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Understanding the mechanical behaviors of soft materials, especially those involving fracture and adhesion, is critical for evaluating the mechanical reliability of emerging applications such as soft robotics and biomedical implants. However, experimental characterization of the large deformation field in soft materials has been challenging. Although existing methods such as digital image correlation have been used to measure strain fields in soft materials, it remains a challenge to measure the nonlinear deformation field under large deformation. I will present an approach to map the large deformation field by optically tracking the displacements of randomly distributed tracer particles. The tracer particles serve to provide displacement data at discrete spatial locations without affecting the mechanical property of the underlying soft material sample. The discrete displacement data are further converted to continuous displacement and strain fields through an interpolation scheme. Using a soft silicone elastomer as a model system, we demonstrate that the particle tracking method is capable of resolving the highly concentrated nonlinear deformation field at the crack tip. We also apply the particle tracking method to characterize mixed鈥恗ode crack propagation in an elastic elastomer and rate鈥恉ependent crack propagation in a viscoelastic gel. Finally, we extend the particle tracking method to three鈥恉imension (3D) for mapping the 3D strain field within the volume of a soft gel substrate indented and sheared by a micro鈥恜illar.

baby直播app Rong Long

Rong Long is currently an Associate Professor and Lyall Faculty Fellow in the Department of Mechanical Engineering at CU Boulder. Prior to joining CU in 2014, he was an Assistant Professor at University of Alberta in 2013鈥�2014, a Research Associate at University of baby直播app in 2012, and a Postdoctoral Associate at Cornell University in 2011. He received his Ph.D. degree in Theoretical and Applied Mechanics from Cornell University in 2011 and his B.S. degree in the same field from University of Science and Technology of China in 2006. He received a few awards including the Young Adhesion Scientist Award (2014), the 3M non鈥怲enured Faculty Award (2017), the NSF CAREER Award (2018) and the Provost鈥檚 Faculty Achievement Award (2020). His research interests include: continuum mechanics of soft materials, fracture mechanics, contact mechanics, adhesion and biomechanics.

This presentation discusses finite element and physics鈥恇ased data鈥恉riven modeling of progressive damage in fiberreinforced composites microstructure. We focus on two鈥� and three鈥恉imensional representations of additively manufactured composites. Such simulations are critical in order to design optimized composites for additive manufacturing. In fiber鈥恟einforced composites, cracks initiate around the fibers aligned transversely to the loading direction. The transverse cracks can potentially cause leakage in specific applications or progress to inter鈥恜ly delamination and catastrophic failure. We, first, integrate an efficient numerical framework with robust and accurate constitutive equations to study transverse behavior and multiple cracking of two鈥恉imensional representations of fiber鈥恟einforced composite laminates. We then extend our simulations to three鈥恉imensional representations of additively manufactured discontinuous fiber鈥恟einforced composites. The representative volume element is generated randomly, based on a target distribution for the fibers' aspect ratios reported by experiments. The developed frameworks are validated versus experimental results. The effects of several material properties on the transverse crack density, delamination, intra鈥恜ly stress redistribution, and damage propagation are then investigated. Since finite element simulations of such composites are complex and computational cost is expensive, we develop a deep learning framework to predict the post鈥恌ailure full鈥恌ield stress distribution and the crack pattern in two鈥恉imensional representations of the composites based on their microstructures. The deep learning framework contains two stacked fully鈥恈onvolutional networks. A physics鈥恑nformed loss function is also designed and incorporated to improve the proposed framework's performance further and facilitate the validation process. The accuracy of the deep learning framework to predict the complex nonlinear behavior is obtained to be more than 90%.

baby直播app Maryam Shakiba

Maryam Shakiba is an assistant professor at the Civil and Environmental Engineering Department at Virginia Tech. Before joining Virginia Tech, she was a Postdoctoral Research Associate at the University of Illinois at Urbana鈥怌hampaign. She received her Ph.D. from Texas A&M University and her B.S. and M.S. degrees from Tehran Polytechnic. Shakiba鈥檚 group develops physics, chemistry, and mechanics鈥恇ased constitutive equations to simulate multiphysics conditions for different advanced materials. The group also devises high鈥恌idelity as well as mechanistic machine learning approaches to solve engineering problems. Our goals are to (1) develop theoretical frameworks to understand advanced material responses under extreme multi鈥恌actor conditions and (2) integrate the theoretical framework with machine learning approaches as physics鈥恇ased machine learning is key to creating true digital twins. Shakiba is the recipient of a NSF CAREER Award and of a AFOSR young investigator program (YIP) award to investigate additively manufactured composites for high鈥恡emperature application.

Granular and other heterogeneous materials exhibit complex behaviors which are difficult to capture using classical continuum theories. Enhancements through higher order descriptions of the deformation such as micropolar or, most generally, micromorphic continuum have been proposed but suffer from difficulty in calibrating the numerous parameters. We here propose and demonstrate a variationally based method for computing, or 鈥渇iltering,鈥� the deformation and stress response of a Direct Numeric Simulation (DNS) to the micromorphic macro鈥恠cale utilizing only the continuum equations of Eringen and Suhubi. Once determined for several DNS, we calibrate micromorphic finite deformation elastoplastic constitutive equations within the context of a surrogate鈥恇ased Bayesian uncertainty quantification framework and comment on upcoming extensions.听

baby直播app Nathan Miller

Nathan Miller, a research and development engineer at听Los Alamos National Laboratory, has been a research and development engineer in Los Alamos National Laboratory's advanced engineering analysis group W鈥�13 since his graduation with a bachelor's and masters of science in mechanical engineering from baby直播app State University in 2009 and 2010 respectively. He recently received his Ph.D. in Civil Engineering from CU Boulder with an emphasis on the application of micromorphic theory to elasto鈥恜lastic materials undergoing finite deformation and is the lead developer of Tardigrade. His research focuses on the development of constitutive models for polymer bonded granular materials both at the phenomenological macroscale and the mechanistic micro鈥恠cale and techniques for bridging those domains. Additional focus is placed on the development of uncertainty quantification frameworks for nonlinear problems using direct and surrogate based techniques.

Controlled spalling is a method to produce thin, continuous single鈥恈rystal films at semiconductor wafer scale. A stressed material with excellent adhesion to the wafer transmits forces sufficient to propagate a near鈥恠urface fracture in the crystal, resulting in the removal of a microscale鈥恡hickness, single鈥恈rystal film and leaving the remainder of the wafer intact. This talk illustrates the impact of nickel stressor film and laminate processing conditions on spall depth and fracture surface morphology, using germanium and gallium arsenide wafers as example high鈥恦alue semiconductors. Fracture surfaces exhibit various features across nanometer鈥� to centimeterlengthscales; their morphology is characterized, mechanical origins are explained, and defects are cross鈥恈orrelated to local optoelectrical performance in testbed photovoltaic cells.

baby直播app Corinne Packard

Dr. Packard is an Associate Professor in the George S. Ansell Metallurgical and Materials Engineering Department at the baby直播app School of Mines and holds a joint appointment at the National Renewable Energy Laboratory in the National Center for Photovoltaics. Prior to appointment at Mines, Packard earned her Ph.D. in Materials Science & Engineering from MIT. Packard鈥檚 research focuses on elucidating the principles and mechanisms of deformation behavior in ceramics at the micro鈥� and nano鈥恠cales. Specific examples include determining the role of chemistry in controlling the deformation behavior in rare鈥恊arth orthophosphate ceramics; engineering fracture in photovoltaic semiconductors to enable dramatic cost reduction through wafer reuse; and high鈥恡hroughput materials discovery and optimization to design for durability in transparent conducting oxides for photovoltaics & flexible electronics, and metallic glass鈥恇ased wear coatings. The thread that ties these diverse projects together is a deep interest in understanding how complex stress state can be controlled to yield desirable mechanical behavior in materials. In 2014, she received a National Science Foundation Faculty Early Career Development (CAREER) Award and was selected as a TMS Young Leader. In 2017, she received the AIME Robert Lansing Hardy Award. In 2019, she received the baby直播app School of Mines Faculty Excellence Award. To date, she has more than 60 archival publications, 4 issued patents, and has given over 40 invited and contributed talks.

Research activities in smart materials and adaptive structures are of a great interest for many years. New technologies are now available for implementing smart cells inside architected material allowing us to design and construct a new class of smart materials that we can call 鈥減rogrammable metacomposites鈥�. These new generalized composites allow integration of dense and distributed set of smart materials, electronics, chip sets and power supply system inside material, for implementing distributed control strategies. This paves the way to imagine new strategies to control vibroacoustic flow in a large frequency band and implement unconventional behavior such as non鈥恟eciprocal wave propagation. After a state of art of this new domain of research, the wave diffusion control by means of piezoelectric metacomposites is firstly presented and analysed. Concepts of energy lensing and mechanical diode are also presented. On a second step one deeply presents acoustical wave reciprocity breaking by means of electroacoustic metaliner. Implementation and industrial applications of an acoustical diode prototype allows to show all the potential of such new technologies.

baby直播app Manuel Collet

Manuel Collet is senior researcher with the French CNRS. He is associated with the Tribology and System Dynamics Laboratory (LTDS) of Ecole Centrale de Lyon. Graduating in 1992 with a degree in Mechanical Engineering from Ecole Centrale de Lyon, he obtained his PhD in acoustics from the same university in 1996. His dissertation concerned the 鈥淎ctive control of vibrating structures by mean of semi distributed piezoelectric patches." His current research interests focus on smart structures and active control, adaptive programmable materials and metamaterials. He has published more than 89 journal papers, 54 invited conferences, 6 patents with at least 2600 citations. From 1998 to 2013, Manuel Collet was member of the Department of Applied Mechanics of FEMTO鈥怱T Institute. He was its president of the Scientific Council from 2005 to 2010 and Assistant Director in charge of the scientific strategy and organization from 2008 to 2013. After having spent one year at the Georgia Institute of Technology as a visiting professor in 2006鈥�2007, Manuel Collet created and chaired a new research program concerned with Hybrid Vibroacoustic Interface Control by Metacomposite Optimization. He has been working with the LTDS in Ecole Centrale de Lyon since 2013 and is developing research programs for integrating new class of adaptive and programmable metacomposites and developing the associated technologies. He is head of the 鈥楧ynamics of Complex Structures鈥� research team comprising 19 researchers and over 35 PhD students. He is also president of Carnot Institute Engineering at Lyon, member of Board of Directors of Ecole Centrale de Lyon and University of Lyon. As member of ASME Adaptive Structures & Material Systems Branch Committee, he chairs the ASMS scientific committee in 2018. He is also expert in different panels as ERC (UE), ANR (FR) ; AERES(FR), SNSF (CH), NSERC (CA), NSF (US).

Guided Ultrasonic Waves (GUW) are a well鈥恊stablished tool in the fields of Structural Health Monitoring (SHM) and Non鈥怐estructive Testing (NDT), where they are mainly used for the mechanical characterization and non鈥恑nvasive inspection of structural components. The advantage of GUW with respect to other ultrasound鈥恇ased techniques lies in their inherent dispersive nature, multimodal properties, and capability to carry energy for long distances, which can be fully exploited in the study and understanding of the dynamic behavior of waveguide鈥恖ike structures of different nature. This seminar will focus on two different types of such structures, namely the human skull and two鈥恉imensional curved waveguides. The use of GUW as complementary tool to focused ultrasound in the study of the skull鈥恇rain system is first analyzed with reference to three different applications: (i) the retrieval of the orthotropic mechanical properties of the cranial bone, (ii) the evaluation of the equivalent stiffness of cranial sutures and (iii) the enhancement of transcranial ultrasound delivery via mode conversion. When applied in conjunction with principles and methods of differential geometry, GUW can also be used to recreate exotic waveguiding effects and to mimic the dynamic behavior of complex materials via induced spatial curvature. Specifically, it is demonstrated that, in the short wavelength limit, GUW propagate on curved surfaces along trajectories defined by geodesics. In addition, non鈥怑uclidean transformations are investigated that allow to map a generic planar refractive index distribution into a geometrically equivalent geodesic waveguide with unit refractive index. These principles are demonstrated for lenses with known waveguiding properties, such as Luneburg and Eaton lenses, and for more complex curved profiles through numerical simulations and experimental tests.

baby直播app听Matteo Mazzotti

Matteo Mazzotti is a Research Associate at the Department of Mechanical Engineering of CU Boulder, where he performs research on the dynamics of the human skull, wave propagation, metamaterials, fiber鈥恟einforced composites, ultrasound鈥恇ased nondestructive evaluation methods, optimization problems and biosensors. Before joining CU Boulder, Dr. Mazzotti held a position as Postdoctoral Researcher at the Civil, Architectural and Environmental Engineering (CAEE) Department of Drexel University, where he worked on numerical methods for guided wave鈥恇ased applications and participated on several research projects related to structural health monitoring of civil structures and infrastructures.

During early stages of injury, degeneration, and disease, soft tissues often exhibit disrupted or abnormal tissue microstructure, or extracellular matrix. Currently, direct detection of these changes requires invasive optical imaging methods not suitable for routine screenings. Alternatively, multi鈥恠cale modeling of mechanical behavior could leverage structure鈥恌unction relationships to 鈥渟ee鈥� tissue microstructure, with lower resolution, non鈥恑nvasive imaging modalities (e.g., MRI, ultrasound). However, accurate material modeling of soft tissues has been challenging, as strain heterogeneity invalidates assumptions required for standard modeling methods. In contrast, full鈥恌ield (i.e., image鈥恇ased) methods for material characterization make strain heterogeneity an asset; the multiple modes and states of strain present in a single heterogeneous deformation are collectively used to better inform models of material behavior. In this talk, I will present evidence that specific microstructural features can be discerned using mesoscale measures of mechanical function alone. Specifically, I will describe my work using full鈥恦olume deformation fields and inverse methods to construct constitutive models for ligament tissues. I will also discuss ongoing work to develop damage models for soft tissues and the application of my methods to study how extracellular matrix composition and organization affect injury risk. Finally, I will present my roadmap for integration of these ideas to develop mechanics鈥恇ased technology for non鈥恑nvasive detection of tissue microdamage and microstructural abnormalities.

baby直播app Callan Luetkemeyer

Callan Luetkemeyer is a Schmidt Science Fellow in the Department of Mechanical Engineering at the University of baby直播app Boulder, where she collaborates with Sarah Calve, Corey Neu, and Virginia Ferguson. She earned a bachelor鈥檚 degree in Biomedical Engineering in 2014 from St. Louis University and a Master鈥檚 degree and Ph.D. in Mechanical Engineering in 2020 from the University of Michigan, where she was advised by Ellen Arruda. For her work on image鈥恇ased material modeling of soft tissues, Callan received the McIvor Award for Excellence in Applied Mechanics Research from the University of Michigan College of Engineering, the Savio Woo Young Researcher Award from the International Symposium on Ligaments and Tendons, and the Schmidt Science Fellowship from Schmidt Futures, which has allowed her to pursue her own research vision as a postdoctoral fellow.

An understanding of polymer behavior is critical for their use alone or as part of a composite leveraged in military, aerospace, automotive and medical applications. At the same time, the mechanical properties of polymers are strongly dependent on strain rate, temperature, and pressure, as well as highly governed by their composition and microstructure. As such, this talk will first focus on open cell polyurethane foams used as protective helmet liners in defense. Given both their low impedance and strong rate鈥恉ependent behavior, novel dynamic experimental techniques and metrologies are leveraged to aid in the development of physics鈥恇ased constitutive models for cellular solids. The quantitative microstructural characterization of these foams, its relation to the bulk response, and the compressive behavior across six orders of magnitude in strain rate utilizing a non鈥恜arametric formulation of the Virtual Fields Method to obtain dynamic material behavior will be presented. The last part of the talk will introduce the extraction of multiple viscoelastic constitutive parameters of polymers using the Image Based Inertial Impact Test (IBII). Using polymethylmethacrylate (PMMA) as a model material, both numerical simulations with a generalized Maxwell model and experimental validations will be presented to demonstrate the successful determination of viscoelastic parameters across multiple time constants in a single experiment.

baby直播app Leslie Lamberson

Leslie Lamberson is an Associate Professor in Mechanical Engineering with affiliation in Materials Science at the baby直播app School of Mines.听Her area of expertise is in mechanics of materials under extreme conditions. She earned her B.S. in Aerospace Engineering and B.A. in Dance Performance from the University of Michigan, her M.S. in Aerospace Engineering from the Georgia Institute of Technology, and her Ph.D. in Aeronautics from the California Institute of Technology. Prior to her baby直播app position, Leslie was a postdoctoral researcher with K.T. Ramesh at the Johns Hopkins University. A former Lockheed Martin 鈥淪kunk Works鈥� engineer, Leslie was a 2013 NASA Glenn Faculty Fellow in the Materials and Structures under Extreme Conditions Division. She is a recipient of an ONR Young Investigator Award, an NSF CAREER award, and is currently an Associate Editor for the journal Strain.

When exposed to water, colonies of fire ants (Solenopsis invicta) are well鈥恉ocumented to form buoyant rafts that helps them escape from floods. In this presentation, I will discuss the collective morphogenesis of these rafts when docked to stationary, vertical rods. These living structures consist of a condensed, floating, structural network of interconnected ants on top of which a population of dispersed ants can actively walk. Under these conditions, ant rafts can change their shape substantially and continuously over the span of several hours through a mechanism we call treadmilling. During this process, rafts frequently sprout tether鈥恖ike protrusions from their edges that fire ants can use to dynamically explore their environment and create land bridges as an escape route. Employing both experimental characterization and an agent鈥恇ased numerical model, we here unveil a local set of mechanisms that reproduce the stochastic emergence of these instabilities in the absence of long鈥恟ange interactions, targeted cues, or external gradients. Furthermore, we demonstrate that by modulating their activity level,听ant collectives can exhibit oscillatory phases of outwards expansion (exploration) and inwards contraction (dormancy). These results suggest that collective morphogenesis in fire ant aggregations is strongly mediated by local interactions at the constituent length scale. We draw analogies with cell mechanics and discuss potential applications to the development of decentralized, autonomous active matter and swarm robotics. This work was performed with graduate student Rob Wagner.

baby直播app Franck Vernerey

Franck Vernerey is a professor in the Department of Mechanical Engineering at the University of baby直播app, Boulder. He received his Ph.D. from Northwestern University in 2006 in the field of theoretical and applied mechanics. His interests are in developing statistical mechanics approaches to understand the emerging response of soft biological and bio鈥恑nspired networks based on the physical interactions between their building blocks. This research has applications in the mechanics of polymers, liquid crystal elastomers, soft biological networks, morphogenesis, and growth. Dr. Vernerey is the author of ~100 scientific publications in peer鈥恟eviewed journals and book chapters. He is also the recipient of the NSF career award in 2014 and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2017.

The collection and processing of information are essential tasks for the successful operation of natural and synthetic systems. Adaptive materials that respond mechanically to their environment present an opportunity to embed intelligent behaviors into mechanical structures by connecting their deformation states to abstract computing operations, both in digital (mechanologic) and analog forms. To explore these concepts, we first harness the bistable behavior of the waterbomb origami unit constructed from a humidity鈥恟esponsive polymer to reproduce the truth table of simple logic gate building blocks. Networks of waterbomb origami units demonstrate how neighboring stable state assignments modify the energetics of the individual waterbomb units, which could serve as a method to control the order and logic of stable state switching in the array. We then shift toward analog computing concepts by investigating the computing capacity of 2D nonlinear spring networks using a reservoir computing approach. Reservoir computing is a class of recurrent neural networks that trains only a readout layer of the network dynamics in contrast to tuning all the internal parameters of the network. We introduce a mechanical analog for the rectified linear unit (ReLU) activation function from neural network community and benchmark the memory capacity, nonlinearity and output tasks of mechanical ReLU networks sampled from a distribution of spring properties. Preliminary results indicate that the stiffness ratio of the ReLU spring (ie. ratio of the bilinear slopes) is a key driver of the nonlinearity score of the network, even more so than the incidence of activating the spring nonlinearity. Collectively, the results highlight the potential to harness multistability and nonlinear dynamics as a source of physical computation to augment the collection and processing of information in adaptive mechanical systems.

baby直播app听Philip Buskohl

Division at the U.S. Air Force Research Laboratory. The Division delivers materials and processing solutions to revolutionize AF capabilities in Survivability, Directed Energy, Reconnaissance, Integrated Energy and Human Performance. Phil has authored over 36 peer鈥恟eviewed papers ranging from the chemical鈥恗echanical feedback of selfoscillating gels, design of reconfigurable origami structures and mechanical computing concepts. His research interests include nonlinear elasticity, optimization methodology for material design, and mechanically adaptive materials.