The new UConn AIAA student chapter competed for the first time in the Spaceport America Cup during the summer of 2018. Advised by ME professor, Dr. Jackie Sung, the students designed and built a rocket for the competition. More details on the School of Engineering website.
Abstract: Origami, the ancient Japanese art of paper folding, is not only an inspiring technique to create sophisticated shapes, but also a surprisingly powerful method to induce nonlinear mechanical properties. Over the last decade, advances in crease design, mechanics modeling, and scalable fabrication have fostered the rapid emergence of architected origami structure and material systems. They typically consist of folded origami sheets or modules with intricate three-dimensional geometries, and feature many unique and desirable mechanical properties like auxetics, tunable nonlinear stiffness, multi-stability, and impact absorption. Rich designs in origami offer great freedom to prescribe the performance of such origami structures and materials. In addition, folding offers a unique opportunity of fabrication at vastly different sizes. This talk will highlight our recent studies on the different aspects of origami-based structures and materials–geometric design, mechanics analysis, and achieved properties–and discusses the challenges ahead.
Bio Sketch: Dr. Suyi Li is an assistant professor of mechanical engineering at the Clemson University. He received his Ph.D. at University of Michigan in 2014. After spending two additional years at Michigan as a postdoctoral research fellow, he moved to Clemson in 2016 and established a research group on dynamic matters. His technical interests are in origami-inspired adaptive structures, multi-functional mechanical metamaterials, and bio-inspired robotics. Within his first three years at Clemson, Dr. Li has secured more than one million dollars of research funding, including the prestigious NSF CAREER award. His paper on fluidic origami received the Best Paper Award by the ASME Branch of Adaptive Structures and Material Systems.
Abstract: In this talk, we present a novel multiscale moving contact line (MMCL) theory, which offers a powerful numerical simulation method for modeling and analysis of dynamic wetting, liquid droplet spreading on solid substrates, and various capillary motion phenomena. In the proposed multiscale moving contact line theory, we couple molecular scale adhesive interaction i.e. the van der Waals type interaction force and the macroscale fluid mechanics to solve droplet motions on solid substrates. In specific, we combine a coarse-grained adhesive contact model with a modified Gurtin-Murdoch surface hydroelasto-dynamics theory and the Navier-Stokes equation in the bulk fluids to formulate the multiscale moving contact line hydrodynamics theory in order to simulate a broader class of colloidal and soft matter physics phenomena, and related chemomechanical problems, such as cell motility, water spider walking, colloid suspension, and gas bubble in water, etc.
The advantage of adopting the coarse grain adhesive contact model in the moving contact line theory is that it can levitate and separate the liquid droplet with the solid substrate, so that the proposed multiscale moving contact line theory avoids imposing the non-slip condition, and then it removes the subsequent shear stress singularity problem, which allows the surface energy difference and surface stress propelling droplet spreading naturally.
We have also developed a soft matter model for biological cells that can model actin polymerization and ATP hydrolysis, and retrograde flow in cellular lamellipodia. By employing the MMCL method, we have successfully simulated cell durotaxi over the soft elastic substrates with non-uniform elastic stiffness. By employing the proposed method, we have successfully simulated droplet spreading over various elastic substrates and cell durotaxi over the substrates with non-uniform elastic stiffness. The obtained numerical simulation results compare well with the experimental and molecular dynamics results reported in the literature.
Biographical Sketch: Dr. Shaofan Li is currently a full professor of applied and computational mechanics at the University of California-Berkeley. Dr. Li graduated from the East China University of Science and Technology (Shanghai, China) with a BS degree in 1982; he also holds MS Degrees from both the Huazhong University of Science and Technology (Wuhan, China) and the University of Florida (Gainesville, FL, USA) in 1989 and 1993 respectively. In 1997, Dr. Li received a PhD degree from the Northwestern University (Evanston, IL, USA), and he was also a post-doctoral researcher at the Northwestern University during 1997-2000. In 2000, Dr. Li joined the faculty of the Department of Civil and Environmental Engineering at the University of California-Berkeley. Dr. Shaofan Li is the recipient of IACM Fellow Award [2017]; Distinguished Fellow Award of ICCES [2014]; ICACM Computational Mechanics Award [2013], USACM Fellow Award (2013), A. Richard Newton Research Breakthrough Award [2008], and NSF Career Award [2003]. Dr. Li has published more than140 articles in peer-reviewed scientific journals (SCI) with h-index 43 (Google Scholar), and he is also the author of two research monographs/graduate textbooks.
Abstract: The next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements to diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven prediction of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials for testing, a predictive model would allow chemists to screen fuel properties of potentially desirable fuel candidates at the ideation stage. Recent work has shown that predictive models, in this case artificial neural networks (ANN’s) analyzing quantitative structure property relationships (QSPR’s), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel’s CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. Model validation and expansion of the experimental database used in this study implemented DCN data acquired using an IQT. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types. The approach enables researchers to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.
Biographical Sketch: Hunter Mack is an Assistant Professor in the Department of Mechanical Engineering at the University of Massachusetts Lowell. His research focuses on combustion, biofuels, and energy efficiency. Prior to joining UML, he was a Project Scientist & Lecturer at the University of California at Berkeley, a Senior Engineer at solar concentrator start-up Banyan Energy, and a Postdoctoral Researcher in the Combustion Analysis Laboratory at UC Berkeley. He received his M.S. (2005) and Ph.D. (2007) from UC Berkeley with an emphasis on multi-component fuels in Homogeneous Charge Compression Ignition (HCCI) engines. He also holds a B.S. in Mechanical Engineering from Washington University in St. Louis and a B.A. in Physics from Hendrix College (Conway, Arkansas).
The talk will focus on two themes. The first will be an overview of our current ARPA-E project to condense water from flue gas for dry-cooled power plants power plants. Water use by power plants is an increasing concern across the U.S., and is particularly problematic in arid regions, such as the southwest. In this project, an advanced two-phase thermosyphon concept is employed that removes several of the traditional limitations of conventional thermosyphons. The condensed water can be used to pre-cool the condenser air to reduce the effective ambient temperature, for turbine inlet air evaporative cooling, or for other uses in the plant, as needed. The second portion of the talk will provide a short overview of other energy-related research activities in the Mechanical Engineering department at Stony Brook University with the goal to explore future opportunities for collaboration and joint projects between both institutions.
Bio: Jon Longtin joined the Mechanical Engineering Faculty at Stony Brook University in 1996. He came to Stony Brook after receiving his Ph.D. degree in 1995 from U.C. Berkeley, followed by a one-year postdoc at the Tokyo Institute of Technology in Japan. His research interests include energy conservation, innovative energy transfer and storage, and energy monitoring and diagnostics, as well as laser materials processing, particularly with ultrafast lasers and the development of sensors for harsh environments. His research has been funded by NSF, DOE, DOD, NASA, NYSERDA, and a variety of industrial sources. He is the author of over 140 technical publications and holds 11 issued and pending patents. He has received the Presidential Early Career Award for Scientists and Engineers, two Excellence in Teaching Awards, and an R&D 100 award. He is a licensed Professional Engineer in New York State and serves as a technical advisor to a variety of companies and non-profit organizations.
The U.S. Army Research Laboratory (ARL) was activated 25 years ago with a mission to discover, innovate and transition science and technology to ensure dominant strategic land power. One of key research strategies at ARL is a development of superior protection systems for individual warfigter and vehicles. The protective systems often use polymers due to their low weight, good strength and toughness which improves resistance to ballistic penetration.
In this presentation, we will review fundamental research projects carried out at ARL and aimed at understanding and prediction of mechanical properties for polymers at high strain rates. We will focus on semicrystalline polyethylene discussing onset of crystallization in polymer melt upon drawing and cooling. Our model includes amorphous domains, explains experimentally known mechanics and supports fracture mechanism through chain pulling. We will discuss shock propagation in anisotropic semicrystalline models at atomistic level.
We also study emerging 2D polymers inspired by Kevlar(R) which is well known for its remarkable strength and stiffness facilitated by the hydrogen bonds formed between Kevlar chains. Molecular and micromechanical calculations predict that ensembles of 2D molecules bonded with hydrogen bonds will form stiff, strong and tough films of unprecedented mechanical performance.
Bio: Dr. Jan Andzelm serves as the Team Leader of the Multiscale Modeling Team in the Polymer Branch at ARL investigating properties of materials important for Soldier protection. The team is developing and applying novel computational techniques at quantum mechanical, atomistic and mesoscale levels aimed at understanding and predicting structural, mechanical and electronic properties of macromolecules and composite materials.
Dr. Andzelm has co-authored over 140 scientific papers and book contributions that attracted more than 14000 citations and generated h-index of 42. Dr. Andzelm was selected as a U.S. Army Research Laboratory Fellow in 2010.
Abstract: This presentation is focused on Isogeometric Analysis (IGA) with applications to solids and structures, starting with early developments and results, and transitioning to more recent work. Novel IGA-based thin-shell formulations are discussed, and applications to progressive damage modeling in composite laminates due to low-velocity impact and their residual-strength prediction are shown. Fluid–structure interaction (FSI) employing IGA is also discussed, and a novel framework for air-blast-structure interaction (ABSI) based on an immersed approach coupling IGA and RKPM-based Meshfree methods is presented and verified on a set of challenging examples. The presentation is infused with examples that highlight effective uses of IGA in advanced engineering applications.
Bio: Yuri Bazilevs is the E. Paul Sorensen Chair in the School of Engineering at Brown University. He was previously a Professor and Vice Chair in the Structural Engineering Department at the University of California, San Diego. Yuri is the original developer of Isogeometric Analysis (IGA), a new computational methodology that aims to integrate engineering design (CAD) and simulation (FEM). For his research contributions Yuri received a number of awards and honors, including the 2018 ASCE Walter L. Huber Research Prize. He is included in the 2014-2018 lists of Highly Cited Researchers, both in the Engineering and Computer Science categories.
Due to the hierarchical nature of high-temperature hydrocarbon oxidation, modeling the combustion chemistry of higher hydrocarbon fuels typically requires a fuel-specific reaction model that describes the fragmentation of the fuel into small species, and a foundational fuel chemistry model that describes the oxidation of these species. Shared by the combustion of large hydrocarbons, the foundational fuel chemistry is also the rate-limiting step and therefore a crucial part for constructing reliable combustion models for any higher hydrocarbons. The talk examines the aforementioned problems from both ends. The Foundational Fuel Chemistry Model (FFCM) is an effort directed at developing a reliable combustion model for the foundational fuels with rate parameters optimized and uncertainty minimized. The first version, FFCM-1,optimized for H2,H2/CO,CH2O and CH4 combustion,was constrained with carefully evaluated fundamental combustion data. The model reconciles all the experimental targets chosen and has significantly reduced prediction uncertainties. The remaining kinetic uncertainties in FFCM-1 were further analyzed with extinction and ignition residence time predictions in perfectly-stirred reactor conditions to examine the critical remaining kinetic uncertainties in FFCM-1. The optimization and uncertainty quantification procedures were also extended to include the optimization of activation energies. As a single-component large-fuel example, JP10 was studied using the Hybrid Chemistry (HyChem) approach, which assumes a decoupled fuel pyrolysis and oxidation of pyrolysis products. The pyrolysis model is described with highly-lumped steps and optimized against experimental data from shock tube and flow reactor measurements. The model performance will be discussed.
Beyond the mere notion of a material, metastructures draw their unique characteristics from their finite size and the existence of interfaces. The resulting structural assemblies feature unprecedented performance in terms of stress wave mitigation, wave guiding, acoustic absorption, and vibration isolation.
The talk illustrates the frequency-selective properties of periodic metastructures, which result in their ability to direct waves in preferential direction and attenuate vibrations at certain frequencies. Such properties are observed in complex structural lattices, and in structural components equipped with periodic arrays of adaptive electromechanical resonators. The presentation will also introduce basic concepts that govern the onset of localized, interface wave modes. Specifically, spring-mass systems, lattices, and plates with internal resonators will be presented as part of a framework that seeks for mechanical lattices that exhibit one-way, edge-bound, defect-immune, wave motion. Finally, quasi-periodic structural assemblies are introduced as configurations that support vibration confinement in systems that are not ordered, but are described by deterministic property distributions. Results for beam and plate structures with quasiperiodic arrangements of grounding springs and lumped masses are presented to illustrate the unique dynamic behavior characterized by a multitude of highly localized modes of vibration.
Biographical Sketch: Massimo Ruzzene is the Pratt and Whitney Professor in the Schools of Aerospace and Mechanical Engineering at Georgia Institute of Technology. He received a PhD in Mechanical Engineering from the Politecnico di Torino (Italy) in 1999. He is author of 2 books, more than 160 journal papers and 200 conference papers. He has participated as a PI or co-PI in various research projects funded by the Air Force Office of Scientific Research (AFOSR), the Army Research Office (ARO), the Office of Naval Research (ONR), NASA, the US Army, US Navy, DARPA, the National Science Foundation (NSF), as well as companies such as Boeing, Eurocopter, Raytheon, Corning and TRW. Most of his current and past research work has focused on solid mechanics, structural dynamics and wave propagation with application to structural health monitoring, metamaterials, and vibration and noise control. M. Ruzzene is a Fellow of ASME, an Associate Fellow of AIAA, and a member of AHS, and ASA. He served as Program Director for the Dynamics, Control and System Diagnostics Program of CMMI at the National Science Foundation between 2014 and 2016.