• Journal of Advanced Navigation Technology
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• v.25 no.3
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• pp.203-210
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• 2021

In many metropolitan cities around the world, ground and underground transportation networks are saturated due to urbanization. In addition, regulations on carbon emissions to prevent global warming are becoming stricter, and eVTOL, which will be operating in complex cities, is gaining popularity as the next generation of eco-friendly transportation. In this study, the hover lift efficiency and disc loading of eVTOLs for each type were calculated by classifying eVTOLs into following types: multicopter, lift+cruise, and vectored thrust. In addition, using the aerodynamic analysis programs OpenVSP, Fluent and Javaprop, the specific battery energy required for the smooth operation of eVTOL, which will be realized in the near future, was calculated and analyzed base on reports published by Uber and airworthiness authorities of each country.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.7
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• pp.593-599
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• 2021

The demand for e-VTOL aircraft is expected to be increased rapidly in the future as a means of urban transportation due to operating cost reduction, eco-friendliness and convenience of vertical take-off and landing using pilot-aids automation system. However, there are many technical hurdles to be solved in securing safety and certification which are essential for commercialization and urban operation. So far, there is no e-VTOL aircraft that has received type certificate and standard airworthiness certificate due to the technical problems and safety requirement differences with conventional aircraft. The e-VTOL aircraft certification should also meet the equivalent level of safety required by the current airworthiness standards, but there are existing problems in securing safety and meeting current standards. In this study, the e-VTOL's certification problems and technical limitations in satisfying the current standards are presented.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.67-74
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• 2023

Recently, Urban Aircraft Mobility (UAM) has been attracting attention as a transportation system of the future, and small drones also play a role in various industries. The failure of various types of aviation systems can lead to crashes, which can result in significant property damage or loss of life. In the defense industry, where aviation systems are widely used, the failure of aviation systems can lead to mission failure. Therefore, this study proposes an anomaly detection model using deep learning technology to detect anomalies in aviation systems to improve the reliability of development and production, and prevent accidents during operation. As training and evaluating data sets, current data from aviation systems in an extremely low-temperature environment was utilized, and a deep learning network was implemented using the convolutional neural network, which is a deep learning technique that is commonly used for image recognition. In an extremely low-temperature environment, various types of failure occurred in the system's internal sensors and components, and singular points in current data were observed. As a result of training and evaluating the model using current data in the case of system failure and normal, it was confirmed that the abnormality was detected with a recall of 98 % or more.