JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

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.

Speaker: Mr. Sungkun Chung (Ph.D. student, POSTECH) [GS]

Abstract: For advanced nuclear and thermal energy storage (TES) systems, medium-Prandtl-number fluids like molten salts and water are widely used as both heat storage and transfer fluids. In vertical tubes, mixed convection, a combination of forced and natural convection, arises from the interaction between bulk inertia and buoyancy induced by temperature gradients. Depending on the flow direction, this interaction causes non-linear behaviors, severely deteriorating or enhancing local heat transfer. Understanding these mechanisms is essential for reliable system design. To analyze these phenomena, continuous temperature profiles are experimentally measured using distributed optical fiber sensors (OFS) under various flow conditions. While inertia-dominant cases exhibit heat transfer similar to forced convection, significant heat transfer variations caused by buoyancy-induced flow distortion are observed in mixed regimes. Based on these findings, we propose mixed convection regime maps and a heat transfer model with force balance. We demonstrate their applicability in predicting non-linear thermal-hydraulics, ultimately improving the design and control accuracy of advanced systems.

Speaker: Ms. Nami Ha (Ph.D. student, Georgia Institute of Technology) [GS]

Abstract: Fluid ejection is a tricky problem in biological systems, especially for tiny insects. Herein, we reveal the physical mechanisms of phloem sap-feeding insects, spotted lanternflies (SLF, Lycorma delicatula), to understand how they efficiently eject fluid droplets under strong capillary constraints. By combining high-speed imaging, micro-CT imaging, kinematic analyses, and fluid property measurements, we uncovered how biological morphology and rapid actuation enable honeydew droplet detachment in the SLF, revealing a developmental mechanism switch from a capillary ratchet in nymphs to an elastic catapult in adults. Further, we simulated reduced-order mathematical models to capture the underlying physics of these distinct ejection strategies and integrates these models into a scaling framework using dimensionless parameters across organisms. Finally, we mapped the actuator and droplet response phase spaces to establish the theoretical kinematic limits of these ultrafast rotational movements and analyze divergent post-launch spinning droplet dynamics. Together, this work would fill critical gaps in our understanding of fluid-ejecting biological systems and provide general design principles for novel bioinspired fluid transport and antifouling devices.

Speaker: TBA

Abstract: TBA

Speaker: Mr. Tomoya Oura (Ph.D. student, Keio University)

Abstract: The aim of this work is to improve particle tracking simulations in filtered turbulent flow fields. Numerical simulations of fluid flow are essential for analyzing various phenomena, such as the transport of small particles. In engineering applications, a large eddy simulation is widely employed using filtered fields to reduce computational costs. However, particle tracking simulations in filtered fields often deteriorate the accuracy of particle motion. To address this issue, this study introduces a defiltering method based on machine-learning techniques to recover filtered velocities. The defiltering model is trained in a minimal turbulent channel flow and evaluated in a larger test domain, demonstrating excellent improvements in several particle statistics. Furthermore, the model successfully reconstructs statistics in curved turbulent channels even though it is trained in a minimal plane channel. However, the results also suggest that careful attention should be paid to the similarity of the model input for robust estimation. This talk highlights the remarkable performance, robustness, and limitations of the proposed defiltering method.