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).

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.