CSMD 연구실 고병현 학생 (석박통합과정)이 2022년 한국멀티스케일역학회 심포지움 에서 “Topology optimization for programmable structures using the multimaterial level set method under finite deformations” (저자: 고병현, 정하영)으로 우수 포스터 논문상을 수상했습니다. 축하드립니다!

CSMD 연구실 오영택 학생 (석박통합과정)이 2022년 한국멀티스케일역학회 심포지움 에서 “The effect of porosity and regularity to energy dissipation in the Voronoi-Tessellated Structure” (저자: 오영택, 정하영)으로 우수 포스터 논문상을 수상했습니다. 축하드립니다!

USACM (미국 전산 역학회)의 Large Scale Structural Systems and Optimal Design 부문(Large scale TTA) 초청으로 멀티스케일 시뮬레이션에 대한 1시간짜리 강연을 하게 되었습니다. 

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November 2, 2022: Prof. Hayoung Chung, Ulsan National Institute of Science and Technology

Title: Understanding Multifunctional Behaviors using Sequential Multiscale Approaches
Abstract: Multiscale methods broadly refer to computational frameworks used to investigate the influence of small-scale parameters on macroscopic behaviors of multifunctional materials, namely low-dimensional materials and smart materials, to just name a few. The way that small-scale information, typically extracted from in-silico experiments (e.g., DFT, MD), is transferred to the macroscopic analysis model (e.g., finite element) distinguishes one multiscale method from the others. Among these, the sequential multiscale method is an alias of a one-way bridging model where the small-scale data forms a basis of a high-fidelity multiphysics surrogate that is utilized as a constitutive model of the macroscopic analysis. The method is especially useful in analyzing structural behaviors of multifunctional materials, where we can assume static equilibrium conditions on both lower and higher scales. However, despite its simplicity and wide usage, the multiscale method is far from being generalized due to its need for massive data and knowledge of the specific domain, which my team and I are trying to eliminate. In this talk, we first aim to explain the broad application of the sequential multiscale method, as it enables a more profound understanding of non-traditional multifunctional materials such as nanomaterials and liquid-crystal solids. After that, we present our recent endeavor toward a scalable and transferable multiscale model using the state-of-the-art machine learning approach. We finalize the talk by introducing our other endeavors toward fast and efficient topology optimization.
Bio: Hayoung Chung is an assistant professor in the Department of Mechanical Engineering at Ulsan National Institute of Science and Technology (UNIST) in Korea since 2019. He obtained his bachelor’s and doctoral degree at Seoul National University (SNU), Korea, in 2010 and 2017. His Ph.D. research focused on analyzing and tailoring multiphysics phenomena in photo-responsive liquid crystal networks. After graduation, he held postdoctoral positions at the Structural Department at UC San Diego, USA, where he worked on several projects revolving around efficient topology optimization of thermoelastic large-deforming structures. His current research interests include topology optimization, data-driven multiscale modeling, and computational mechanics to address issues found in 4D printing technology.

2021년 8월 온라인으로 진행된 학회에서 고병현, 김주헌 연구원이 참석하여 연구 내용을 발표하였습니다.

As of Mar 1. 2021, Mr. Semin Lee graduated from UNIST (School of Mechanical and Aerospace and Nuclear Engineering) and joined the CSMD lab.

He is working on the ML-aided modeling and design of non-conventional materials, including additively manufactured ones.

2020년 12월 16일부터 24일간 온라인 학술대회로 이루어진 2020년 대한 기계학회에

본 연구실 정하영 교수와 강태훈 석사과정생이 참가했습니다.

정하영 교수는 “대변형을 고려한 다중 열탄성 구조체의 위상 최적 설계”에 대한 구두 발표를,

강태훈 연구원은 “Selective enrichment and moment fitting method to analyze elastic structure with weak discontinuity”에 대한 포스터 발표를 수행했습니다.