WEDNESDAY, JUNE 1, 8:30 - 9:30 AM
Yonggang Huang, Ph.D.
Jan and Marcia Achenbach Professor of Mechanical Engineering
Civil and Environmental Engineering and Materials Science and Engineering
Mechanics-guided 3D Assembly of Complex Mesostructures and Functional Devices
ABSTRACT: A rapidly expanding research area involves the development of routes to complex 3D structures with feature sizes in the mesoscopic range (that is, between tens of nanometres and hundreds of micrometres). A goal is to establish methods to controll the properties of materials systems and the function of devices constructed with them, not only through chemistry and morphology, but also through 3D architectures. Hwever, existing approaches of 3D assembly/fabrication are only compatible with a narrow class of materials and/or 3D geometries. In this talk, I will introduce a mechanics-guided assembly approach that exploits controlled buckling for constructing complex 3D micro/nanostructures from patterned 2D micro/nanoscale precursors that can be easily formed using established semiconductor technologies. This approach applies to a very broad set of materials (e.g., semiconductors, polymers, metals, and ceramics) and even their heterogeneous integration, over a wide range of length scales (e.g., from 100 nm to 10 cm). To enrich the class of 3D geometries accessible to the proposed assembly approach, we devised a set of mechanics-driven design strategies, such as kirigami/origami designs of 2D precursors, heterogeneous substrate designs and loading-path controlled shape morphing strategies. I will also introduce a series of mechanics models for the direct postbuckling analysis, as well as inverse design methods that map target 3D topologies onto unknown 2D precursor patterns, which could provide an important theoretical foundation of the rational 3D assembly. The compatibility of the approach with the state-of-the-art fabrication/processing techniques, along with the versatile capabilities, allow transformation of diverse existing 2D microsystems into 3D configurations, providing unusual design options in the development of novel functional devices. I will demonstrate a few examples in this presentation, including biomedical devices conformally integrated with organoids/tissues/organs, 3D MEMS capable of efficient energy harvesting of low-frequency vibration, bioinspired electronic systems, and 3D microfluidic devices.
SHORT BIO: Yonggang Huang is the Achenbach Professor of Mechanical Engineering, Civil and Environmental Engineering, and Materials Science and Engineering at Northwestern University. He is interested in mechanics of materials and structures and has published more than 600 journal papers, including 10 in Science and 6 in Nature, and is the editor-in-chief of the Journal of Applied Mechanics. He is a Highly Cited Researcher in Engineering (2009), in Materials Science (since 2014), and in Physics (2018) and has won multiple awards including the Guggenheim Fellowship (2008) from the John Simon Guggenheim Memorial Foundation; the Drucker Medal (2013), Nadai Medal (2016), and Honorary Member (2021) from the American Society of Mechanical Engineers; the Prager Medal (2017) from the Society of Engineering Sciences; the Bazant Medal (2018) and von Karman Medal (2019) from the American Society of Civil Engineers. He is a member of US National Academy of Engineering, US National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.
WEDNESDAY, JUNE 1, 1:00 - 2:00 PM
Shirley J. Dyke, Ph.D., A.M.ASCE
Professor of Civil Engineering and Mechanical Engineering
Empowering Engineers by Leveraging Automation in Reconnaissance
ABSTRACT: After each natural hazard event reconnaissance teams collect perishable data to evaluate building performance. These data, often taking the form of photographs, guide engineers to important questions that need to be addressed regarding design practices, and improving building codes. Machine learning is emerging as a powerful means to automatically extract information from and organize these data. However, only by learning to couple these algorithms with domain knowledge can we empower the engineer to exploit machine learning and train the computer to aid those human engineers. Furthermore, teaching a computer to extract unbiased and task-driven information from such images requires large volumes of data. Using a database of images from past field reconnaissance missions around the world, we have developed the ability to automate several of the key steps in this process. This presentation will discuss the collection of methods developed and share important lessons on how to apply machine learning to real world problems meant to aid the work of the engineer in performing these tasks. This work comes together in the development of the Automated Reconnaissance Image Organizer (ARIO) which is meant to exploit the collection of capabilities to streamline the work during field missions and accelerate research meant to improve the safety and performance of our infrastructure.
SHORT BIO: Professor Shirley J. Dyke holds a joint appointment in Mechanical Engineering and Civil Engineering at Purdue University. She is the Director of the NASA funded Resilient Extraterrestrial Habitat Institute (RETHi) and the Director of Purdue's Intelligent Infrastructure Systems Lab at Bowen Lab. Dyke is the Editor-in-Chief of the journal Engineering Structures. Her research focuses on the development and implementation of “intelligent” structures, and her innovations encompass structural control technologies, structural health monitoring, real-time hybrid simulation, and machine learning and computer vision for structural damage assessment. She was awarded the Presidential Early Career Award for Scientists and Engineers from NSF (1998), the International Association on Structural Safety and Reliability Junior Research Award (2001) and the ANCRiSST Young Investigator Award (2006). She has also led many educational programs, including Research Experiences for Undergraduates, GK12, and the University Consortium on Instructional Shake Tables. She holds a B.S. in Aeronautical and Astronautical Engineering from the University of Illinois, Champaign-Urbana in 1991 and a Ph.D. in Civil Engineering from the University of Notre Dame in 1996. She served as the Co-leader for Information Technology for the NSF-funded Network for Earthquake Engineering Simulation (NEES) building a community-driven Cyberinfastructure Platform for the earthquake engineering community.
THURSDAY, JUNE 2, 8:30 - 9:30 AM
Charbel Farhat, Ph.D.
Vivian Church Hoff Professor of Aircraft Structures, School of Engineering
Chair, Aeronautics and Astronautics, Stanford University
Director, Stanford-King Abdulaziz City of Science and Technology Center of
Excellence for Aeronautics and Astronautics
Computational Mechanics for Landing on Mars
ABSTRACT: Current technology for decelerating a spacecraft from the high speed of atmospheric entry to the final stages of landing on Mars is based on low-density supersonic decelerators, such as low mass and high packaging efficiency parachute systems. To enable future exploration missions featuring sophisticated robots and safely land heavier spacecraft on Mars, larger than before high-speed parachutes and inflatable drag devices are needed. The design, development, and maturing of such devices for future use at Mars require guidance from predictive simulations based on a high-fidelity, multi-disciplinary computational model for parachute inflation dynamics (PID) and drag prediction. The development of such a model is a formidable challenge. It must account for shocks, turbulence, and porous flow boundary conditions; the complexity of the behaviour of fabric material; massive contact between very thin surfaces; and highly nonlinear fluid-structure interactions in the presence of topological changes. The computational model must also be able to predict various instabilities of a parachute such as flutter and pulsation, the influence on its performance of several design factors including material and geometric porosities, and material failure. This lecture will discuss an ongoing effort at Stanford University, in collaboration with NASA Ames and the Jet Propulsion Laboratory, for the development of such a computational model, and cover some of the associated computational innovations. These include: a multi-scale approach for modeling the dynamics of woven fabrics based on the concept of a locally attached microstructure and mechanics-informed machine learning; a discrete-event-free, spurious-oscillation-free, and total variation diminishing embedded boundary method for multi-material problems; a subgrid scale modeling approach for the treatment of porous wall boundary conditions; and an energy-conserving approach for the discretization of transmission conditions at the interface between fluid and structural representations of disparate spatial dimensions. The lecture will also report on validation results for the simulation of the supersonic inflation dynamics of: the parachute that landed Curiosity on Mars, on August 6, 2012; and those used in NASA’s 2018 ASPIRE tests to help decide which parachute design to use on the recent Mars 2020 mission that landed Perseverance on Mars, on February 18, 2021.
SHORT BIO: Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures, Chairman of the Department of Aeronautics and Astronautics, and Director of the Stanford-KACST Center of Excellence for Aeronautics and Astronautics at Stanford University. His research interests are in computational engineering sciences for the design and analysis of complex systems in aerospace, mechanical, and naval engineering. He is a Member of the National Academy of Engineering, a Member of the Royal Academy of Engineering (UK), a Doctor Honoris Causa from Ecole Centrale de Nantes, a Doctor Honoris Causa from Ecole Normale Supérieure Paris-Saclay, a designated ISI Highly Cited Author, and a Fellow of AIAA, ASME, IACM, SIAM, USACM, and WIF. He has trained more than 90 PhD and post-doctoral students. For his research on aeroelasticity, aeroacoustic scattering, CFD, dynamic data-driven systems, fluid-structure interaction, high performance computing, and model reduction, he has received many professional and academic distinctions including: the Ashley Award for Aeroelasticity from AIAA; the Spirit of St Louis Medal from ASME; the Gordon Bell Prize from IEEE; the Gauss-Newton Medal from IACM; the Grand Prize from the Japan Society for Computational Engineering Science; and the John von Neumann Medal from USACM. He was appointed on the Scientific Advisory Board of the US Air Force, selected by the US Navy recruiters as a Primary Key-Influencer, and flown by the Blue Angels.
THURSDAY, JUNE 2, 1:00 - 2:00 PM
George Deodatis, Ph.D., F.EMI, M.ASCE
Santiago and Robertina Calatrava Family Professor
Department of Civil Engineering and Engineering Mechanics
Reflections and Recent Developments on the Use of Monte Carlo Simulation in Stochastic Mechanics
ABSTRACT: Monte Carlo Simulation (MCS) techniques involving stochastic processes, fields and waves were introduced in the field of Engineering Mechanics in the 1970s to deal with dynamic input as well as system uncertainties. Subsequently, MCS techniques enabled the accurate solution of complex stochastic problems whose solutions were unavailable through analytical methods. A critical part of these MCS techniques in the field of Stochastic Mechanics is the simulation of stochastic processes and fields involved in the problem under consideration. Usually, stochastic processes (and waves) are used to model stochastic system excitations, and stochastic fields to model stochastic system properties. These processes, waves and fields can be scalar or vector, one-dimensional or multi-dimensional, Gaussian or non-Gaussian, stationary or non-stationary, homogeneous or non-homogeneous, or any combination of the above. The lecture will focus on the Spectral Representation Method developed specifically for simulation of stochastic processes, fields and waves for use in MCS schemes. The presentation will start with a historical review of the method, and will proceed to several recent developments. Emphasis will be given to applications in various areas of engineering mechanics, especially to problems where the input/system uncertainty has a pronounced effect, not only on the magnitude of the response uncertainty, but also on the nature of the response itself. As MCS techniques involving stochastic processes, fields and waves are still beyond the realm of common professional practice, a recently developed approach will be presented facilitating its adoption by practicing engineers.
SHORT BIO: Professor George Deodatis received his Diploma in Civil Engineering from the National Technical University of Athens in Greece in 1982. He holds M.S. and Ph.D. degrees in Civil Engineering from Columbia University. He started his academic career at Princeton University where he served as Assistant Professor and Associate Professor (with tenure). He moved to Columbia University in 2002 where he served as Associate Professor and Professor. He currently holds the Santiago and Robertina Calatrava Family Endowed Chair at the Department of Civil Engineering and Engineering Mechanics at Columbia University. He served as Department Chair from 2013 to 2019 (two terms). His research interests are in the area of probabilistic methods in civil engineering and engineering mechanics, with emphasis on simulation of stochastic processes and fields to model uncertain earthquake/wind loads and material/soil properties, reliability and safety analysis of structures, and risk analysis and risk management of the civil infrastructure subjected to natural and technological hazards (including earthquakes, floods and climate change). He has received the National Science Foundation Young Investigator Award, the International Association for Structural Safety and Reliability Junior Research Prize, and the American Society of Civil Engineers Walter Huber Research Prize. He is a Fellow of the Engineering Mechanics Institute of the American Society of Civil Engineers. In 2009, he was elected President of the International Association for Structural Safety and Reliability for a four-year term. In 2017, he was elected President of the Engineering Mechanics Institute of the American Society of Civil Engineers for a two-year term. While on the faculty at Princeton University, he was awarded the President's Award for Distinguished Teaching, Princeton's highest teaching honor. At Columbia University, he has received the Presidential Award for Outstanding Teaching and the Great Teacher Award from the Society of Columbia Graduates, Columbia's highest teaching honors.
FRIDAY, JUNE 3, 8:30 - 9:30 AM
Charles Meneveau, Ph.D.
L.M. Sardella Professor of Mechanical Engineering
Johns Hopkins University
Democratizing Really Big Data in Turbulence and Sample Application
ABSTRACT: Computational mechanics, and computational turbulence in particular, is generating massive amounts of simulation data. These data are notoriously difficult to share with the community, limiting their usefulness mainly to the scientists who generate them. In this presentation, we describe an ongoing effort at democratizing access to massive datasets from direct numerical simulations (DNS) of various turbulent flows. The Johns Hopkins Turbulence Databases (JHTDB) exposes DNS data to the research community while at the same time providing easy-to-use client interfaces based on Web Services that act as “virtual flow sensors” that can be placed in the turbulent flows. This approach has greatly facilitated retrieving and interacting with the data. At present JHTDB contains over 1/2 Petabyte of data from various turbulent flow simulations. The data have been used in over 250 peer-reviewed journal publications on turbulence from authors world-wide. We also present an application of the transitional (by-pass) boundary layer dataset contained in JHTDB for a study of scaling properties of the interface separating turbulent spots from laminar, outer flow. Parts of the work presented has been performed with Drs. Zhao Wu and Tamer Zaki, while the database (supported by the NSF) has resulted from a long-term collaboration with the JHTDB team.
SHORT BIO: Charles Meneveau is the Louis M. Sardella Professor in the Department of Mechanical Engineering, is Associate Director of the Institute for Data Intensive Engineering and Science (IDIES) and is jointly appointed as Professor in the Department of Physics and Astronomy at Johns Hopkins. He has been on the Johns Hopkins faculty since 1990. His area of research is focused on understanding and modeling hydrodynamic turbulence, and complexity in fluid mechanics in general. The insights that have emerged from Professor Meneveau’s work have led to new numerical models for Large Eddy Simulations (LES) and applications in engineering and environmental flows, including wind farms. He also focuses on developing methods to share the very large data sets that arise in computational fluid dynamics. He is Deputy Editor of the Journal of Fluid Mechanics and has served as the Editor-in-Chief of the Journal of Turbulence. Professor Meneveau is a member of the US National Academy of Engineering, a foreign corresponding member of the Chilean Academy of Sciences, a Fellow of APS, ASME, AMS and recipient of the Stanley Corrsin Award from the APS, the AIAA Fluid Dynamics Award, the JHU Alumni Association's Excellence in Teaching Award, and the APS' François N. Frenkiel Award for Fluid Mechanics.
FRIDAY, JUNE 3, 9:30 - 10:30 AM
Kurt Maute, Ph.D.
Aerospace Mechanics Research Center (AMReC)
Ann and H.J. Smead Department of Aerospace Engineering Sciences
University of Colorado Boulder
On the Optimal Design of Problems in Solid and Fluid Mechanics Dominated by Interface Phenomena
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 dominated by interphase phenomena, 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 for coupled multi-physics and multi-material topology optimization problems. A modular realization of such a framework will be presented where the geometry of the body is described by one or multiple level set functions, and the physical response is predicted by an immersed finite element method, namely the eXtended Finite Element Method (XFEM). To improve the accuracy and efficiency of this approach, the level set and state variables fields are discretized on adaptively refined meshes, using truncated hierarchical B-splines.
The characteristics of this level set topology optimization framework will be illustrated with 2D and 3D problems in solid and fluid mechanics, including flow and conjugate heat transfer problems with a RANS fluid model.
SHORT BIO: Dr. Maute is a professor of the Department of Aerospace Engineering Sciences and the Palmer Endowed Chair at the University of Colorado Boulder. He is the Past Director of the Aerospace Mechanics Research Center and former Associate Dean for Research of the College of Engineering and Applied Science. Dr. Maute received his Ph.D. degree in Civil Engineering in 1998, and his Bs/Ms. in Aerospace Engineering in 1992, 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 the University of Colorado Boulder 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 25 years, Dr. Maute worked on topology and shape optimization methods for a broad range of problems, including multi-physics and multi-scale problems. Dr. Maute is a Fellow of the US Association for Computational Mechanics, delivered the 2016 Koiter Lecture in Engineering Mechanics, and has published his work in over 200 journal articles, book chapters, and conference proceedings.