This paper proposes visualization based on augmented reality (AR) for aerodynamics simulation in a sustainable cloud computing environment that allows the Son of Grid Engine different types of computers to perform concurrent job requests. A simulation of an indoor air-purification system is performed using OpenFOAM computational fluid dynamics solver in the cloud computing environment. Post-processing converts the results to a form that is suitable for AR visualization. Simulation results can be displayed on devices, such as smart phones, tablets, and Microsoft HoloLens. This AR visualization allows for users to monitor purification of indoor air in real time.

Wave power is an eco-friendly power generation method. Owing to the highly volatile nature of wave energy, the application of prediction techniques for power generation, failure diagnosis, and operational efficiency plays a key role in the successful operation of wave power plants (WPPs). To this end, we propose the following approaches: (i) deriving the correlation between highly volatile data such as wave height data and sensor data in an oscillating water column (OWC) chamber; (ii) development of an optimal training model capable of accurate prediction of the state of the wave energy converter (WEC) based on the collected sensor data. In this study, we developed a big data analysis system that can utilize the machine learning framework in KNIME (an open analysis platform), and to enable smart operation, we designed a training model using a digital twin of an OWC–WEC that is currently in operation. Using various machine learning models, the pressure of the OWC chamber was predicted, and the results obtained were tested and evaluated to confirm its validity. Furthermore, the prediction performance was comparatively analyzed, demonstrating the excellent performance of the proposed CNN-LSTM-based prediction model.

In this study, stability evaluation is performed through structural analysis based on digital dental implant design variables. The design variables include the implant length and thickness, cortical bone thickness, and elastic modulus of the cancellous bone. Subsequently, the stress in the external cortical bone, in which numerous nerves exist, is analyzed. Results show that stress increases as the implant length decreases. However, when the implant length is 10 mm, the stress decreases, owing to stress dispersion at the lower section of the implant. Moreover, as the implant thickness increases, the stress decreases. As the elastic modulus of the cancellous bone decreases, the stress exerted on the cancellous bone decreases; consequently, the stress exerted on the cortical bone increases. Finally, as the thickness of the cortical bone increases, the stress decreases when a vertical load is applied. However, when a load is applied in the oblique direction, the stress increases. Based on data obtained via digital radiography, which is a digital dental technology, a more precise implantation plan will be established by substituting the data via structural analysis.