Research

Vision

Our research aims to understand and engineer complex fluid processing by integrating transport phenomena, rheology, and data-driven modeling. Many materials used in energy and environmental systems—such as particle suspensions, battery slurries, catalyst inks, and polymer melts—behave as complex fluids. During industrial processes including mixing, coating, and flow transport, these materials undergo continuous microstructure evolution, which strongly influences their rheological behavior, transport properties, and ultimately their performance.

Our goal is to uncover the process–structure–performance relationships governing these systems and to develop predictive frameworks for designing and optimizing advanced manufacturing processes.

Themes

1. Transport and Rheology of Complex Fluids

We focus on understanding the fundamental transport and rheological behavior of complex fluids, including particle suspensions, polymer melts, and thixotropic materials. We investigate how flow fields influence microstructure evolution and how these changes affect macroscopic properties such as viscosity, stress response, and transport behavior.

Key topics

Colloidal particle transport and deposition
Mixing and chaotic flow dynamics
Thixotropic fluid dynamics
Nonlinear rheology of particle suspensions
Suspension microstructure and particle networks

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.
S.Y. Jung et al., Phys. Fluids 37 (2025) 093132.

2. Complex Fluid Processing

We investigate how complex fluids behave during industrial manufacturing processes. In many advanced materials systems, such as batteries and fuel cells, the performance of the final product strongly depends on the processing history of functional slurries and suspensions. We study how flow, deformation, and mixing influence microstructure evolution during processing.

Key topics

Battery electrode slurry processing
Catalyst ink formulation and coating
Microfluidic mixing for nanoparticle systems
Electrochromic film processing
Mixing and coating of particulate suspensions

Selected publications

S.Y. Jung et al., Desalination 532 (2022) 115732.
B.U. Youn et al., Phys Fluids. 36 (2024) 103125.
R.J. Garcia et al., Appl. Surf. Sci. 719 (2026) 165134.

3. Data-Driven Process Engineering

To better understand and predict complex fluid behavior, we develop data-driven approaches that integrate physics-based modeling with machine learning techniques. These approaches enable improved prediction of rheological properties and provide new tools for optimizing complex fluid processing in industrial applications.

Key topics

Machine learning for viscosity prediction
Generative-AI-based fouling prediction
AI-assisted optimization of mixer geometry
Data-driven modeling of complex fluids
Integration of CFD and machine learning

Selected publications

We are currently developing data-driven approaches for optimizing complex fluid processing.

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