JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Dr. Innyoung Kim (Postdoc Research Associate, POSTECH, Upcoming Assistant Professor, Sejong University) [GS]

Abstract: Understanding, designing, and controlling multiphysics fluid systems are essential across engineering disciplines. Despite substantial progress, formidable challenges persist: (1) the inherent complexity of tightly coupled physical phenomena that defy precise prediction; (2) the inefficiencies of simulation workflows relying on iterative, expert-driven trial-and-error; and (3) design optimization involving nonlinear, nonconvex, and high-dimensional problems requiring substantial computational resources. To address these issues, this talk presents an intelligent CFD framework including high-fidelity multiphysics CFD for accurate prediction of intricate interactions; AI-enhanced CFD to automate and accelerate iterative simulation workflows; and optimization and control methods for complex fluid systems. Advanced AI techniques are introduced to scale these methods to industrial-scale and system-level design with over 100 design variables. The vision is to advance CFD to autonomously simulate, design, and control complex fluid systems, uncovering solutions beyond human intuition.

Speaker: Dr. Seongsu Cho (Postdoctoral Researcher, University of Pennsylvania) [GS]

Abstract: Droplet microfluidics offers great versatility for applications ranging from functional particle fabrication to biological assays. Despite its potential, manually refining experimental conditions to achieve desired droplet properties remains a significant bottleneck, hindering widespread adoption. To address this, we developed an AI-driven control system for the automated optimization of droplet generation. Integrating convolutional neural networks (CNNs) for real-time analysis and Bayesian optimization (BO) for refining experimental conditions, the system works with minimal human intervention. Due to efficient utilization of datasets in BO, the amount of training datasets was reduced, identifying optimal conditions within 15 iterations on average. We demonstrated robustness of the system across various working fluids, channel geometries, and droplet morphologies. This system is expected to accelerate research using droplet microfluidic system.

Speaker: Mr. Shun Tomizawa (Ph.D. student, The University of Tokyo)

Abstract: Vascular networks play important roles in transporting nutrients and oxygen to sustain life. Organisms may optimize vascular networks based on hemodynamic factors, such as wall shear stress. Elucidating the relationships among vascular geometry, hemodynamic factors, and transport efficiency is of fundamental biological significance and also of engineering significance owing to bio-inspired applications such as high-performance heat exchangers. Despite its importance, studying microcirculation remains challenging due to the difficulty of the measurement, the complexity of red blood cell (RBC) dynamics, and the influence of the biochemical environment on the phenomena. To address these issues, fluid simulation, particularly transport dissipative particle dynamics (tDPD), can be a promising method. tDPD is a coarse-graining of MD and a Lagrangian method. It can handle highly deformable solids such as RBCs, calculate mass transport at high Schmidt numbers, and incorporate chemical effects. In this study, we show that tDPD can reproduce RBC dynamics and predict the concentration field. Through this presentation, we demonstrate that tDPD is a powerful tool for studying complex fluids.

Speaker: Dr. Shintaro Sato (Assistant Professor, Tohoku University) [GS]

Abstract: Modal analysis, including proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), has attracted significant attention to understand or extract the fundamental structures hidden in complex fluid flow dynamics and to develop real-time sensing and control of fluid flows. Reduced-order models (ROMs) describe the dynamics of fluid flows, originally represented in a very high-dimensional space, in a low-dimensional subspace detected by modal analysis. ROMs significantly reduce the computational cost compared with the full-order model and are therefore expected to enable real-time flow-field simulation for active flow control. A major limitation is that conventional ROMs often fail to capture fluid-flow dynamics at parameter values different from those used for modal analysis, because the extracted subspace is optimized for the training snapshot data. In this study, we discuss a framework for parametric POD-based ROM with a focus on the parametric representation of the subspaces on the Grassmann manifold. The smooth variation of the subspace spanned by POD modes with respect to the change of the flow parameters can be described as a smooth curve on the Grassmann manifold. We demonstrate the developed framework through parametric POD-Galerkin ROM and flow-field estimation from limited sensor data based on subspace interpolation on the Grassmann manifold.

Speaker: Dr. Sangwon Kim, Assistant Professor, Kobe University; Visiting Scientist, RIKEN-CCS [GS]

Abstract: This work presents a time-stepping Hamiltonian simulation framework for nonlinear PDEs on a hybrid quantum–classical approach. Using warped phase transform (WPT)–based Schrödingerization, spatial discretizations are reformulated as Hermitian/anti-Hermitian operators for Schrödinger-type equations, enabling unitary propagation even for dissipative systems. In contrast to traditional linearizations (Carleman, KvN) that cause exponential statevector growth and truncation errors on NISQ hardware, we update nonlinear terms classically at each step, incorporate into the Hamiltonian, and calculate it by unitary evolution on the quantum circuit. This local linear approximation over small time intervals prevents dimensional inflation while securing calculation accuracy. We implement the framework in Qiskit and evaluate it with the Qiskit Aer statevector simulator on linear advection–diffusion and nonlinear problems including Burgers and Allen–Cahn phase field models. The results show good agreement with classical solutions, highlighting its potential for efficiently simulating nonlinear dynamics without dimensional inflation.