Our research
Our group is dedicated to the identification of flow physics characteristics and their application in advancing the quantitative analysis and prediction of complex physical phenomena. We specifically focus on studying the human circulatory system as well as real-world engineering problems. By deepening our understanding of hemodynamics, we strive to develop innovative approaches that enhance the treatment of cardiovascular diseases. Our research encompasses various areas, including pediatric cardiovascular disease (both congenital and acquired heart conditions), adult cardiovascular disease, and the development of cardiovascular medical devices.
Furthermore, we explore the industrial applications of multiphase fluid mechanics, spanning laminar and turbulent flows. Leveraging numerical methods and high-performance computing, we aim to non-invasively estimate and predict key quantities of interest in systems involving complex flows. Our ultimate objective is to establish a highly intelligent and robust system design process that combines human intuition, physics, and data-driven decision-making. This integrated approach holds the potential to drive innovation in engineering industry.
To achieve our goals, we plan to integrate state-of-the-art tools and techniques, including high-fidelity multiphysics solvers, computational design optimization, uncertainty quantification, and artificial intelligence. By leveraging these advanced methodologies, we aspire to revolutionize the field, ultimately leading to significant advancements in cardiovascular research, treatment, and engineering practices.
Research areas
Cardiovascular disease
Coronary artery disease
Coronary artery bypass graft surgery
Kawasaki disease
Pediatric patients with single ventricle
Medical device design
Thrombosis modeling
Fluid mechanics
Turbulence
Multiphase flows
Superhydrophobic surface
Liquid metal jetting for additive manufacturing
Simulation-driven design
Numerical methods
Multiphase interface tracking method
Contact line dynamics
Uncertainty quantification
Physics-informed machine learning
Linear solvers
Fluid-structure interaction
Research Projects
Project: Flow quantification in pulmonary arteries in patients with repaired Tetralogy of Fallot (2022- Current)
Collaborators: Seoul National University Hospital (Pediatrics)
Prof. Gi-beom Kim, Seungmin Paik
Funding: National Research Foundation (2023-2026)
Project: Identifying the relationship between Fontan complications and surgical design via computational modeling and hemodynamics simulation
(2022- Current)
Collaborators: Prof. M. Han (KHU/KHMC) Prof. S. Kim (BSH)
Funding: National Research Foundation (2023-2026)
Project: Hemodynamic characterization of coronary aneurysms in patients with Kawasaki disease: Nationwide cohort study (2022- Current)
Collaborators: Korean Society of KD - 10 hospitals Nationwide.
Funding: Kyung Hee University Young Investigator Award (2023-2024)
Project: Risk assessment of intracranial aneurysm rupture using CFD (2023- Current)
Collaborators: Prof. W. Yoon, Neurosurgery, Korea University Guro Hospital, Prof. Khan (Toronto Metropolitan University)
Project: Virtual surgery on congenital heart disease patients (2023-Current)
Collaborators: Prof. J. Kwak, Seoul National University Hospital (Cardiothoracic surgery - pediatrics)
Fundings: National Research Foundation (2023-2026)
Project: Implementation of the conservative phase field method for multiphase flow simulation on unstructured finite volume framework (2021- Current)
Collaborators: PARC, Stanford CTR (Dr. Mirjalili, Prof. Mani).
Funding: KHU Paper Award (2023)
Project: Super-resolution reconstruction of turbulent flows via artificial neural network (2023- Current)
Funding: KHU Next-generation Energy Convergence Graduate Program (2023-2027)
Project: Computational investigation on the effects of slippery surfaces in cardiovascular devices (2022- Current)
Collaborators: Dr. W. Yang (Stanford)
Funding: National Research Foundation (2022-2023)
Before KHU (~2021)
Project: Computational Evaluation of Fractional Flow Reserve of Coronary Artery Aneurysms in patients with Kawasaki disease. (2020- 2022)
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Funding: Private gift funds, GS Design Research.
Collaborators: Prof. Alison Marsden, Dr. Menon (Stanford), Prof. Burns, Prof. Kahn (UCSD).
Project: Virtual evaluation of coronary artery bypass graft surgery (2019-2021)
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Funding: National Institute of Health & National Science Foundation.
Collaborators: Prof. Andrew Kahn (UCSD), Prof. Alison Marsden, Prof. Jack Boyd (Stanford), Dr. Abhay Ramachandra (Yale)
Project: Uncertainty quantification in cardiovascular simulation with clinically-informed data uncertainty (2017-2019)
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Funding: National Institute of Health (NIH), National Science Foundation (NSF).
Collaborators: Prof. Daniele Schiavazzi (University of Notre Dame), Prof. Andrew Kahn (UCSD), Prof. Alison Marsden (Stanford)
Project: Physics-based nozzle design rules for high-frequency liquid metal jetting (2021-2022)
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Funding: DARPA, Xerox Inc.
Collaborators: Morad Behandish (PARC), Svyatoslav Korneev (PARC), Adrian Lew (Stanford)
Project: Turbulent drag reduction by superhydrophobic surfaces for naval applications (2011-2016)
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Funding: Office of Naval Research (ONR) MURI, Kwanjeong Scholarship.
Collaborators: Ricardo Garcia-Mayoral (Cambridge), Ali Mani (Stanford)
MURI participants:
Prof. Steven Ceccio, Prof. Marc Perlin, University of Michigan Ann Arbor
Prof. Gareth McKinley, MIT
Prof. Joseph Katz, Johns Hopkins University
Prof. Krishnan Mahesh, University of Minnesota
Methods and tools
High-Performance Computing (HPC)
Private computing cluster at KHU (Intel Xeon Gold Processors for Scalable Computing)
Amazon AWS
Numerical methods: Finite-volume method (FVM), Finite element analysis (FEA), Finite difference (FD).
Multiphase flow simulation: Volume-of-fluid(VoF), Phase field model(PFM), Linearized model.
Fluid-structure interaction: Arbitrary-Lagrangian-Eulerian framework (ALE), Coupled-momentum method (CMM).
Uncertainty Quantification: Multiresolution stochastic expansion, Multi-level Monte-Carlo, Stochastic Collocation.
Simulation software: Simvascular (3D/1D/0D models), OpenFOAM, In-house turbulent flow code.
Linear solvers: Trilinos, PETSC, FFT, In-house solvers.
In near future, we plan to develop AI-based tools.