JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Mr. Ryo Koshikawa (Undergraduate student, Tohoku University)

Author list: Ryo Koshikawa [1], Ryo Araki [2], Qiong Liu [3], and Kai Fukami [1]

1. Department of Aerospace Engineering, Tohoku University 

2. Department of Mechanical and Aerospace Engineering, Tokyo University of Science 

3. Department of Mechanical and Aerospace Engineering, New Mexico State University

Abstract: Aerodynamicists have aimed to capture dominating features of flow fields to understand complex unsteady flow and aerodynamic phenomena. Indeed, our eyeballs have persistently sought the causal relationship between vortical structures and aerodynamic forces. To grasp such an aerodynamic causal relationship in an interpretable manner, this talk discusses information-theoretic manifold learning that provides a time-varying informative modal structure with respect to variables of interest in the future. The current convolutional neural network-based approach produces spatially continuous modes, which has been challenging to achieve with conventional techniques. Furthermore, a low-order submanifold representing informative vortical structures and the aerodynamic coefficient is identified. The proposed technique is applied to a range of aerodynamic flow examples, including extreme vortex-gust airfoil interaction, experimental measurements of transverse jet-wing interaction, and a turbulent separated wake. We reveal the causal relationship between vortical structures and the lift dynamics relying only on flow data and information metrics. This work will contribute not only to a deeper understanding of unsteady aerodynamics but also to obtaining insights into modeling and controlling fluid flows.

Speaker: Mr. Jaewon Jang (Graduate student, Inha University)

Abstract: Generative models such as GANs and diffusion models have been widely adopted for surrogate modeling and super-resolution in fluid dynamics. However, these approaches, originally developed for computer vision tasks, prioritize generation over prediction and introduce stochastic artifacts through Gaussian noise-based processes, compromising physical consistency in data-scarce regimes. We present the Energy Dissipation Model (EDM), a deterministic framework fundamentally designed for flow field prediction rather than generation. Inspired by the Kolmogorov energy cascade, EDM employs frequency-based progressive refinement, sequentially reconstructing flow structures from large-scale (low-frequency) to small-scale (high-frequency) components. This physics-oriented hierarchy enables accurate predictions with substantially reduced training data while minimizing both global and local reconstruction errors. Unlike existing methods that adapt computer vision techniques to fluid dynamics, EDM establishes an independent framework grounded in turbulence physics, offering a principled approach to data-driven flow prediction that respects the multi-scale nature of turbulent flows.

Speaker: Ms. Morie Koseki (Ph.D. student, Okinawa Institute of Science and Technology)

Abstract: When fluids flow over deformable walls, the flow field is modified by the fluid-wall interactions. The complex coupling can be described by a combination of many undistinguished effects, e.g., roughness effects (due to the wall deformation by the hydrodynamic force), non-zero wall-normal fluctuations (coming from the wall movement), and wall motion (owing to the wave propagation on the surface and inside materials). This study aims to disentangle the effects of fluid-structure interaction and wall shape/undulations individually in a turbulent flow. We conduct direct numerical simulations of turbulent channel flows over deformable and statistically equivalent rough walls.  Turbulent flows over relatively rigid compliant walls share similar features to those over rough walls; however, as wall flexibility increases, distinct effects are observed that are specific to the mutual fluid-structure interaction.

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, Postdoctoral Researcher, RIKEN-CCS [GS]

Abstract: TBA