Past Seminars

Credible Computational Solid Mechanics for Critical Decision Making in Engineering

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Abstract: Advanced computational modeling, high performance computing technology, and extensive knowledge of simulation form a strong and unique foundation of research, development and engineering at Sandia National Laboratories that enable the Lab to meet its commitment of ensuring the national security of the United States.  Computational models are utilized extensively to predict the complex behavior of materials in multiphysics environments across a wide range of length and time scales, and analysts run simulations routinely to evaluate the performance and reliability of complicated engineering systems designed for national security applications.  In the past three decades, capabilities of simulation tools and models were advanced significantly, and numerous scientific questions and engineering challenges were resolved successfully with the help from computational simulations.  Examples include providing insight of non-linear material response in complex loading environments, examining the integrity of engineering structure when test data are insufficient, modeling microstructure and its linkage to material properties, and predicting aging and material property changes during service.  Although the progress of developing computational predictive capabilities has been highly encouraging so far, it is well recognized in the computational mechanics community that many issues in theories and numerical algorithms yet to be addressed.  While modeling and simulation are being used increasingly as the information-generating and decision-making tools in the cycle of engineering product from design to retirement, how to create and demonstrate credibility of computational analyses, especially for applications in the solid mechanics discipline, is becoming an inevitable challenge for model developers and computational analysts simply because neither codes nor models are perfect.

At this seminar, years of efforts at Sandia to advance the capability of computational solid mechanics modeling for national security and industry applications will be presented, highlighting their challenges and successes.  Lessons learned from bridging physics at different length scales and coupling different simulation codes will be shared.  Most importantly, strategies, including effective and ineffective ones, of developing and presenting model credibility will be discussed. 

Biographical Sketch: Dr. Eliot Fang is the Manager of the Solid Mechanics Department at Sandia National Laboratories. He received his B.S. degree from the National Central University in Taiwan and M.S. and Ph.D. degrees from the University of California at Santa Barbara, all in mechanical engineering. Dr. Fang’s research interest is to apply modeling approaches and high performance computing to elucidate mechanisms of material behaviors and to predict material behaviors at various length scales in different environments.  He has over 60 publications and 70 invited presentations reporting his technical accomplishments and contributions to materials modeling and mechanical simulations.  Dr. Fang is a Fellow of the American Society of Mechanical Engineers and a recipient of the 2006 Asian American Engineer of the Year Award.

Modeling and Control Additive Manufacturing Processes for Ceramics and Glass

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Abstract: Additive Manufacturing (AM), which has been referred to as the 4th revolution in manufacturing, is a truly disruptive class of manufacturing. In AM, location-specific mechanical properties can be tailored by grading materials and microstructure, complex geometries that cannot be manufactured with traditional processes can be fabricated, and cost-effective part repair and low volume manufacturing can be realized. However, AM processes have tremendous variability and are not well understood. This has led to significant research efforts into controlling these processes. This talk will discuss our research efforts in the control-oriented modeling and feedback control of two AM processes. The first process is a ceramic extrusion process known as Freeze-form Extrusion Fabrication (FEF) of ceramics, where an aqueous-based ceramic paste is extruded in a freezing environment. This process is ideal for the fabrication of ceramic parts with complex geometries and multiple materials. We will explore the major variations in this process, empirical modeling techniques to describe its dynamic behavior and construct control-oriented models, and methods to control the extrusion force. We will then transition to our work in the first principle, control-oriented modeling of the extrusion force and filament freezing time, and the understanding of the process that is elucidated from these models. The second AM process we will discuss is a new direct energy deposition process to additively manufacture glass. In the AM glass process, filament or fiber is fed into a molten pool of glass formed by a laser energy source. The process can be used to fabricate fully dense transparent free-form parts for gradient index optics, complex structures for embedded optics and waveguides, and freeform structures that open up the glass design space. We will discuss our work in understanding the process and discovering process parameter spaces suitable for fabrication. Two issues that limit the AM glass process are bubble formation and the challenge of placing the glass in a desired location. We will discuss our work in controlling these two issues and discuss future directions for this process.

Biographical Sketch: Dr. Robert G. Landers (landersr@mst.edu) is a Curators’ Distinguished Professor of Mechanical Engineering in the Department of Mechanical and Aerospace Engineering at the Missouri University of Science and Technology (formerly University of Missouri Rolla) and served as the department’s Associate Chair for Graduate Affairs for eight years. He received his Ph.D. degree in Mechanical Engineering from the University of Michigan in 1997. His research interests are in the areas of modeling, analysis, monitoring, and control of manufacturing processes (laser metal deposition, glass direct energy deposition, selective laser melting, freeze–form extrusion fabrication, wire saw machining, metal cutting, friction stir welding), estimation and control of lithium ion batteries and hydrogen fuel cells, and digital control applications. He has over 200 refereed technical publications, including 79 journal articles, an h index of 22 with 1734 citations (Scopus), and $6.4M in funding. He received the Society of Manufacturing Engineers’ Outstanding Young Manufacturing Engineer Award in 2004 and the ASME Journal of Manufacturing Science and Engineering Best Paper Award in 2014, is a Fellow of ASME, a senior member of IEEE and SME, and a member of ASEE. He is currently a program manager at the National Science Foundation, served as associate editor for the ASME Journal of Dynamic Systems, Measurement, and Control (2009–2012), ASME Journal of Manufacturing Science and Engineering (2010–2014), and the IEEE Transactions on Control System Technology (2006–2012), and is currently an associate editor for Mechatronics.

Reversible Solid Oxide Cells and Protonic Ceramic Fuel Cell Technologies as Flexible, Dispatchable Energy Resources

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Abstract: ​L​ow-cost, high efficiency, electrical energy storage (EES) is needed for the future electric grid which will include more variable energy resources, such as wind and solar. Movement towards predominately low-carbon energy systems requires renewable resources and could be accelerated by integration of high temperature electrochemical technologies. Currently, substantial penetration of wind and solar resources into the electric power grid is challenged by their intermittency and the timing of generation which can place huge ramping requirements on central utility plants, which are also limited in dynamic response capability. This talk will discuss employing novel EES systems derived from reversible fuel cell technology and advances in protonic ceramics as dispatchable energy resources. Reversible solid oxide cells (ReSOCs) are capable of providing high efficiency and cost-effective electrical energy storage. These systems operate sequentially between fuel-producing electrolysis and power-producing fuel-cell modes with storage of reactants and products (CO​2/​ CH​4g​ ases) in tanks for smaller-scale (kW) applications and between grid and natural gas infrastructures for larger scale (MW) systems. In this talk, the use of ReSOC technology for both grid-scale energy storage and as a Power-to-Gas platform that can address issues with high renewables penetration is presented. In stand-alone systems, strategies for effective thermal management and balance-of-plant systems integration in both operating modes are critical to achieving high roundtrip efficiencies. Design challenges and techno-economic analyses which suggest levelized cost of storage that ranges between 15 – 30 $/MWh are highlighted. A brief overview of recent progress in the performance of intermediate temperature (500-600°C) protonic ceramic fuel cells (PCFCs) which have demonstrated both fuel flexibility and increasing power density that approach commercial application requirements will also be given. The PCFCs investigated in this work are based on a BaZr​0.8Y​ ​0.2O​ ​3-δ(​ BZY20) thin electrolyte supported by BZY20/Ni porous anodes, and a triple conducting cathode material comprised of BaCo​0.4F​ e​0.4Z​ r​0.1Y​ ​0.1O​ ​3-δ(​ BCFZY0.1). Performance characteristics, modeling challenges, and techno-economic outlook of mixed-charge conducting PCFCs are presented.

Biographical Sketch: ​Dr. Robert Braun is Associate Professor of Mechanical Engineering at the Colorado School of Mines. He received a Ph.D. from the University of Wisconsin–Madison in 2002. From 2002-2007, Dr. Braun was at United Technologies Fuel Cell and Research Center divisions where he last served as project leader for UTC’s mobile solid oxide fuel cell (SOFC) power system development program. Dr. Braun has multidisciplinary background in mechanical and chemical engineering and his research focuses on energy systems modeling, analysis, techno-economic optimization, and numerical simulation of transport phenomena occurring within fuel cell and alternative energy systems. His industry experience encompasses development of low-NOx burners, CO​2-​ based refrigeration, and fuel cell technologies (including PEM, PAFC, MCFC, SOFC, and PCFC). Dr. Braun’s current research activities focus on high efficiency hybrid fuel cell/engine systems, renewable energy pathways to synthetic fuel production, grid-scale energy storage, novel protonic ceramics, supercritical CO​2 p​ ower cycles, and dispatch optimization of concentrating solar power plants. He is a Link Energy Foundation Fellow, a member of ASME, ECS, and ASHRAE, and holds 6 U.S. patents.

Physical biology at the semiconductor-enabled biointerfaces

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Abstract: ​Recent studies have demonstrated that in addition to biochemical and genetic interactions, cellular systems also respond to biophysical cues, such as electrical, thermal, and mechanical signals. However, we only have limited tools that can introduce localized physical stimuli and/or sense cellular responses with high spatiotemporal resolution. Inorganic semiconductors display a spectrum of physical properties and offer the possibility of numerous device applications. My group integrates material science with biophysics to study several semiconductor-based biointerfaces. In this talk, I will first pinpoint domains where semiconductor properties can be leveraged for biointerface studies, providing a sample of numbers in semiconductor-based biointerfaces. Next, I will present a few recent studies from our lab and highlight key biophysical mechanisms underlying the non-genetic optical modulation interfaces. In particular, I will present a biology-guided two-step design principle for establishing tight intra-, inter-, and extracellular silicon-based interfaces in which silicon and the biological targets have matched mechanical properties and efficient signal transduction. Finally, I will discuss new materials and biological targets that could catalyze future advances.

Biographical Sketch: ​Bozhi Tian received his Ph.D. degree in physical chemistry from Harvard University in 2010. He is now an associate professor at the University of Chicago, working on semiconductor-enabled fundamental studies of subcellular biophysics and soft matter dynamics. Dr. Tian’s accolades from his independent career include the Inaugural ETH Materials Research Prize for Young Investigators (2017), Presidential Early Career Awards for Scientists and Engineers (2016), and TR35 honoree (2012).

Recent Progress in Black-Box Function Optimization for Industry Problems

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Abstract: One of the most common and important problems in the engineering industry is, arguably, to optimize a black-box expensive-to-evaluate function given a strict budget. The function can represent a real-world experiment or a costly simulation code. Specifically, given a set of potential power plant layouts, how do we find the best layout defined against a set of Quantities of Interest? Given a steam turbine, how do we configure its geometry to achieve the best efficiency? How can we optimize the life of a machine by knowing its design variables and how they connect to damage? Given a set of Computational Fluid Dynamics simulations, can we optimize a blade structure for cooling? The problem of course extends to a broad range of other industries and academia as well. As a different example, borrowed from materials discovery, consider a set of binary alloy lattice points: Which atoms should be placed on said points to discover the ground states? Surely, for all these cases, the faster we achieve high-quality optima (ideally global and robust) in terms of resources, the lower the overall cost.

Towards answering these questions, at General Electric Research our team has developed and maintain an industry-strength Efficient Global Optimization scheme called “Intelligent Design and Analysis of Computer Experiments” (IDACE) which builds on a time-tested Gaussian Process meta-model called Bayesian Hybrid Modeling (BHM) originally built from Kennedy O’Hagan’s work.

Having introduced the BHM/IDACE framework, we present in detail a set of successful BHM/IDACE industry case studies and compare to other optimization approaches such as Genetic Algorithms. Finally, we go on to discuss a range of recent modifications and enhancements to these tools all driven from real-world customer needs.

Short Bio: Jesper Kristensen works as a Lead Engineer in the Probabilistics and Optimization team at General Electric’s (GE) Research Center in upstate New York. The team is managed by Dr. Liping Wang. He joined the team in the fall of 2015 as a research engineer. Among other projects, he is currently in charge of a $1MM project leading six engineers to ensure GE stays ahead in Probabilistic capabilities including, but not limited to, meta-modeling, optimization, uncertainty quantification, and uncertainty propagation. He is also the project leader on multiple damage modeling efforts to create Digital Twins of steam turbines.

 

Jesper is a graduate of the Technical University of Denmark (DTU) and holds a Ph.D. from Cornell University in Applied and Engineering Physics advised by Prof. Nicholas Zabaras. His work has generally focused on surrogate modeling and on advanced optimization methods such as adaptive sequential Monte Carlo and Bayesian Global Optimization for improving materials discovery and test cost reduction.

Layer-to-Layer Control in Laser Metal Direct Energy Deposition Additive Manufacturing

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Abstract: ​Additive manufacturing (AM), or 3D printing, is beginning to deliver on its long-promised potential to transform industrial production.  Already, tooling and molds are making regular use of AM’s rapid CAD-to-part flexibility to deliver in days what previously took months.  In addition, AM facilitates much greater geometric complexity, which increases the value proposition for AM fabricated parts that are serving in increasingly critical roles.  However, the rate of industrial insertion remains slow due to stubborn problems in process variability arising from the spatial and dynamic complexity of AM, amplifying challenges in qualification.  In-process measurement and analysis, and the utilization of that data in closed-loop feedback control, are widely regarded as the remedy.   This talk will explore one such instance in a blown-powder, direct energy deposition (sometimes referred to as LENS) process.  Here, a laser scanner is used to detect and correct geometric anomalies.  The talk will consider how in-layer and layer-to-layer dynamics may couple to create multi-dimensional dynamic behavior not typically considered, and how novel control methods may stabilize these processes.

Biographical Sketch: Dr. Douglas A. Bristow is currently an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Missouri University of Science and Technology (Missouri S&T).  He received his B.S. in Mechanical Engineering from Missouri S&T in 2001.  He received his M.S. and Ph.D., also in Mechanical Engineering, from the University of Illinois at Urbana-Champaign in 2003 and 2007, respectively.  Dr. Bristow is the Director of the Center for Aerospace Manufacturing Technologies, an industry consortium that currently includes eleven member companies.  He has more than 80 peer-reviewed publications and his research interests include precision motion control, repetitive and iterative process control, additive manufacturing process control, atomic force microscopy, and volumetric error compensation in machine tools and robotics.  Dr. Bristow’s research is currently funded by the National Science Foundation, the Department of Energy, the Digital Manufacturing and Design Innovation Institute, and multiple companies.  He is an Associate Editor at the ASME Journal of Dynamic Systems, Measurement and Control.

Mechanics under the Fold: How Origami Creates Sophisticated Mechanical Properties

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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.

A Multiscale Moving Contact Line Theory and Simulation of Droplet Spreading and Cell Durotaxi

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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.

Predicting Fuel Properties of Potential Biofuels Using an Improved Artificial Neural Network Based on Molecular Structure

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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).