물의 안전성을 검토하였다. 또한, 허브의 금속재 삽입부분의 전단핀에 의한 핀 홀 주위의 응력해석을 수행하여 충분히 안전함을 확인하였고, 수정 설계된 구조물이 운용구간내에서 공진이 발생하지 않음을 확인하였다.