JK-FLOW Japan-Korea Fluid Mechanics Online Workshop

Speaker: Dr. Yelyn Ahn (Postdoctoral Research Associate, Seoul National University)

Abstract: Smoothed Particle Hydrodynamics (SPH) has emerged as a powerful mesh-free method for simulating complex multi-physics phenomena, including severe accident scenarios in nuclear power plants. However, the inherently high computational cost of SPH simulations has limited its practical application. To overcome this limitation, we have developed a dynamic load balancing algorithm for the multi-GPU parallelization of SPH simulations. This approach effectively distributes computational workloads across GPUs, addressing challenges from dynamically evolving, non-uniform particle distributions. In this work, we present the development and application of one-dimensional and two-dimensional dynamic load balancing techniques and demonstrate their effectiveness through large-scale simulations relevant to nuclear severe accident conditions, such as IVR-ERVC and corium spreading scenario. The proposed methods significantly improve computational efficiency and scalability, enabling high-resolution simulations within practical time frames. This advancement provides a crucial step toward realistic modeling of accident progression and mitigation strategies in nuclear safety research.

Speaker: Dr. Ming Liu (Project Research Associate, The University of Tokyo) [GS]

Abstract: Turbulent simulations with wall models are commonly used approaches to produce high fidelity flow fields with acceptable computational cost. However, most existing wall models are built under certain assumptions, which can affect their adaptivity to practical turbulent flows. To this end, we develop a novel wall model based on a deep neural network, namely, discriminator, which can discriminate instantaneous under-resolved and well-resolved flow fields. The fully developed velocity fields from direct numerical simulations (DNSs) on fine and coarse grids are performed and then adopted as the datasets to train the discriminator. Then, the well-trained discriminator is implemented into DNSs on coarse grids as a wall model. This dynamically updating the instantaneous velocity fields so as to make them indistinguishable from well-resolved ones through body force. The turbulent flow under bulk Reynolds number of 4600-40000 are investigated. As the discriminator-based wall model is introduced, the predicted wall shear stress, mean and rms velocity profiles are significantly improved compared with DNSs on coarse grids without a wall model.

Speaker: Dr. Misa Ishimura (Assistant Professor, Yokohama National University)

Abstract: In a falling liquid film where surface waves induced by Kapitsa instability exist, it is known that heat/mass transfer are enhanced when a counter-current turbulent gas flow is applied, but on the other hand, the risk of flooding increases. We investigate the mechanism of flooding through experiments, 2D modeling, and linear stability analysis (LSA). One of the potential causes of flooding is absolute instability (AI). Using open-domain calculations with 2D model, we investigated the effects of AI, and we found that because the linear spatial growth rate of AI is unbounded, the absolute frequency is selected near the liquid inlet, and highly regular nonlinear surface waves are generated without causing flooding. In experimental studies, we reproduced a type of flooding called ripples, which are upward waves with wavelengths much shorter than typical downward long-waves (LW). Based on these experiments, we performed temporal LSA and identified three different instability modes: LW, new short-wave (SW) and new merged mode. In particular, the latter two instability modes showed negative velocities, suggesting that the ripples observed in the experiment were caused by SW mode.

Speaker: Dr. Ryungeun Song (Assistant Professor, Chungbuk National University)

Abstract: Electrohydrodynamic (EHD) jetting is a versatile technique for producing fine fibers or micro/nano-sized droplets, regardless of ink properties. The formation of a stable cone-jet, driven by the interaction between interfacial tension and electric forces, is central to its success. However, achieving this regime is challenging, as it requires measurement of various fluid properties, including viscosity, conductivity, permittivity, and surface tension. To address these challenges, we performed simulations based on the leaky-dielectric model to analyze cone-jet formation under various conditions. Our study reveals that cone-jet morphology is governed by key non-dimensional parameters, such as the Ohnesorge number (Oh), Weber number (We), electric capillary number (CaE), and relaxation parameter (α). This understanding allows us to predict how jet shapes respond to changes in these parameters and helps guide optimization toward a desired cone-jet regime. These findings support the development of a data-driven optimization system, such as Bayesian optimization. The simulation results enable automatic tuning of operating conditions based on observed jet shapes, even when the ink properties are unknown. This approach provides a foundation for more efficient and adaptive EHD jetting systems.