https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/35 2026-04-04T17:09:19Z 2026-04-04T17:09:19Z Jung, Hyunsung Ryu, Keun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/127166 2025-11-25T07:30:24Z 2026-05-01T00:00:00Z

Title: The Role of Recess Configuration and Working Fluid in Hybrid Bearing Performance Authors: Jung, Hyunsung; Ryu, Keun Abstract: Hybrid bearings, combining hydrostatic and hydrodynamic lubrication principles, are essential for supporting modern high-speed, high-power cryogenic rocket engine turbopumps. These bearings provide the necessary stiffness and load-carrying capacity for stable operation across a wide range of speeds. This study investigates design strategies for hybrid bearings lubricated with various fluids, including air, helium, liquid methane (LCH<inf>4</inf>), liquid oxygen (LOX), liquid nitrogen (LN<inf>2</inf>), and water. Each fluid presents unique challenges related to pressure, flow, and operating conditions, necessitating precise modifications in bearing geometry. A comprehensive analysis is conducted to assess the influence of key design parameters on both static and dynamic bearing performance. These parameters include the recess depth, recess area, recess aspect ratio, number of recesses, and arrangement of recesses. The impact of these factors on bearing stiffness and load capacity is evaluated across a range of operating conditions, including bearing unit load, rotor speed, eccentricity, and fluid supply pressure. Dimensionless parameters, such as the ratio of bearing area to recess area, bearing length to recess length, and bearing clearance to journal radius, are employed to characterize bearing behavior. This research provides valuable insights and practical guidelines for selecting hybrid bearing geometry for diverse fluids and operating conditions. By elucidating the complex interplay between fluid properties and bearing configurations, this study contributes to the advancement of high-speed rotating machinery design, particularly for demanding cryogenic applications. © 2025 Elsevier B.V., All rights reserved.

2026-05-01T00:00:00Z Lim, Homin Ryu, Keun https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/127237 2025-12-11T08:00:20Z 2026-05-01T00:00:00Z

Title: On the Performance of Hybrid Thrust Bearings: Recess Design and Lubricant Influences Authors: Lim, Homin; Ryu, Keun Abstract: Hybrid thrust bearings are essential components in high-performance turbomachinery, particularly in applications experiencing significant static axial loads or dynamic axial load fluctuations. Their ability to combine hydrostatic and hydrodynamic lubrication mechanisms results in superior stiffness and load-carrying capacity, crucial for maintaining rotor stability and precision. This paper investigates the relationship between recess design parameters and lubricant properties to optimize the performance of hybrid thrust bearings across a wide spectrum of operating conditions. The research encompasses a comprehensive analysis of various recess configurations. The impact of the recess geometric parameters on performance of thrust bearing is evaluated for a diverse range of lubricants, encompassing gas like air, water, and cryogenic fluids such as liquid methane and liquid oxygen. By meticulously considering factors such as static thrust load, rotational speed, and fluid supply pressure, the study aims to provide an extensive understanding of bearing behavior. The influence of recess design on critical performance metrics, namely, stiffness and load capacity, is thoroughly assessed. This comprehensive analysis facilitates the development of a robust design framework, offering practical guidelines for tailoring bearing geometry to specific lubricants and operational requirements. Furthermore, the research delves into the interplay between lubricant properties and the bearing’s recess configuration under specific machinery operating conditions. This investigation elucidates optimal design strategies for achieving superior performance in diverse applications, including cryogenic environments and high-speed machinery. The findings contribute valuable insights to the field of fluid film bearings for cryogenic applications, enabling engineers to design more efficient and reliable hybrid thrust bearings for the demanding requirements of modern machinery. © © 2026 by ASME.

2026-05-01T00:00:00Z Hua, Yufeng Tian, Xin Chen, Yunbing Tian, Zhihua Oh, Jehoon https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/127224 2025-12-12T00:00:17Z 2026-03-01T00:00:00Z

Title: An optimized machine learning methodology leveraging computational fluid dynamics-derived hemodynamic features for enhancing prediction accuracy of thin-walled regions in intracranial aneurysms Authors: Hua, Yufeng; Tian, Xin; Chen, Yunbing; Tian, Zhihua; Oh, Jehoon Abstract: Thin-walled regions (TIWRs) in intracranial aneurysms (IAs) are closely associated with high-risk spontaneous rupture and can increase intraoperative rupture likelihood due to incomplete wall visualization. While computational fluid dynamics (CFD)–derived hemodynamic features (HFs) are valuable biomedical signals for predicting TIWRs, reliance on any single HF yielded unsatisfactory accuracy, and multiple HF combinations were difficult to further enhance accuracy due to the subjectively determined weighting coefficients. Machine learning (ML) algorithms can coordinate multiple HFs to facilitate prediction, yet a hasty application without hyperparameter tuning cannot maximize accuracy. Here, we develop an optimized ML methodology leveraging CFD-derived HFs to enhance the prediction accuracy of TIWRs in IAs. Given the structures of IA mesh-surface datasets, a nested leave-one-patient-out cross-validation framework was followed to conduct feature selection and improved grey wolf optimizer (IGWO) hyperparameter tuning for ten candidate ML algorithms, thereby determining the ML algorithm with the superior performance and enabling external generalization testing. The results showed that hyperparameter tuning by IGWO—which integrated chaotic mapping, nonlinear convergence factor, and adaptive position update—universally boosted the prediction accuracy. Among ten ML algorithms, Light Gradient Boosting Machine (LightGBM) exhibited the superior predictive performance: on the external testing case, it achieved an accuracy of 91.9 %, a precision of 92.9 %, a recall of 93.9 %, and areas under the relevant curves all exceeding 0.94. Therefore, IGWO-tuned LightGBM leveraging CFD-derived HFs has the potential to enhance preoperative TIWRs prediction accuracy in IAs, thereby reinforcing pretherapy guidance, reducing intraoperative rupture, and providing a methodological foundation for large-scale clinical translation. © 2025 Elsevier Ltd

2026-03-01T00:00:00Z Kang, Minseok Hong, Sukjoon Choi, Joonmyung https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126703 2025-12-01T05:30:25Z 2026-02-01T00:00:00Z

Title: Molecular mechanics study on mechanical load transfer of dielectric layer to copper pad Authors: Kang, Minseok; Hong, Sukjoon; Choi, Joonmyung Abstract: To enable high-density 3D integration in advanced semiconductors, reliable bonding technologies with thermal resistance and diffusion-blocking properties are essential. While various wafer bonding methods have been proposed, bonding stability along the sidewall interface between dissimilar materials remains insufficiently studied. This research uses all-atom molecular dynamics simulations to characterize mechanical load transfer at the Cu conductor-SiCN dielectric interface. Specifically, the impact of surface composition changes caused by pre-annealing of SiCN on interfacial bonding strength was thoroughly investigated. Results revealed that carbon nanoclusters precipitated onto the SiCN surface at elevated temperatures significantly enhance bonding energy with Cu. Additionally, high-temperature silicon nitride formation alters surface roughness and stress distribution. These findings offer atomic-level insights into optimizing SiCN/Cu interface reliability and mechanical rigidity.

2026-02-01T00:00:00Z