Simulation-driven Structure Design LabSeoul National University

연구실소개자료

시뮬레이션주도구조설계연구실김도년교수님지도

Simulation-driven Structure Design Laboratory

Simulation-driven Structure Design LabSeoul National University

▶ Research Area

Finite Element Analysis

Multi-scale Simulations

Structural DNA Nanotechnology

Mechanical Metamaterials

Computational Lithography

▶ General description on SSDL (more info. on http://ssdl.snu.ac.kr)

The main goal of our lab is to develop and use novel simulation methods to expedite the design-

analysis-validation process of structural system in engineering as well as to advance our understanding

on the underlying mechanism of various structures in nature.

We design mechanical meta-materials and DNA-based nanostructures, analyze properties with

computational simulation, and validate the results experimentally. Our simulation covers multiple scales

from the atomic to continuum level for design and analysis of structures for various applications, and

we also develop FEA modelings. Recently, we study lithography patterns using deep-learning methods.

▶ Contact

E-mail: dnkim@snu.ac.kr

Lab.:

Room 304, 307 Building 314

Tel.: 02)880-7145

Introduction

Do-Nyun Kim

Associate Professor

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Structural DNA Nanotechnology

▪What is DNA origami?

▪Applications

▪ Research Goal

: Development of integrated DNA origami design platform

Experimental validationMultiscale/MultiphysicsComputational analysis

Design methods

▪ CAD-based intuitive platform▪ Automated design procedure

▪ Single-molecule measurement▪ Quality analysis▪ Operation test

▪ Self-assembly of DNA nanostructures▪ Precise 2D/3D shape formation▪Wide design flexibility

▪ Nanosensors and actuators▪Molecular container▪ Nanophotonics

Annealing

Short DNAs

Long DNA

Nat. Comm. 7,10935 (2016) Science 335, 831 (2012) ACS Nano 10, 7303 (2016)

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Structural DNA Nanotechnology▪ Multiscale Analysis

Dynamic propertiesGlobal shape prediction

RMSF

12-helix-bundle

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Structural DNA Nanotechnology▪ Shape / Mechanical property control

Polymorphic structure

Modular twist control

Stiffness control

▪ Functional / Dynamic DNA structure

Graphene

Liposome

Metal Oxide

Functional DNA structures

Morphing-Domino mechanism

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Mechanical Metamaterials: Auxetics▪What is Auxetics?

▪Applications

▪ Research Goal

: To control mechanical properties using auxetics

▪Mechanical meta-materials▪ Negative Poisson’s ratio▪ Various auxetic behaviors

▪ Tubular medical devices▪Wave control systems▪ Non-pneumatic tires

Specific patterns Auxetic behaviors

Pre-curved tubes

Plaque

Vessel

Stent

Auxetic structure design

▪ Unified auxetic unit cell definition▪ Property-oriented structure design

Poisson’s ratio

Wave propagation

Thermal properties Stability

Stiffness

Mechanical property design

Simulation-driven Structure Design LabSeoul National University

Mechanical property control

Research Area

▪ Mechanical Metamaterials: Auxetics

Shape-morphing (kirigami) Patterned structure design, simulation

▪ Numerical optimization analysis▪ Developable & non-developable surface design

▪ Static, dynamic property design using auxetics▪ FEA-based linear/nonlinear analysis

Undeformed Tension Shear

Auxetic tube

▪ Unit pattern

▪ Patterns in structure▪ Property map

▪ Designing unit patterns with homogenization▪ Controlling mechanical properties▪ Validating with experimental results

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Deep-learning-based Computational Lithography

▪ Hotspot Detection, Prediction and Correction

SEM

HotspotExpose

CAD

ExpectedHotspot location

CAD

Deep learning model

Expected SEM

Correction

CAD

▪ Lithography Pattern Matching and Clustering

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Deep-learning-based Computational Lithography

▪ Lithography Simulation and Mask Design

Designed CAD

Target Mask

Print silicon wafer

OPC

What is OPC?

Print silicon wafer

Faster than conventional OPC

ConventionalOPC

Deep learningguided OPC

OPC simulator

Deep learning model

Designed CAD

Target MaskIteration

Target Mask

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Computational Mechanics

▪ The developed continuum mechanics based beam elements can accurately and efficiently describe the

complex mechanical behavior under various loading and boundary conditions.

▪ Nonlinear Finite Element Analysis

Simulation-driven Structure Design LabSeoul National University

Research Area

▪ Computational Mechanics

▪ Constitutive Material Modeling

▪ We are developing elastoplastic constitutive models for metallic structures that can predict, for example,

precisely the reduced stretcher strain on the auto outer panels in forming analysis with the result of

leveling simulation.

Stre

ss

YPP

Strain

Steel showing YPP

Leveler

YPP reduced

Strain

Stre

ss

YPP reduced

Stretcher strain

Leveling Simulation

Simulation-driven Structure Design LabSeoul National University

▪ Thermo-mechanical Analysis and Fatigue Life Prediction

Research Area

▪ Computational Mechanics

Memory (DRAM) : Creep fatigue analysis

Turbomachinery : Thermo-mechanical analysis

Strain Energy Density Accumulated per Cycle

Number of Cycles to Crack Initiation

Crack Growth Rate

Fatigue Life

V

VWΔΔWave

Fatigue Life prediction

2K

ave10 ΔWKN Crack Initiation:

Crack Growth: 4K

ave3 ΔWKdN

da

da/dN

aNα 0W

Here, K1 through K4 are material parameters.