Abstract: Topology optimization has emerged as a promising and powerful approach to design engineered materials and components. Initially restricted to two-phase, solid-void design problems in linear elasticity, topology optimization approaches for multi-physics and multi-material problems have emerged. These problems are often dominated by interface phenomena, such as contact and delamination at material interfaces and boundary layer effects at fluid-solid interfaces. Accurately modeling these phenomena and, at the same time, allowing for topological changes in the optimization process pose interesting challenges on the formulation of the design optimization problem, the physics model, and the discretization method.

This talk will provide an overview of topology optimization approaches for problems, reviewing both density and level set topology optimization methods. This overview will show that level set methods combined with immersed finite element approaches provide a promising framework, especially for coupled multi-physics and multi-material topology optimization problems. The accuracy, robustness, and accuracy of the finite element analysis play a crucial role for such problems. This talk will present an isogeometric formulation of the eXtended Finite Element Method where the level set and state variables fields are discretized on adaptively refined meshes, using truncated hierarchical B-splines. Using approximate Lagrange extraction, this formulation can be integrated in standard finite element solvers.

The characteristics of this XFEM analysis and level set topology optimization framework will be illustrated with 2D and 3D problems in solid and fluid mechanics, including elastic, flow, and conjugate heat transfer problems.

Biographical Sketch: Dr. Maute is the Palmer Endowed Chair and a professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. Dr. Maute received a Bs/Ms. in Aerospace Engineering in 1992 and Ph.D. in Civil Engineering in 1998, both from the University of Stuttgart, Germany. After working as a postdoctoral research associate at the Center for Aerospace Structures, he started his faculty position at CUB in 2000. His research is concerned with computational mechanics and design optimization methods. He focuses on fundamental problems in solid and fluid mechanics and heat transfer with applications to aerospace, civil, mechanical engineering problems. For the past 30 years, Dr. Maute has worked on topology and shape optimization methods for a broad range of problems focusing on coupled multi-physics and multi-scale problems, such as fluid-structure interaction and chemo-mechanically coupling. Dr. Maute has published his work in over 200 journal articles, book chapters, and conference proceedings.

Abstract:

The emerging economic and societal needs such as advanced manufacturing, environmental treatment, and space exploration call for machines that can operate in harsh and complex environments. An attractive approach is to utilize a new paradigm of physical intelligence in material development: a rational material design will enable its on-board actuation, sensing, and analysis, without a need for central computing or complex control. This talk will present our recent progress in the fundamental research of embedding physical intelligence in soft active materials. A stretchable polymer network responds to an external stimulus such as light or heat, dramatically changes its shape or material property, and enables special functionality in its bulk or surface. The first part of the talk presents photoactive liquid crystal elastomers that can change their shape and generate work output under light illumination or temperature change. Emphasis is placed on the fundamental photo-thermo-mechanical coupling across many length scales, especially at the mesoscale where the polymer network and liquid crystal mesogens behave collectively, leading to multiple interesting phenomena and their consequences in the macroscopic actuation. The second part of the talk presents temperature-switchable adhesives with high adhesion strength, large switching ratio, fast switching speed, and good reversibility. A polymer network containing many free-end dangling chains is a strong adhesive at ambient environment due to the long chains, dense physical bonds, and large dissipation from the polymer matrix, and is completely non-adhering at an elevated temperature due to its thermo-responsive phase transition. This talk is hoped to help advance the fundamental knowledge of soft active materials, bring together communities of relevant research fields, and expand the potential large-scale applications.

Bio:

Ruobing Bai is an assistant professor in the Department of Mechanical and Industrial Engineering at Northeastern University. He received his BS in Theoretical and Applied Mechanics at Peking University in 2012, and PhD in Engineering Sciences at Harvard University in 2018. He was a postdoctoral fellow in the Department of Mechanical and Civil Engineering at California Institute of Technology from 2018 to 2020. He is the recipient of the Chun-Tsung Scholar in Peking University, the Haythornthwaite Research Initiation Award from the Applied Mechanics Division of American Society of Mechanical Engineers (ASME), and the Extreme Mechanics Letters (EML) Young Investigator Award. Research in the Bai group aims to combine theory and experiment in areas including solid mechanics, soft active materials, fracture and toughening of materials, adhesion, and sustainable materials, for applications such as soft robotics, advanced manufacturing, human-machine interfaces, and human health.

Abstract: The properties of materials are highly dependent on their structures, which include morphologies, grain boundaries, phases, atomic structures, Etc. In particular, the atomic structures determine the limit of their properties, which are the unique characteristics of each material. However, predicting physical properties and understanding their origins demand accurate electronic structure and atomic structure calculations, which require a lot of computational resources. Over the past few decades, computational power has been tremendously improved, enabling atomistic simulations on a reasonable length and time scale to answer such questions. Accordingly, high-throughput calculations, data mining, and machine-learning(ML) solutions have been becoming mainstream in materials research.

In this talk, we introduce our effort to understand the atomic structure and materials property relationship to practical application in the next-generation battery and semiconductor materials using atomistic simulation tools such as; ab initio Density Functional Theory, Classical Molecular Dynamics, and ML algorithms. First, we present newly developed active materials for next-generation rechargeable battery applications, which include novel cathode and electrolyte materials. And then, we exhibit the structure-property relationship to describe dielectric properties using the ML algorithm and intuition of fundamental physics. These examples will sufficiently show atomistic simulation’s practical application to materials research.

Biographical Sketch: Dr. Shin is a Sr. Staff Engineer and Project Leader in the Advanced Materials Lab at Samsung Semiconductor Inc (SSI). He studies theoretical and computational materials science through computational modeling, simulation, and Artificial-intelligence driven materials discoveries for energy harvesting, conversion, and storage materials.

Dr. Shin received his Ph.D. in Materials Science and Engineering at Boston University in 2012 and held a Chemistry Postdoc Fellow position in the Energy Storage and Distributed Resources Division at Lawrence Berkeley National Laboratory. He worked as Research Engineer at Samsung Research America before joining SSI.