Ki‐Yong Oh
Hanyang University · Engineering
About the Lab
Professor Ki-Yong Oh's research lab specializes in the development of advanced monitoring systems and physics-informed modeling for sustainable energy systems and battery safety. The lab focuses on structural health monitoring of wind turbine components, offshore wind foundation design considering soil-structure interaction, and predictive modeling of thermal runaway in lithium-ion batteries using multiphysics-informed neural networks. Key research directions include real-time condition monitoring, failure detection in renewable energy infrastructure, and the integration of physical laws with data-driven algorithms for improved accuracy and reliability.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This 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
Research Areas
Dive deeper into Ki‐Yong Oh's research on Nubint
Open this lab's papers in the app to read with AI, summarize, and cite in your writing.