Past Events

Seminars are held Tuesdays and Thursdays in POB 6.304 from 3:30-5:00 pm, unless otherwise noted. Speakers include scientists, researchers, visiting scholars, potential faculty, and ICES/UT Faculty or staff. Everyone is welcome to attend. Refreshments are served at 3:15 pm.

  • Additional Information

    Hosted by Irene Gamba

    Sponsor: Oden Seminar

    Speaker: Quentin Wargnier

    Speaker Affiliation: Associated Researcher, CMAP, Ecole Polytechnique, Palaiseau, France

  • Abstract

    This contribution deals with the fluid modeling of multicomponent magnetized plasmas in thermo-chemical non-equilibrium from the partially- to fully-ionized collisional regimes, aiming at simulating magnetic reconnection in Sun chromosphere conditions. Such fluid models are required for large-scale simulations by relying on high performance computing. The fluid model is derived from a kinetic theory approach, yielding a rigorous description of the dissipative and non-equilibrium effects and a well-identified mathematical structure. We start from a general system of equations that is obtained by means of a multiscale Chapman-Enskog method, based on a non-dimensional analysis accounting for the mass disparity between the electrons and heavy particles, including the influence of the electromagnetic field and transport properties. The latter are computed by using a spectral Galerkin method based on a converged Laguerre-Sonine polynomial approximation. Then, in the limit of small Debye length with respect to the characteristic scale in the solar chromosphere, we derive a two-temperature single-momentum multicomponent diffusion model coupled to Maxwell's equations, which is able to describe fully- and partially-ionized plasmas, valid for the whole range of solar chromosphere conditions.

    The second contribution is the development and verification of an accurate and robust numerical strategy based on a massively parallel code with adaptive mesh refinement capability. We rely on the canop code, based on two libraries: P4EST for the adaptive mesh refinement (AMR) capability and MUTATION++ for computing the transport properties with a high level of accuracy, in order to ensure that the full spectrum of scales and the dynamics of the magnetic reconnection process are captured. Finally, we present a 2D and 3D magnetic reconnection configuration in solar chromospheric conditions and assess the potential of the numerical strategy for simulating astrophysical plasmas.

    [[Work in collaboration with Marc Massot, Thierry Magin, Benjamin Graille and Nagi Mansour, as well as NASA SuperComputing Division of NASA Ames Research Center, and von Karman Institute, Belgium]]


  • Additional Information

    Hosted by Leszek Demkowicz

    Sponsor: ICES Seminar

    Speaker: Birkan Tunc

    Speaker Affiliation: Post-Doctoral Fellowm Oden Institute, UT Austin

  • Abstract

    In this study, a complete pathway to construct a constitutive model well suited for finite element analysis of viscoelastic media, in particular solid propellants, undergoing large deformation and damage is described. Damage is assumed to initiate by failure of the particle-binder bond or failure in the binder itself. Opening of the micro-cracks resulting from either failure is associated with the evolution of damage. Stress softening during unloading and reloading is captured via a cyclic function modifying the viscoelastic stress. The model accounts for the effect of temperature, pressure as well. The constitutive model was implemented in commercial software Abaqus for finite element analysis. Numerical algorithm used for implementation was explained in great detail.

    The model was validated using a set of solid propellant test data. Uniaxial and cyclic loading tests was used for calibration of both viscoelastic model and damage parameters. Both stress as dilatation responses of the material were investigated. The distributions of these parameters were found to be consistent with the stress–strain field predictions of the rocket motor analysis, indicating efficiency and stability of the computational algorithm. A three-dimensional stress analysis of a rocket motor under cyclic temperature loading was performed. The results were compared to the test data. It was concluded that the formulation and its implementation enable realistic stress analysis of solid rocket motors under general loading and environmental conditions, providing valuable information for their life assessment.

    Co-Authors: Birkan Tunc (Post-Doctoral Fellow at Oden Institute), Sebnem Ozupek (Associate Professor at Bogazici University, Istanbul, Turkey)

    Bio
    Birkan Tunc recieved his PhD in 2017 from mechanical engineering from Bogazici University, Istanbul with a focus on the constitutive modelling and finite element analysis of viscoelastic materials undergoing large deformations and damage. At the same time, he worked for research and development departments of international companies such as Ford Motor Company on the design of vehicle body structures using finite element analysis. He was a visiting scholar at ICES between 2018-20119 and since the beginning of 2019 he has been working as a post-doctoral fellow at Oden Institute on the modelling of brain cancer tumor growth.


Friday, Aug 16

Systematic Design of Decentralized Algorithms for Consensus Optimization

Friday, Aug 16, 2019 from 10:30AM to 12:30PM | POB 6.304

  • Additional Information

    Hosted by Ufuk Topcu

    Sponsor: ICES Seminar

    Speaker: Shuo Han

    Speaker Affiliation: Assistant Professor, Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC).

  • Abstract

    Decentralized optimization algorithms are widely used in the control of networked cyber-physical systems such as the power grid, transportation networks, and multi-robot teams. In a decentralized algorithm, the nodes (agents) collectively solve an optimization problem by solving part of the problem locally and exchanging messages over a communication network. In this talk, I will present a systematic procedure for designing decentralized optimization algorithms for a special class of problems in which the objective function is a sum of local objective functions. Specifically, I will show that a decentralized optimization algorithm can be synthesized by combining an existing base optimization algorithm (e.g., gradient descent) and a consensus tracking algorithm. A major benefit of this procedure is that one can separately choose the base optimization algorithm to accommodate different types of objective functions and the consensus tracking algorithm to accommodate different types of communication networks. In addition, parameters used in the synthesized algorithm can be selected in an automated manner by numerically computing a certificate of convergence using tools from robust control theory.

    Bio
    Shuo Han is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago (UIC). Previously, he was a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.E. and M.E. in Electronic Engineering from Tsinghua University in 2003 and 2006, and his Ph.D. in Electrical Engineering from the California Institute of Technology in 2014. His research interests lie broadly in the areas of optimization and control theory with applications in large-scale interconnected cyber-physical systems such as transportation networks and the power grid.


  • Additional Information

    Hosted by Ron Elber

    Sponsor: ICES Seminar-Molecular Biophysics Series

    Speaker: Pradipta Bandyopadhyay

    Speaker Affiliation: Computational and Integrative Sciences, Jawaharlal Nehru University

  • Abstract

    Electrostatics is one of the major interactions in molecular systems. One intriguing phenomenon in electrostatics is that two large molecules, having the same sign of charges, can attract each other in the presence of counterion and/or salt. In this work, we have investigated the salt-dependent attraction between the monomers of a protein, B-lactoglobulin, in spite of carrying a large net charge. The calculation of binding free energy of salt-dependent protein-protein interaction is challenging as it involves subtle changes in the electrostatic interaction. We have used a combination of molecular dynamics simulation and reference interaction site model (RISM) to calculate binding free energy between protein monomers at different salt concentrations, which matches the experimental results semi-quantitatively. An explanation of how salt modulates the electrostatic interaction between the protein monomers will be given.


Thursday, Aug 15

Modelling of non-equilibrium flows containing CO2.

Thursday, Aug 15, 2019 from 3:30PM to 4:30PM | POB 4.304

  • Additional Information

    Hosted by Irene Gamba

    Sponsor: ICES Seminar-Applied Mathematics Series

    Speaker: Alena Kosareva

    Speaker Affiliation: Research Associate, Applied Mathematics and Mechanic, Saint-Petersburg State University

  • Abstract

    Multi-temperature models for description of non-viscous 1D CO2 containing flows are investigated. Simulation of vibrational and chemical relaxation of three- and five-component mictures are obtained using different kinetic theory approaches. Effects of initial vibrational exitation, different free stream conditions and chemical scheme are studied.

    Application of simplified models for VV, VT transition and chemical reactions in CO2 flows is considered in the framework of an existing CFD solver.

    This will be a short lecture followed by a coffee/discussion time.


Tuesday, Aug 6

Supermodeling of a tumor with isogeometric analysis solvers

Tuesday, Aug 6, 2019 from 10:30AM to 12PM | POB 6.304

  • Additional Information

    Hosted by Thomas Yankeelov and Ernesto Lima

    Sponsor: ICES Seminar - CCO Series

    Speaker: Maciej Paszynski

    Speaker Affiliation: Professor, AGH University, Krakow, Poland

  • Abstract

    In this presentation, we show that it is possible to obtain reliable prognoses about cancer dynamics by creating the supermodel of cancer, which consists of several coupled instances (the sub-models) of a generic cancer model, developed with isogeometric analysis [1,2]. Its integration with real data can be achieved by employing a prediction/correction learning scheme focused on fitting several values of coupling coefficients between sub-models, instead of matching scores (even hundreds) of tumor model parameters as it is in the classical data adaptation techniques. We show that the isogeometric analysis is a proper tool to develop a generic computer model of cancer, which can be used as a computational framework for developing high-quality supermodels. The latent fine-grained tumor features e.g. microscopic processes and other unpredictable events accompanying its proliferation not included in the model, are hidden in incoming real data. The details of the supermodeling algorithm are the following. The tumor growth model involves tumor cells density, tumor angiogenic factor, oxygen concentration, extracellular and degraded extracellular matrices scalar fields, and the vascular network. The progression of the model is controlled by 21 parameters.

    Our goal is to obtain the progression of the supermodel similar to the one obtained from measurements. We first perform a sensitivity analysis of the tumor growth model, where we identify four more sensitive parameters, namely, tumor cell proliferation time, tumor cell survival time, and threshold oxygen con-centration for tumor cells to multiply or die. Based on the analysis, we set up three different models resulting in different tumor growth evolution. Next, we construct a supermodel by a linear combination of particular models, with coupling parameters obtained by the predictor/corrector technique. We run the three models, and after performing several time steps we correct the resulting fields, e.g. b(1)=C_{12}(b(1)-b(2))+C_{1,3}(b(1)-b(3)), where b(i) is the tumor cell density from model i. Next, we correct the fields by referring to the measurements e.g. b(1)=C_{1,meas}(b_meas-b(1)). Finally, we compute the average oxygen field, b=(b(1)+b(2)+b(3))/3, and we correct the coupling constants c_{i,j}=const*integral(b_meas-b)(b(i)-b(j)), where const is some magic correction constant, selected experimentally. Thus, by using the supermodeling approach we can simulate the reality by a linear combination of different models, and the resulting fields maybe not possible to obtain by any of the single models. This approach is similar to the one already used for climate supermodeling [3].

    Acknowledgement. National Science Centre, Poland grant no. 2016/21/B/ST6/01539
    [1] Marcin Łoś, Maciej Paszyński, Adrian Kłusek, Witold Dzwinel, Application of fast isogeometric L2 projection solver for tumor growth simulations, Computer Methods in Applied Mechanics and Engineering 316 (2017) 1257-1269
    [2] Marcin Łoś, Adrian Kłusek, Muhammad Amber Hassaan, Keshav Pingali, Witold Dzwinel, Maciej Paszyński, Parallel fast isogeometric L2 projection solver with GALOIS system for 3D tumor growth simulations, Computer Methods in Applied Mechanics and Engineering, 343, (2019) 1-22
    [3] Frank M. Selten, Francine J. Schevenhoven, Gregory S. Duane, Simulating climate with a synchronization-based supermodel, Chaos 27, 126903 (2017)


Thursday, Aug 1

Self-supporting design for Additive Manufacturing (3D printing)

Thursday, Aug 1, 2019 from 2PM to 3:30PM | POB 6.304

  • Additional Information

    Hosted by Omar Ghattas

    Sponsor: ICES Seminar

    Speaker: Hongyu (Alice) Zhu

    Speaker Affiliation: United Technologies Research Center

  • Abstract

    Additive manufacturing (AM) allows the creation of components in a layer-by-layer, additive fashion, which offers enormous geometrical freedom compared to conventional manufacturing technologies. It is widely recognized that topology optimization is essential to exploit the design space AM allows. However, overhang limitation in additive manufacturing prevents the direct production of topology optimized parts since extra support is usually required during printing and needs to be removed during post-processing. Lately, a layerwise filter has been incorporated in density-based topology optimization on uniform structured meshes for print-ready designs. The limitation of this technique is that the minimum allowable overhang angle (the angle a down-facing surface has with the base plate) is restricted to 45 degree. In practice, smaller overhang angles cause more roughness. At times, a greater overhang angle is desired due to smoothness requirement. In this work, we present a multi-layer based overhang constraint that allows minimum overhang angle greater than 45 degree without changing the element aspect ratio of the mesh. The newly developed constraint is demonstrated on 2D examples while it can be extended to 3D.

    Bio
    Hongyu (Alice) Zhu is a senior research engineer in System Dynamics and Optimization Group at United Technologies Research Center. She is currently working on optimal design for a wide range of systems including additive manufacturing, fire suppression system, aircraft cargo power driven unit configuration, etc. She graduated from University of Texas at Austin in 2017 with a PhD in Computational Science, Engineering and Mathematics, and joined United Technologies Research Center afterward.


Monday, Jul 29

New Computational Tools for Designing Drugs and Chemical Probes

Monday, Jul 29, 2019 from 2PM to 3:30PM | POB 6.304

  • Additional Information

    Hosted by Ron Elber and Pengyu Ren

    Sponsor: ICES Seminar-Molecular Biophysics Series

    Speaker: David Minh

    Speaker Affiliation: Illinois Institute of Technology

  • Abstract

    Most pharmaceuticals are small organic molecules that work via noncovalent interactions with biological macromolecules. Although drugs have saved or improved countless lives, drug discovery remains an inexact science that involves much trial and error. My research group has been developing fast and theoretically rigorous computer modeling methods to characterize noncovalent protein-ligand interactions. Most of our tools are based on implicit ligand theory, a theoretical framework that I derived to predict how tightly molecules bind and how they influence the population of conformations accessed by their targets. At this point, we have established that our methods are able to reproduce results of more computationally expensive approaches. We are working on making them more efficient and feasible to use with large libraries of chemical compounds. We have also advanced the theory of end-point binding free energy methods, in which binding affinity is predicted based on molecular simulations of the bound complex without a time-consuming series of intermediate thermodynamic states.


  • Additional Information

    Hosted by Leszek Demkowicz

    Sponsor: ICES Seminar

    Speaker: Mohamed Salah Ebeida

    Speaker Affiliation: Sandia National Laboratory

  • Abstract

    Bayesian inference provides a powerful framework for inductive reasoning which has proven applicable to an extensive range of disciplines and industrial applications. In this statistical theory, prior knowledge regarding quantities of interest are combined with observations to produce a full posterior distribution over the true values under consideration. MCMC methods have provided a practical framework for solving a very wide range of problems, yet there still remain a number of key difficulties and challenges for practitioners (e.g. issues with local trapping, inefficient mixing, correlated samples, and difficult convergence diagnostics).

    Our novel VoroSpokes framework for Bayesian inference resolves these issues by removing the underlying reliance on Markov Chains from the posterior sampling procedure. The posterior is instead adaptively approximated using an implicit domain partitioning in the form of Voronoi tessellations to construct a Voronoi Piecewise Surrogate (VPS) model. The surrogate model and domain partitioning are then used to prescribe a hierarchical sampling procedure designed to efficiently draw independent samples from the approximate posterior distribution. In this talk we will present the VoroSpokes framework, its theoretical properties and demonstrate its superior performance over MCMC in practice. ​​​

    Bio
    Mohamed Ebeida is a research scientist at Sandia National Laboratories and an expert in computational geometry. He specializes in Voronoi diagrams, hyper-plane sampling and sphere packing. He developed a number of nontraditional Voronoi-based algorithms with application to meshing, uncertainty quantification, optimization, and machine learning. He graduated from University of California Davis in 2008 with a PhD in Mechanical and Aeronautical Engineering and a Masters in Applied Mathematics. He worked for two years as a Postdoc at Carnegie Mellon University. In 2010, he joined Sandia National Laboratories where he actively works in exploring the potential of implicit Voronoi tessellations for a wide range on applications in low and high-dimensions.


Tuesday, Jun 18

  • Additional Information

    Hosted by Fabrizio Bisetti

    Sponsor: ICES Seminar

    Speaker: Lucas Esclapez

    Speaker Affiliation: Lawrence Berkeley National Laboratory (LBNL)

  • Abstract

    Experiments have demonstrated that electric fields can be employed in combustion applications to control the flame stabilization and reduce pollutant emissions. However, the multi-scale nature of the physical processes has so far preventedthe development of efficient unsteady numerical methods, able to further analyze the interactions between turbulent flows,combustion kinetics and electric fields. We propose an algorithm for low Mach number combustion that incorporates the chemical production of charged species and their transport induced by electric field. The strategy relies on the multi-implicit spectral deferred correction (MISDC) approach that allows for a strong and efficient coupling of the stiff drift/diffusion and reaction terms with the slower advection terms. Along with the momentum, energy and species transport equations, a Poisson equation is solved to provide a consistent electrostatic potential. To overcome the stringent time-stepping constraint due to the fast electron motion, a non-linear implicit solve of the electrostatic potential and electron number density equations is used. For the strategy to be amendable to large scale computations, the non-linear system is solved using a Jacobian-Free Newton Krylov method, for which a preconditioner is developed. We will present the details of the method and demonstrate its capabilities on steady and unsteady of one-dimensional cases.

    This is joint work with J. B. Bell and M. S. Day.

    Bio:
    Dr. Lucas Esclapez is a post-doctoral researcher in the Center for Computational Sciences and Engineering, at Lawrence Berkeley National Laboratory (LBNL). His current research focuses on the development of high-performance simulation tools to study the interactions between flames and electric fields. Prior to his appointment at LBNL, he was a post-doctoral researcher at the Center for Turbulence Research (Stanford, USA. 2015-2016) and CERFACS (Toulouse, France. 2016-2018) working with Large Eddy Simulations (LES) to investigate the combustion process in industrial systems. Dr. Esclapez received is PhD from CERFACS in 2015 where he was studying aeronautical gas turbines ignition.