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- Four-University Mechanical Engineering Graduate Student Workshop 2026 (Yonsei Univ., Korea Univ., Keio Univ., Waseda Un
- Four-University Mechanical Engineering Graduate Student Workshop 2026 (Yonsei Univ., Korea Univ., Keio Univ., Waseda Univ.) The research groups of Professor Kyoungmin Min and Professor Seok Kim from the Department of Mechanical Engineering participated in the Four-University Mechanical Engineering Graduate Student Workshop 2026, held from August 5 to 7, 2026, at Keio University in Japan. The workshop is an international academic exchange event jointly organized by the mechanical engineering departments of Keio University, Korea University, Waseda University, and Yonsei University, and this year's edition was hosted by the Department of Mechanical Engineering at Keio University. Thirteen faculty members and about forty graduate students from the four departments took part, sharing their research results and exploring opportunities for joint research. Yonsei University was represented by Professor Kyoungmin Min, Professor Seok Kim, and six graduate students, who delivered two oral presentations and four poster presentations. The oral presentations introduced research on machine learning interatomic potentials, optical and process engineering approaches for high-performance DLP 3D printing, and the design of metamaterials with physical intelligence, while the poster presentations covered battery research based on machine learning and first-principles calculations, as well as extreme manufacturing technologies combining metamaterial design with 3D printing. Participants discussed future cooperation, including graduate student research exchange, the identification of joint research topics, and the pursuit of international joint research projects, centered on shared interests such as AI-driven materials design, 3D printing and metamaterials, and sensors and biomechanics.
- 기계공학부 2026.08.11
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241
- Yonsei University Department of Mechanical Engineering Hosts the 2026 Summer International Intensive Lecture Series on B
- Yonsei University Department of Mechanical Engineering Hosts the 2026 Summer International Intensive Lecture Series on Bioengineering Convergence The Department of Mechanical Engineering at Yonsei University successfully hosted the 2026 Summer International Intensive Lecture Series from July 20 to 24, 2026. The lecture series, titled "Mechanics Meets Genomics: Modern Biotech, Synthetic Biology, and Therapeutic Engineering for Mechanical Engineers," was delivered by Professor Sungjin Park of Georgia Institute of Technology and Emory University. Approximately 15 graduate students participated in the program, which covered a wide range of cutting-edge topics in modern biotechnology and therapeutic engineering. The curriculum included Basic Electrophysiology, CRISPR gene-editing technology, CAR-T cell therapy, stem cells, tissue engineering, and biofabrication, providing participants with a comprehensive introduction to key concepts and emerging technologies in the field. Through the program, students gained a deeper understanding of the convergence between mechanical engineering and life sciences, while broadening their perspectives on the latest developments in biomedical research and next-generation therapeutic technologies. The Department of Mechanical Engineering at Yonsei University plans to continue organizing its International Intensive Lecture Series by inviting distinguished scholars from around the world, providing students with globally competitive educational opportunities and strengthening their research capabilities in advanced interdisciplinary engineering fields.
- 기계공학부 2026.08.03
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240
- 2026 AI Summer School' Successfully Held at Yonsei University (2026.07.15)
- 2026 AI Summer School' Successfully Held at Yonsei University The '2026 Artificial Intelligence Summer School', hosted by the Machine Artificial Intelligence Division (Chair: Sejin Lee) of the Korean Society of Mechanical Engineers (KSME), was successfully held over three days from July 13 (Mon) to July 15 (Wed), 2026, at Auditorium B106, Engineering Hall, Yonsei University (Seoul). The summer school was an intensive educational program covering a broad range of topics, from the core theories of artificial intelligence to the latest research trends, and comprised a total of five lectures. On Day 1, Prof. Beomsu Park (Seoul National University of Science and Technology) lectured on 'Generative AI (VAE, GAN, Diffusion, Flow-based Models)'. On Day 2, Prof. Taejin Kim (Jeonbuk National University) presented 'Physics-Informed AI (PINN, DeepONet, Neural Operators)', and Prof. Chanhee Park (University of Seoul) covered 'LLM and AI Agent (Principles and Training of LLMs, RAG, Ontology)'. On Day 3, Prof. Jongmoon Ha (Ajou University) delivered 'Foundation Models (Self-supervised Learning, Transfer Learning)', and Prof. Kyoungmin Min (Department of Mechanical Engineering, Yonsei University) lectured on 'Efficient Learning (Active Learning, Domain Adaptation, and Beyond)'. In particular, Prof. Kyoungmin Min of Yonsei University's Department of Mechanical Engineering provided a comprehensive overview of the landscape of Data-Efficient Learning, starting from real-world engineering problems where labeled data is scarce. He introduced recent research trends and practical methodologies extending from the query strategies of Active Learning to Domain Adaptation and Autonomous Discovery. The event was conducted on a first-come, first-served basis for 120 participants and concluded amid strong interest from both industry and academia.
- 기계공학부 2026.07.22
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239
- Yonsei Mechanical Engineering Hosts 2026 Summer Intensive Course Series
- Yonsei Mechanical Engineering Hosts 2026 Summer Intensive Course Series The School of Mechanical Engineering at Yonsei University hosted the 2026 Summer Intensive Course Series from July 1 to July 3. The course, titled "Reinforcement Learning and AI @ Yonsei," was jointly taught by Professor Chang-hee Won of Temple University and Professor Jongbaeg Kim of Yonsei University. The program brought together 54 undergraduate and graduate students and covered the fundamentals of reinforcement learning, including Markov Decision Process, dynamic programming, Monte Carlo learning, temporal-difference learning, and model-free control. Through hands-on exercises using Python and Google Colab, participants implemented reinforcement learning algorithms and explored their applications in robotics, manufacturing, healthcare, and other engineering fields, gaining practical experience with state-of-the-art AI technologies. The School of Mechanical Engineering will continue to expand its international educational collaborations by inviting leading researchers from around the world, providing students with opportunities to learn cutting-edge technologies and strengthening their global research capabilities through future intensive course programs.
- 기계공학부 2026.07.22
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238
- Publication of Lattice Boltzmann Methodology for Single-Phase and Multiphase Nanoparticle Modeling, Springer
- Publication of Lattice Boltzmann Methodology for Single-Phase and Multiphase Nanoparticle Modeling, Springer The introductory chapters systematically present the fundamental concepts, theoretical background, and relevant previous studies of LBM. The book then provides an in-depth explanation of LBM methodologies for single-phase and multiphase flows. It also introduces various approaches to nanoparticle modeling, allowing readers ranging from graduate students encountering LBM for the first time to researchers in related fields to develop their knowledge in a step-by-step manner. The application section analyzes a wide range of physical phenomena involving multiphase flows containing nanoparticles through LBM-based simulations. These include double emulsions, porous structures, inkjet printing, and the coffee-ring effect. The book also addresses topics closely related to practical engineering and physical systems, including interfacial behavior, contact-line dynamics, multiphase flow analysis under high-density-ratio conditions, particle interactions, and particle deposition patterns. The book was published by an imprint of the global academic publisher Springer Nature in June 2026. The link: https://link.springer.com/book/10.1007/978-981-95-9117-6
- 기계공학부 2026.07.22
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237
- Development of Non-Contact Ethanol Molecular Sensing Technology Using Multilayer Graphene Fresnel Lenses and Deep Learni
- Development of Non-Contact Ethanol Molecular Sensing Technology Using Multilayer Graphene Fresnel Lenses and Deep Learning The research team led by Professor Seong Chan Jun of the Department of Mechanical Engineering, together with Geon Mo Kim and Yun Ji Hwang, integrated Ph.D. students as co-first authors, and Teajong Hwang, an integrated Ph.D. student as a co-author, developed a non-contact ethanol molecular sensing technology using multilayer graphene Fresnel lenses and deep learning. Conventional ethanol detection technologies have mainly relied on precision analytical instruments such as gas chromatography or contact-based electrochemical and semiconductor sensors. However, these approaches have limitations, including bulky equipment, long analysis times, and performance degradation of sensing materials caused by repeated chemical reactions. In this study, the research team focused on the extremely weak Rayleigh scattering effect that occurs when ethanol molecules interact with laser light. Instead of directly measuring the scattered light, they proposed a method that analyzes subtle changes in the focal spot of the main laser beam, which is slightly distorted by scattering. To achieve this, the team used a Fresnel lens fabricated from five-layer graphene to focus light through diffraction and extracted ethanol-concentration-dependent changes in focal intensity, width, and shape as optical fingerprints. In addition, they applied a self-developed Self-Aware Assembly Network (SAAN) deep-learning model and implemented an integrated system capable of inferring ethanol content in the range of 0.01%–0.1% in a non-contact manner. This technology enables optical analysis of ethanol molecular information without chemical reactions or direct contact with sensing materials, suggesting potential applications in non-invasive alcohol testing, breath-gas-based disease diagnosis, and hazardous gas monitoring in industrial environments. The study was published in Opto-Electronic Advances (IF: 22.4, JCR top 4.3%). The paper was selected as Editor's Choice. The link: https://www.oejournal.org/oea/article/doi/10.29026/oea.2026.250278
- 기계공학부 2026.07.22
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236
- Overcoming Durability Limits in SOEC Stacks through Mass-Transfer Control (2026.05.21)
- Overcoming Durability Limits in SOEC Stacks through Mass-Transfer Control Professor Jong-Seop Hong’s research team, including M.S. graduate Janghyun Lim and Ph.D. graduate Woo-seok Lee, has developed a mass-transfer-controlled stack architecture to overcome the durability limitations of solid oxide electrolysis cell (SOEC) stacks through collaborative research with Hyundai Motor Company and the Korea Institute of Science and Technology (KIST). The research team focused on a critical issue observed in commercial large-area SOEC stacks: the concentration of steam and current density near the fuel inlet, which accelerates the degradation of the Ni–YSZ fuel electrode microstructure. To address this problem, the team introduced a slit-sheet structure patterned with fine slits, enabling more uniform fuel supply, diffusion, and electrochemical reaction distribution within the stack. Through long-term harsh operation tests, three-dimensional multiphysics simulations, and post-mortem FE-SEM analysis, the researchers demonstrated that the proposed structure effectively alleviates the high-humidity and highly reactive environment near the inlet region. As a result, the structure suppresses nickel depletion and coarsening, as well as damage to the electrical percolation network. Notably, the optimized stack reduced the degradation rate over 500 hours from approximately 8% in the conventional stack to about 3%, while also maintaining stable durability during long-term operation for more than 2,000 hours. This study is significant in that it presents a practical and scalable design strategy for improving the durability of high-temperature water electrolysis systems by controlling internal transport phenomena within the stack, without requiring any modification of the electrode materials. The research findings were published in Joule, a prestigious international journal in the energy field, which has an Impact Factor of 37.1 and ranks in the top 1.3% of the JCR Energy & Fuels category as of 2025. The link: https://doi.org/10.1016/j.joule.2026.102485
- 기계공학부 2026.07.22
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235
- Development of a Liquid-to-Gas Phase-Change Actuator and Flexible Tactile Display for Sophisticated Tactile Feedback
- Development of a Liquid-to-Gas Phase-Change Actuator and Flexible Tactile Display for Sophisticated Tactile Feedback The research team led by Prof. Jongbaeg Kim in the Department of Mechanical Engineering at Yonsei University has developed a flexible tactile display incorporating a liquid-to-gas phase-change-based microactuator array that simultaneously achieves high spatial resolution and large actuation displacement. Conventional flexible tactile displays have been limited in delivering sophisticated tactile information due to their centimeter-scale actuator cells, while phase-change-based actuators have faced challenges in practical applications because of their slow response speed and high power consumption. To address these limitations, the research team developed a high-resolution actuator array with a spatial resolution comparable to that of tactile receptors in the human fingertip, enabling an ultrathin flexible tactile display with a response time on the order of several hundred milliseconds. Furthermore, the device demonstrated stable operation on curved surfaces and was integrated with a virtual reality (VR) system to deliver pressure-based tactile stimuli directly to the user's fingertips, providing synchronized visual and tactile feedback for an immersive haptic interface. This technology is expected to find broad applications in next-generation wearable tactile interfaces, including electronic skin (e-skin), wearable healthcare, virtual and augmented reality (VR/AR), and human–machine interfaces (HMIs). The research was published in Microsystems & Nanoengineering, the top-ranked journal among 81 journals in the Instruments & Instrumentation category. The link: https://www.nature.com/articles/s41378-026-01288-z
- 기계공학부 2026.07.22
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234
- Mechanically-triggered self-powered triboelectric sensor platform with arbitrary-to-constant mechanical input conversion
- Mechanically-triggered self-powered triboelectric sensor platform with arbitrary-to-constant mechanical input conversion The research team led by Professor Jongbaeg Kim at the School of Mechanical Engineering, Yonsei University, developed a self-powered sensing platform capable of reliable environmental/chemical sensing regardless of variations in external mechanical inputs by converting irregular mechanical motion into consistent vibrations. The researchers incorporated a magnetic latching mechanism into a triboelectric nanogenerator consisting of a cantilever and a sagged flexible film, enabling the release of stored elastic energy and the generation of free vibration once the input displacement exceeded a predefined threshold. As a result, the platform generated stable electrical signals with less than 9.6% output variation despite changes in input displacement (25–35 mm) and input frequency (0.1–1 Hz). Furthermore, by implementing humidity and ammonia sensors on the same platform simply by replacing the active sensing layer, the researchers demonstrated that stable self-powered sensing performance could be achieved regardless of variations in mechanical input even when different sensing materials were employed. The platform also achieved a maximum output power of 170.8 μW and maintained stable operation over 20,000 repeated cycles, demonstrating its potential as a universal self-powered sensing platform for wearable and portable environmental and chemical sensing applications. This work was published in Microsystems & Nanoengineering. The link: https://www.nature.com/articles/s41378-026-01306-0
- 기계공학부 2026.07.22
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233
- Thermodynamic Performance Analysis of SOFC Systems under Different Fuels and System Configurations
- Thermodynamic Performance Analysis of SOFC Systems under Different Fuels and System Configurations Professor Jong-Seop Hong’s research team, including Ph.D. student Gwangnam Jeong and M.S. student Jiyong Lee, conducted a study comparing and analyzing the thermodynamic performance of solid oxide fuel cell (SOFC) systems according to changes in input fuel and system configuration. The research team focused on three types of fuels—hydrogen, methane, and ammonia—and two types of system configurations, with and without fuel recirculation, to investigate how each system design and operating condition affects thermodynamic performance. Furthermore, the team incorporated balance-of-plant (BoP) design elements, including heat exchangers that reflect actual geometric information, to construct more realistic SOFC system models. Based on this approach, they quantitatively compared the pressure losses that may occur in practical SOFC systems and provided a quantitative assessment of the performance loss relative to theoretical system performance. This study offers meaningful insights into the design and operation of practical SOFC systems by clarifying how fuel selection, recirculation strategy, and BoP configuration influence overall system efficiency and performance. The research findings were published in Energy Conversion and Management: X, a prestigious international journal in the field of thermodynamics, which has an Impact Factor of 8.8 and ranks in the top 4.5% of the JCR Thermodynamics category as of 2025. The link: https://doi.org/10.1016/j.ecmx.2026.101799
- 기계공학부 2026.07.22