ScholarWorks@Hanyang University: Lee, Sang Hwan

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Lee, Sang Hwan (이상환)

COLLEGE OF ENGINEERING (SCHOOL OF MECHANICAL ENGINEERING)

Research Area: 유체역학, 비선형역학, 유체기계설계

Tel 02-2220-0445
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Lattice Boltzmann method:6||Finite element method:5||Fluidstructure interaction:4||Flexible plate:3||Fluid structure interaction:3||convolutional neural network (CNN):2||Compute unified device architecture (CUDA):2||Flapping flexible plate:2||tree data structure:2||treecode:2||dual phase steels:2||octtree:2||Computational fluid dynamics:1||Computational modeling:1||Convolutional Neural Networks:1||Convolutional neural networks:1||Curved boundary:1||DSMC:1||Data Augmentation:1||Direct simulation Monte Carlo (DSMC) method:1||Discrete bubble method (DBM):1||Discrete element method:1||Discrete element method (DEM):1||Drag correlation:1||Drag force:1||Drag force model:1||Effective Thermal Conductivity:1||Efficiency:1||Feature extraction:1||Finite Volume Method:1||Flapping wing:1||Fouling:1||Free stream velocity:1||Fruit fly:1||Graphic processor unit (GPU):1||Ground Effect:1||Ground effect:1||Highvacuum:1||Image Segmentation:1||Immersed boundary:1||Immersed boundary method:1||Knudsen number:1||Lagrangian method:1||LagrangianLagrangian simulation:1||Lattice Boltzmann:1||Lattice Boltzmann Method:1||Lattice Boltzmann method (격자볼츠만법):1||Lattice Boltzmann node refill:1||Length scales:1||Low Reynolds number:1||Machine Vision:1||Molecular Dynamics(분자동역학):1||Motor cooling fan:1||Moving boundary:1||Moving particle semiimplicit (MPS) method:1||Multiobjective optimization:1||Nbody simulation:1||Nafion:1||Nanoparticles:1||Nanochannel(나노채널):1||Noise reduction:1||Nonisothermal:1||Numerical methods:1||PEMFC:1||Printing system:1||Propulsion efficiency:1||Propulsion velocity:1||Propulsive velocity:1||Radar polarimetry:1||Shape optimization:1||Shielding effect:1||Si:1||Speckle:1||Surface Roughness(표면 거칠기):1||Symmetric Airfoil:1||Synthetic aperture radar:1||Thermal Conduction Analysis:1||Time stepping method:1||Unsteady lifting surface theory:1||Venturi Effect:1||Volume fraction:1||Vortex reconnection (와류재연결):1||Vortex ring (와류고리):1||Vortex structure (와류구조):1||Wing Tip Vortex:1||carbon enrichment:1||carbon nanotube (CNT):1||cellcentered scheme:1||computational fluid dynamics (CFD):1||conditional normalization:1||deep learning:1||deep learning (DL):1||deformation:1||dense block:1||dilated convolution:1||drag coefficient:1||ellipsoidal particle:1||encoderdecoder (ED):1||ferrite transformation:1||fluid structure interaction (FSI):1||generative adversarial networks (GANs):1||heat transfer:1||image inpainting:1||interface wettability(경계 습윤):1||ktree:1||lightweight architecture:1||long shortterm memory (LSTM):1||longrange force:1||matrix solver:1||metals:1||molecular dynamics (MD):1||nano flow:1||nanoindentation:1||nanoparticle:1||nucleation rate:1||plasticity:1||recurrent neural network (RNN):1||selfattention:1||severe plastic deformation:1||speckle noise suppression:1||synthetic aperture radar (SAR):1||unstructured grid:1||work hardening:1||격자중심방법:1||공기층:1||공력특성:1||끝단와:1||대칭익형:1||데이터 증식:1||머신 비전:1||벤츄리효과:1||비정렬격자:1||열전도해석:1||유한체적법:1||유효열전도계수:1||이미지 영역 분할:1||전산해석:1||전열해석:1||지면효과:1||합성곱 신경망:1||Adaptive relaxation times:1||행렬계산:1||Aerodynamic Characteristics:1||Aerodynamic characteristics:1||Agglomerate:1||Aggregate:1||Air Cavity:1||Airfoil section:1||Attention module (AM):1||Boltzmann method:1||Bubble column:1||CFD:1||Cake layer:1||

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