https://scholarworks.unist.ac.kr/handle/201301/80 2026-04-08T00:28:23Z 2026-04-08T00:28:23Z Jin, Howon Yoon, Sangwoong Park, Frank C. Ahn, Kyung Hyun https://scholarworks.unist.ac.kr/handle/201301/91295 2026-04-07T04:09:58Z 2023-09-30T15:00:00Z

Title: Data-driven constitutive model of complex fluids using recurrent neural networks Author(s): Jin, Howon; Yoon, Sangwoong; Park, Frank C.; Ahn, Kyung Hyun Abstract: This study introduces the Constitutive Neural Network (ConNN) model, a machine learning algorithm that accurately predicts the temporal response of complex fluids under specific deformations. The ConNN model utilizes a recurrent neural network architecture to capture the time dependent stress responses, and the recurrent units are specifically designed to reflect the characteristics of complex fluids (fading memory, finite elastic deformation, and relaxation spectrum), without presuming any equation of motion of the fluid. We demonstrate that the ConNN model can effectively replicate the temporal data generated by the Giesekus model and the Thixotropic-Elasto-Visco-Plastic (TEVP) fluid model under varying shear rates. To test the performance of the trained model, we subject it to an oscillatory shear flow, with periodic reversals in flow direction, which has not been trained on. The ConNN model successfully replicates the shear moduli of the original models, and the trained values of the recurrent parameters match the physical prediction of the original models. However, we do observe a slight deviation in the normal stresses, indicating that further improvements are necessary to achieve more rigorous physical symmetry and improve the model prediction.

2023-09-30T15:00:00Z Khanal, Shalil Liu, Yuanhang Bamidele, Adebowale O. Wixom, Alexander Q. Washington, Alexander M. Jalan-Sakrikar, Nidhi Cooper, Shawna A. Vuckovic, Ivan Zhang, Song Zhong, Jun Johnson, Kenneth L. Charlesworth, M. Cristine Kim, Iljung Yeon, Yubin Yoon, Sangwoong Noh, Yung-Kyun Meroueh, Chady Timbilla, Abdul Aziz Yaqoob, Usman Gao, Jinhang Kim, Yohan Lucien, Fabrice Huebert, Robert C. Hay, Nissim Simons, Michael Shah, Vijay H. Kostallari, Enis https://scholarworks.unist.ac.kr/handle/201301/91292 2026-04-07T04:03:37Z 2024-05-31T15:00:00Z

Title: Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release spatially to amplify liver fibrosis Author(s): Khanal, Shalil; Liu, Yuanhang; Bamidele, Adebowale O.; Wixom, Alexander Q.; Washington, Alexander M.; Jalan-Sakrikar, Nidhi; Cooper, Shawna A.; Vuckovic, Ivan; Zhang, Song; Zhong, Jun; Johnson, Kenneth L.; Charlesworth, M. Cristine; Kim, Iljung; Yeon, Yubin; Yoon, Sangwoong; Noh, Yung-Kyun; Meroueh, Chady; Timbilla, Abdul Aziz; Yaqoob, Usman; Gao, Jinhang; Kim, Yohan; Lucien, Fabrice; Huebert, Robert C.; Hay, Nissim; Simons, Michael; Shah, Vijay H.; Kostallari, Enis Abstract: Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 (RAB31) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

2024-05-31T15:00:00Z Yi, Woobin Kim, Dae Yeon Jin, Howon Yoon, Sangwoong Ahn, Kyung Hyun https://scholarworks.unist.ac.kr/handle/201301/90773 2026-04-07T03:23:52Z 2025-05-31T15:00:00Z

Title: Early detection of pore clogging in microfluidic systems with 3D convolutional neural network Author(s): Yi, Woobin; Kim, Dae Yeon; Jin, Howon; Yoon, Sangwoong; Ahn, Kyung Hyun Abstract: In this study, we investigate whether the clogging phenomenon in a particulate suspension can be predicted from earlier observations of the system. Our research focuses on a microfluidic model system of polystyrene particles dispersed in a glycerol solution, where the onset of clogging can be controlled by adjusting the solution viscosity and flow rate. The microfluidic system allows for optical observations of the flow channels, providing detailed information on how particles are deposited in the flow passage. Using data collected from this model system, we developed a predictive algorithm based on 3D convolutional neural networks (3D CNN) that estimates the probability of clogging onset in the future based on past video frames of the system. Our results show that the 3D CNN can accurately predict clogging even under experimental conditions not encountered during training. The 3D CNN model with a depth of 9 was able to detect clogging after just 25 min, even though the actual clogging occurred after 118 min. This performance is superior compared to the 2D CNN, which detected clogging in 35 min under the same conditions. The high predictive performance indicates that the evolution of particle positions in the early stages of flow contains the necessary information for predicting clogging onset. Our findings have practical implications for the possibility of data-driven predictive maintenance of flow systems.

2025-05-31T15:00:00Z Lee, Minwoo Yoon, Sangwoong Kim, Juhan Wang, Yuangang Lee, Keeman Park, Frank Chongwoo Sohn, Chae Hoon https://scholarworks.unist.ac.kr/handle/201301/90532 2026-02-23T06:46:04Z 2022-02-28T15:00:00Z

Title: Classification of impinging jet flames using convolutional neural network with transfer learning Author(s): Lee, Minwoo; Yoon, Sangwoong; Kim, Juhan; Wang, Yuangang; Lee, Keeman; Park, Frank Chongwoo; Sohn, Chae Hoon Abstract: Depending on the equivalence ratio and the Reynolds number, impinging jet flames exhibit several modes of thermoacoustic oscillation. In this study, we present a machine-learning-based method for classifying the regimes of thermoacoustic oscillation. We perform transfer learning to train the convolutional neural network model designed to classify flame images. We show that an accurate classification of impinging jet flames is achieved with an accuracy of 93.6 % by using just a single snapshot image. This study constitutes the first demonstration of transfer learning in classifying fluid images, opening up new possibilities for robust image-based diagnostics of various fluid and combustion systems.

2022-02-28T15:00:00Z