JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Dr. Yasuhiro Yoshida (Postdoc Researcher, The University of Tokyo)

Abstract: Reducing skin-friction drag in wall-bounded turbulent flows is an important issue for engineering applications. Traveling-wave control, in which a sinusoidal wall-normal deformation propagates in the streamwise direction, is a promising drag-reduction technique. This study investigates its performance and underlying mechanisms in a spatially developing turbulent boundary layer through wind-tunnel experiments. The drag reduction was evaluated using the von Kármán momentum integral equation, indicating a global drag-reduction rate of 9.4% over the controlled length. Turbulent statistics show that the drag reduction is achieved through suppression of turbulent momentum transfer. Measurements at multiple downstream stations reveal that turbulence suppression is initially confined to the near-wall region and subsequently spreads toward the outer region. The reduction in turbulent intensities exhibits a progressive diffusion from the near-wall region, following a power law with downstream distance. Two-point correlations indicate reduced streamwise coherence of buffer-layer streaks induced by the traveling wave.

Speaker: Mr. Juhyeong Lee (Ph.D. student, Hanyang University)

Abstract: In molten salt reactors the fuel itself flows, carrying delayed-neutron precursors out of the core; a change in pump speed then redistributes them around the loop and alters the reactivity. Capturing this demands both the system-wide flow and the detailed in-core precursor field. We present a multiscale scheme that couples a 1D system code, resolving the whole-loop flow, to a CFD solver that resolves the in-core distribution of the six precursor groups and weights them by an adjoint importance from OpenMC; the reactivity then follows from this importance-weighted in-core source. The scheme is validated against the MSRE pump-transient benchmark using corrected experimental data. The measured pump-coastdown reactivity, a swing of about 200 pcm, is reproduced in agreement with the data, while a small phase lead at the onset of startup reflects the uncertainty of the estimated flow itself and appears in existing system codes as well. Adjoint weighting proves essential to this agreement, and in axisymmetric transients the 3D solution collapses onto the 1D one, confirming the consistency of the coupled multiscale scheme.

Speaker: Dr. Shaghayegh Zamani Ashtiani (Postdoc Research Associate, Tohoku University)

Author list: Shaghayegh Zamani Ashtiani, Soshi Kawai, and Kai Fukami

• Department of Aerospace Engineering, Tohoku University 

Abstract: Many unsteady aerodynamic flows appear complicated with a range of spatiotemporal length scales, yet the dynamics that govern them are often far simpler. Reduced-order models can extract this simplicity, but their design depends on the flow physics of interest. This talk centers on two-dimensional transonic buffet, a flow described by millions of degrees of freedom whose periodic nature leads to low-dimensional underlying dynamics. We compress the flow with proper orthogonal decomposition followed by a nonlinear autoencoder, whose structured latent space yields a three-dimensional embedding encoding buffet phase, frequency, and angle of attack. Inspired by the supercritical Hopf bifurcation, this embedding supports a parametrized reduced representation for quasi-steady pitching as well as flow-field reconstruction from sparse sensors. Towards the end of the talk, we also briefly introduce our recent efforts on time-dependent basis-based modal analysis that linearly evolves the bases directly in time rather than fixing them as in conventional approaches with an example of extreme vortex gust encounter. These complementary strategies show that encoding prior knowledge of flow physics in the model design is key to parametrization and, ultimately, surrogate modeling of unsteady aerodynamic flows.

Speaker: Mr. Hanbi Kang (Ph.D. student, Dong-A University)

Abstract: Computational fluid dynamics has become an essential tool for solving a wide range of engineering problems. However, the substantial cost of repetitive high-fidelity simulations limits its use in applications requiring rapid or long-term predictions. This challenge has motivated the development of reduced-order models (ROMs), which capture the dominant dynamics of high-dimensional systems in low-dimensional spaces. In particular, Koopman operator theory provides a promising framework for representing nonlinear dynamics through linear evolution in a space of observable functions. Building on this framework, Koopman autoencoders (KAEs) use neural networks to learn nonlinear latent representations while modeling their temporal evolution with a linear Koopman matrix. Despite their potential, conventional KAEs may suffer from accumulated errors and unstable long-term predictions when the spectral properties of the Koopman matrix are not properly constrained. In this work, we present KAEs that impose structural constraints on the latent dynamics. Using unsteady flow-field data, we compare the proposed approaches with a conventional KAE and examine how these constraints affect long-term prediction accuracy and stability. This presentation highlights the potential of structured latent dynamics for developing stable data-driven ROMs.

Speaker: Dr. Ye Won Kim (Postdoc Research Associate, Max Planck Institute)

Abstract: Shallow cumulus clouds over tropical oceans play a fundamental role in the Earth’s energy budget, yet the microphysical processes linking cloud droplets to precipitation remain poorly understood, particularly the interplay between turbulence, entrainment, and rapid droplet growth. Using high-resolution in-situ measurements from the EUREC⁴A campaign (tropical Atlantic, Jan–Feb 2020), we investigate how droplet size distributions relate to their centimeter-scale spatial organization within shallow cumulus clouds. By combining planar Particle Image Velocimetry (PIV) and holographic imaging, we simultaneously characterize local turbulence and three-dimensional droplet fields. Our analysis reveals that droplet clustering coincides with enhanced local turbulence, and that droplet size distributions differ systematically between clustered and void regions. These findings provide insights into how small-scale turbulence and spatial organization influence droplet growth pathways and contribute to precipitation formation in shallow cumulus clouds.

Speaker: Mr. Takuya Takase (Graduate student, Science Tokyo)

Abstract: Accurate prediction of urban wind fields is important for disaster mitigation and safe drone operations, but building-resolving simulations are too computationally expensive for real-time use. Existing super-resolution approaches can accelerate high-resolution flow prediction, yet still rely on building-resolving simulations. We propose a convolutional-neural-network-based three-dimensional super-resolution (3DSR) method that reconstructs building-scale temperature and velocity fields directly from mesoscale meteorological fields. The model is trained and evaluated using building-resolving large-eddy simulation (LES) data, and its transferability to a different urban area is examined using limited-data fine-tuning. The proposed method achieves temperature and velocity inference errors comparable to those reported in previous studies, while delivering a substantially greater speedup of approximately 680-fold relative to high-resolution LES. Fine-tuning improves accuracy in unseen urban areas, demonstrating the potential of 3DSR for fast and transferable urban micro-meteorological prediction.