Research

Research Vision

Our research aims to understand and engineer complex fluid processing by integrating computational modeling, rheological characterization, multiphysics simulation, and data-driven process optimization. Many functional materials used in energy and environmental systems—such as particle suspensions, battery slurries, catalyst inks, and polymer melts—exhibit complex fluid behavior. During industrial processes including mixing, coating, and drying, these materials undergo continuous microstructure evolution, which strongly influences their rheology, transport behavior, and ultimately the performance of the final product.

Our goal is to establish predictive process–structure–performance relationships and to develop engineering frameworks for the design and optimization of advanced materials processing.

Research Area

1. Transport and Rheology of Complex Fluids

We investigate the fundamental transport phenomena and rheological behavior of complex fluids, including particle suspensions, polymer melts, and thixotropic systems. Our research focuses on how flow, deformation, and interparticle interactions drive microstructure evolution, and how these structural dynamics determine macroscopic properties such as viscosity, stress response, and transport characteristics.

Key topics

Colloidal transport and deposition
Nonlinear rheology of complex fluids
Microstructure and particle network evolution

Selected publications

S.Y. Jung et al., J. Membr. Sci. 635 (2021) 119497.
S.Y. Jung et al., Int. J. Heat Mass Transf. 184 (2022) 122310.
M. Hwang et al., Phys. Fluids. 37 (2025) 073389.

2. Computational Modeling and Simulation

We develop computational models and perform multiphysics simulations to understand how complex fluids and functional material systems behave under realistic operating and processing conditions. Our research focuses on how coupled transport, fluid flow, interfacial, and electrochemical phenomena govern structure evolution, process stability, and ultimately material performance across diverse energy and environmental applications.

Key topics

Mixing and chaotic advection
Coating and drying behavior of particulate suspensions
Multiphysics analysis of coupled systems

Selected publications

S.Y. Jung et al., Desalination 532 (2022) 115732
S.Y. Jung et al., Int. J. Mech. Sci. 289 (2025) 110068.
S.Y. Jung et al., Phys. Fluids 37 (2025) 093132.

3. Data-Driven Process Optimization

We develop data-driven frameworks to accelerate the analysis and optimization of complex fluid processing. By combining physics-based understanding with simulation/exeprimental data and machine learning techniques, we aim to build predictive tools that enable efficient exploration of process conditions and design spaces. We perform surrogate modeling, Bayesian optimization, and data-driven analysis to support the rational design and optimization.

Key topics

Machine learning for viscosity prediction
Surrogate modeling for process–property relationships
Data-driven analysis of complex fluids

Selected publications

S.Y. Jung et al., Micromachines 10 (2019) 836.
B.U. Youn et al., Phys Fluids. 36 (2024) 103125.
J. Choi et al., Appl. Math. Model. 150 (2026) 116401

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