Hanyang University · Engineering
Ki-Yong Oh 교수의 연구실은 해양 풍력 기초 구조물의 설계 및 안정성 분석, 풍력 터빈 블레이드의 구조적 건강 모니터링, 그리고 리튬이온 배터리의 열폭주 예측과 제어를 핵심으로 하는 다학제적 연구를 수행하고 있습니다. 특히 지반의 기계적 특성과 구조-지반 상호작용, 실시간 센서 기반 진단 알고리즘, 물리기반 신경망을 활용한 열폭주 예측 모델링 등 응용 기술과 이론적 기반을 융합한 연구가 두드러집니다. 해양 에너지와 첨단 에너지 저장 기술의 안전성 및 신뢰성을 높이기 위한 실용적이고 정교한 솔루션 개발에 초점을 맞추고 있습니다.
Figures are computed from collected data and may differ slightly.
This paper reviews foundations for offshore wind energy convertors considering the significant growth of offshore wind energy since the early 2000s. The characteristics of various foundation types (i.e., gravity, pile, suction caisson, and float type) and the current status of field application are discussed. Moreover, the mechanical characteristics of soil are described in the sense that these characteristics including modulus, strength, damping, and modulus degradation of soil play critical ro
A new diagnostic method is proposed to efficiently monitor the structural health and detect damages in wind turbine blades. A high-resolution real-time blade condition monitoring system that considers the harsh turbine operating environment and uses optical sensors and a wireless network is presented. A hybrid algorithm, which merges probabilistic analysis, design loads, and real-time load estimates, is introduced to enhance operational safety and reliability. Moreover, the alarm limits are upda
In this study, a multiphysics-informed neural network (MPINN) is proposed for the estimation and prediction of thermal runaway (TR) in lithium-ion batteries (LIBs). MPINNs are encoded with the governing laws of physics, including the energy balance equation and Arrhenius law, ensuring accurate estimation of time and space-dependent temperature and dimensionless concentration in comparison to a purely data-driven approach. Specifically, the network is trained using data from a high-fidelity model
Open papers in the app to read, cite, and organize with AI.
