FEEL Laboratory
- Turbulent Boundary Layer (난류경계층 유동)
- Artificial Intelligent and Deep Learning (인공지능/딥러닝)
- Renewable (Wind) Enegy (신재생에너지)
- Wall pressure fluctuation; noise prediction (벽면압력변동 및 노이즈예측)
- Computational Fluid Mechanics (수치유체역학)
- Modelling Wind Flow Over Bluff Bodies (물체주위 유동모델링)
- Wind Turbine Design and Development (풍력발전기 설계 및 개발)
- Visualization and Image Processing Technique(유동가시화 및 유속측정)
Research Interests & Experience
1. Deep learning for Super-Resolution Reconstruction of Turbulent Flows (딥러닝을 통한 고해상도 난류유동 재현)
We propose a methodology for reconstructing high-resolution turbulent flow fields using Enhanced Super-Resolution Generative Adversarial Networks.
2. Physics-guided deep learning for generating turbulent inflow conditions (난류 입구조건생성을 위한 물리정보기반 딥러닝)
We utilise the combination of a multiscale convolutional auto-encoder with a sub-pixel convolution layer (MSCSP-AE) and a long short-term memory (LSTM) model to generate turbulent inflow conditions.
3. Deep-learning based Prediction of Transitional Turbulent boundary layer (딥러닝기반 천이 난류유동 예측)
We propose a methodology for generating transition boundary layer using Deep Learning(DL) method. As for the DL model, we use a Transformer network combined with ESRGAN. The architecture of the Transformer is shown above.
4. Modelling Wind Flow over Bodies (물체주위 유동모델링)
Renewable (Wind) Energy, with the popular interest, many projects regarding to wind energy were undertaken. The main items include the prediction of wind energy around the possible wind site (Wolryong, Jeju and Mr. Seung-Hak, Yongdang and Subyun Park, Pusan) and the design optimization of the blade of wind turbine (NREL S821model).
[Wind tunnel experiment in the wind tunnel and UI panel]
[Wind turbine blades used in the wind tunnel]
[Numerical simulation of wind turbine blades used in the numerical tunnel]
Atmospheric Wind Flow over Complex Real Fields, which accounts for all the perturbing effects of nonuniform and unsteady surface-flow conditions, topography and thermal stratification. Numerical and experimental methods for the prediction of the atmospheric wind flow and the pollutant dispersion over complex terrain were investigated. Field measurements were also carried out on a 6 m, smooth-surfaced cube mounted on a turntable in a level, open field site at Silsoe Research Institute(UK).
[Field measurement of wind flow around bodies (Silsoe, UK)]
[Wind tunnel measurement of wind flow around bodies]
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[LES simulation of wind flow around bodies] " >
[LES simulation of wind flow around bodies]
5. Generation of Turbulent Boundary Layer (난류경계층유동 수치생성)
6. Active Flow Control, Plasma Actuator(능동유동제어, 플라즈마 액츄에이터) – DBL induced flow generation
In order to promote an in-depth understanding of the mechanism of leading-edge flow separation control over an airfoil using a Dielectric Barrier Discharge (DBD) plasma actuator excited by high voltage, an experimental investigation of an airfoil with DBD plasma actuator was performed in a closed chamber and wind tunnel.
7. Active Flow Control, Plasma Actuator(능동유동제어, 플라즈마 액츄에이터) – 실험장치 및 setup
8. Active Flow Control, Plasma Actuator(능동유동제어, 플라즈마 액츄에이터) – 강화학습을 이용한 능동유동제어
9. Wall Pressure fluctuation & Noise Predication (벽면변동압력 및 노이즈 예측기법 개발)
10. Turbulent flow around finned tubes
11. Droplets on vib surface (진동표면위 액적변동)
[Droplet motion and its modes placed under vertical oscillation]
