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http://dx.doi.org/10.5139/JKSAS.2022.50.5.317

Prediction of Vibration Characteristics of a Composite Rotor Blade via Deep Neural Networks  

Yoo, Seungho (Department of Aerospace Engineering, Jeonbuk National University)
Jeong, Inho (Department of Aerospace Engineering, Jeonbuk National University)
Kim, Hyejin (Department of Aerospace Engineering, Jeonbuk National University)
Cho, Haeseong (Future Air Mobility Research Center, Jeonbuk National University)
Kim, Taejoo (Korea Aerospace Research Institute)
Kee, Youngjung (Korea Aerospace Research Institute)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.50, no.5, 2022 , pp. 317-323 More about this Journal
Abstract
In this paper, a deep neural network(DNN) model for predicting the vibration characteristics of the composite rotor blade with c-spar cross section was developed. Herein, the present DNN model is defined by using the natural frequencies obtained through the in-house code based on the nonlinear co-rotational(CR) shell element. For the present DNN model, the accuracy of the model was evaluated via the data with a random distribution of thickness and a tendency to decrease in thickness along the blade span.
Keywords
Cross Section Design; Composite Rotor Blade; Natural Frequency; Deep Neural Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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