Spring 2020 Fluids-Structure-Materials Seminars
Speaker: Jian-Xun Wang
Affiliation: Department of Aerospace and Mechanical Engineering, University of Notre Dame, IN
Date / Time: Wednesday, March 4, 2020, 4:30 - 5:30 pm
Location: AERO 114
Abstract:
Abstract
Recent advances in data science techniques, combined with the ever-increasing availability of high-fidelity simulation/measurement data open up new opportunities for developing data-enabled computational modeling of fluid systems. However, compared to most applications in the computer science community, the cost of data acquisition for modeling complex physical/physiological systems is usually expensive or even prohibitive, which poses challenges for directly leveraging the success of existing deep learning models. On the other hand, there is usually richness of prior knowledge including physical laws and phenomenological theories, which can be leveraged to enable efficient learning in the “small data” regime. This talk will focus on the data-driven/data-augmented modeling for fluid flows based using physics-constrained machine learning and Bayesian data assimilation techniques, where both the sparse data and physical principles are leveraged. Specifically, several separate but related topics will be covered, including machine learning assisted RANS turbulence modeling, physics-constrained deep learning for fluid surrogate modeling and super-resolution, and multi-fidelity Bayesian data assimilation for field inversion in fluid simulations.
Bio:
Dr. Jian-Xun Wang is an assistant professor of Aerospace and Mechanical Engineering at the University of Notre Dame. Dr. Wang received a Ph.D. in Aerospace Engineering from Virginia Tech in 2017 and was a postdoctoral scholar at the University of California, Berkeley before joining Notre Dame in 2018. His research focuses on developing data-driven/data-augmented computational modeling, which broadly revolves physics-constrained machine learning, Bayesian data assimilation, and uncertainty quantification. His current research interests involve surrogate modeling for fluid flows based on physics-constrained deep learning, data-augmented physical/physiological modeling (e.g., assimilation of 4D flow MRI in hemodynamic modeling and data-augmented intracranial modeling).
Speaker: Miguel A. Aguiló
Affiliation: Sandia National Laboratories, Albuquerque, NM
Date / Time: Thursday, February 27, 2020, 3:00 - 4:00 pm
Location: AERO 232
Abstract:
This talk presents Plato, an optimization-based design ecosystem developed at Sandia National Laboratories to meet mission need for agile component and system design. Plato was architected to solve multi-physics design problems and run on High Performance Computing (HPC) platforms. Plato’s architecture features three unique technologies. First, Plato is platform agnostic, which enables optimal code compilation and execution on multiple computer architectures. This unique feature enables Plato to leverage Graphical Processing Unit devices to run optimization-based design problems that are prohibitively expensive to run with state-of-the-art design tools. Second, Plato utilizes automatic differentiation technology to compute partial derivatives of the governing equations and performance criteria with respect to design parameter of interest. This enables rapid exploration and development of new multi-physics, mathematical optimization problem formulations. Third, Plato uses a Multiple Program, Multiple Data (MPMD) parallel programming model to execute multiple, concurrent parallel finite element simulations during optimization. The MPMD parallel model facilitates the solution of design under uncertainty problems in a timely manner. Plato, a one-of-a-kind design tool, permits engineers to explore multi-physics de- sign problems that would have been difficult to solve with current design tools. Multiple examples will be presented to highlighting the range of solutions that are now possible with Plato.
Bio:
Dr. Aguiló has received a B.S. degree in civil engineering in 2005 from the University of Puerto Rico and a M.S. degree in 2009 and a Ph.D. degree in 2011, both in structural engineering from Cornell University. Since 2012, Dr. Aguiló is a researcher and computer scientist at Sandia National Laboratories. Dr. Aguiló focuses on the development of optimization algorithms for High-Performance Computing, optimization under uncertainty methods, and design optimization methods for fundamental problems in solid mechanics and heat transfer with defense applications. Dr. Aguiló is a core developer of PLATO, a high- performance computing ecosystem for optimization-based design developed at Sandia National Laboratories. In addition, Dr. Aguiló has been part of other large-scale development projects at Sandia National Laboratories, including Sierra Mechanics and Trilinos. His research has been supported by ARL, AFRL, DOE, and industry. Dr. Aguiló has directed several large research projects, including ongoing projects with DARPA, AFRL and DOE. Dr. Aguiló has gained leadership experience by participating in NASA Vision 2040 for Integral, Multiscale Materials and Structure Modeling and simulation review panel (2017), organizing or co-organizing the Topology Optimization Roundtable (2016-2019), and serving on multiple Ph.D. dissertation committees.
Speaker: Lorenzo Tamellini
Affiliation: CNR - Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Pavia, Italy
Date / Time: Wednesday, February 19, 2020, 4:30 - 5:30 pm
Location: AERO 114
Abstract:
In this work we propose a methodology based on sparse grids for the Uncertainty Quantification (UQ) of sedimentary basins undergoing mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs with uncertain parameters. We discuss both forward and inverse UQ for this problem, whose quantities of interest (QoI) are the in-depth profiles of porosity, temperature and pressure at T=today. The methodology proposed is based on a sparse-grid approximation of the QoI, and in particular we will discuss an efficient methodology for the computation of the Sobol indices, to evaluate the impact of each random parameter on the total variability of the QoI. The inverse problem will be tackled with a Maximum Likelihood approach, sped up by replacing the full model evaluation with its sparse-grid approximate counterpart. We then consider the case of multi-layered basins, in which each layer is characterized by a different material. The multi-layered structure gives rise to discontinuities in the map from the uncertain parameters to the QoI. Because of these discontinuities, an appropriate treatment is needed to apply sparse grids quadrature and interpolation for UQ purposes. To this end, we propose a two-steps methodology which relies on a change of coordinates to align the interfaces among layers of different materials; note that the map from the physical to the reference domain is random because the location of the interfaces also depends on the values of the random parameters. Once this alignment has been computed (again by means of a sparse grid), a standard sparse-grid-based UQ analysis of the QoI can be performed within each layer. This procedure can then be seen as a composition of sparse grids, or ``deep sparse grid approximation''. The effectiveness of this procedure is due to the fact that the physical locations of the interfaces among layers feature a smooth dependence on the random parameters and are therefore themselves amenable to sparse grid polynomial approximations. We showcase the capabilities of our numerical methodologies through some synthetic test cases.
Bio:
Lorenzo Tamellini is a researcher at the Institute for Applied Mathematics and Information Technologies ``Enrico Magenes‘’, a research institute of the Italian National Research Council (IMATI-CNR) in Pavia. Previously, he was a postdoc for 1 year at the Department of Mathematics of Pavia University with prof. Giancarlo Sangalli, and for 3 years at Ecole Polytechnique Fédérale de Lausanne (EPFL) with prof. Fabio Nobile. Fabio Nobile was also his PhD advisor at Politecnico di Milano (MOX lab, Department of Mathematics), where he got a PhD in Mathematical Models and Methods for Engineering in March 2012. His research activities focus on two topics: 1) Uncertainty Quantification and surrogate modeling for parametric PDEs and 2) isogeometric analysis for PDEs.
Speaker: Monique McClain
Affiliation: Graduate Research Assistant, School of Aeronautics and Astronautics, Purdue University
Date / Time: Wednesday, February 12, 2020, 4:30 - 5:30 pm
Location: AERO 114
Abstract:
Solid rocket motors have been designed, tested, and characterized reasonably well since the 1960’s and have been used for a variety of applications, such as space shuttle boosters and missiles. The performance of several parts in a solid rocket motor, such as the propellant grain and the nozzle, are highly dependent on geometry and structural/thermal properties. Traditional manufacturing processes for creating solid propellant grains and ablative nozzles can limit performance, since it is very difficult, or sometimes impossible, to create multi-material components with fine features. Additive manufacturing (AM), which has been used for many aerospace applications, can potentially be used to make multi-material structures with intricate geometries. This could allow the creation of new propellant grains and nozzle designs which can allow improved optimization of a rocket motor for a specific mission. In general, commercial AM methods have not been able to 3D print high performance propellant or composite mixtures since they are extremely viscous materials. In this talk, I will highlight a new AM method called vibration assisted printing (VAP) which has been used to directly 3D print solid propellant and carbon fiber/polymer mixtures. I will also discuss how this manufacturing method allows the aerospace community the opportunity to investigate how new geometries and material gradients could be used to tailor the performance of solid rocket motors in ways that have never been done before. This work can also expand the capabilities of 3D printing with application to CubeSATs, satellite control systems, and small rockets to allow for rapid prototyping and production, as well as reduce costs compared to integrating more complex propulsion systems that require more thermal management, plumbing, or more electrical power.
Bio:
Monique McClain is a graduate research assistant with a background in propulsion and additive manufacturing who works at Maurice J. Zucrow Laboratories at Purdue University. In 2018, she received her M.S. from the School of Aeronautics and Astronautics from Purdue University and she received her B.S. in Aerospace Engineering from the University of California, San Diego in 2016. She is currently pursuing her Ph.D. and her main research focus is to develop and apply new additive manufacturing technologies to create geometrically intricate, multi-material solid propellant grains and functionalized ceramic parts. In addition, she has worked on other propulsion related projects, such as developing novel hybrid propellant grains and investigating combustion instabilities in combustors.
Speaker: John Evans
Affiliation: Assistant Professor, Department of Aerospace Engineering Sciences, University of Colorado Boulder
Date / Time: Wednesday, February 5, 2020, 4:30 - 5:30 pm
Location: AERO 114
Abstract:
Isogeometric structure-preserving discretizations have emerged as an attractive candidate for Computational Fluid Dynamics (CFD). In the setting of incompressible fluid flow, these discretizations satisfy conservation of mass in a pointwise manner, and as a byproduct, they preserve the underlying geometric structure of the Navier-Stokes equations. In this talk, I will first review the construction of isogeometric structure-preserving discretizations, discuss their basic properties, and provide numerical examples illustrating their promise as a CFD technology. I will then discuss recent research thrusts, including (i) structure-preserving turbulence models for large eddy simulation of turbulent flows, (ii) optimal multi-level solvers which exploit the properties of structure-preserving discretizations, and (iii) adaptive structure-preserving discretizations. I will conclude by discussing related and future research directions, including the application of isogeometric structure-preserving discretizations to fluid-structure interaction and magnetohydrodynamics.
Bio:
Dr. Evans is the Jack Rominger Faculty Fellow and an Assistant Professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences at the University of Colorado Boulder. Dr. Evans received his Ph.D. in Computational and Applied Mathematics from the University of Texas at Austin in 2011, and he worked as a postdoctoral fellow at the Institute for Computational Engineering and Sciences between 2012 and 2013. Dr. Evans’s research leverages synergies between computational mechanics and computational geometry to arrive at new and transformative approaches to the modeling and simulation of aerospace, naval, energy, and biological systems. A particular focus of Dr. Evans’s research is the design of structure-preserving methods for complex fluid flows and fluid-structure interaction. Additionally, Dr. Evans is actively involved in the development of fully integrated design, analysis, and optimization technologies using the framework of isogeometric analysis.
Speaker: Timothy Minton
Affiliation: Professor, Department of Chemistry and Biochemistry, Montana State University
Date / Time: Thursday, January 23, 2020, 10:00 - 11:00 am
Location: AERO 111
Abstract:
High-energy gas-surface interactions might seem to be exotic, but there are numerous situations where such interactions influence the outcome of practical endeavors, for example, spacecraft degradation in low-Earth orbit, heat shield ablation during atmospheric entry, and analysis of highly rarefied planetary atmospheres.
Studies of the relevant gas-surface processes can reveal the rich details of the chemical and physical processes whenever a surface is subjected to a gas at high collision energies or temperatures, thus providing a foundation upon which to advance space technology through improved materials, predictive models, and hardware design.
This presentation will highlight the fundamental aspects of energetic gas-surface scattering dynamics in the context of their applications to the three examples of spacecraft-environment interactions mentioned above.
Bio:
Timothy K. Minton is a Professor in the Department of Chemistry and Biochemistry at Montana State University, as well as a Senior Editor for J. Phys. Chem. A/B/C and an Associate Editor for J. Spacecr. Rockets. He is also a Fellow of the American Physical Society, American Chemical Society, and American Association for the Advancement of Science. He earned his B.S. in Chemistry from the Univ. of Illinois in 1980 and his Ph.D. in Chemistry from UC Berkeley in 1986. Following two post-doctoral positions, at the Univ. of Illinois and at the Univ. of Zürich, Switzerland, he became a Member of Technical Staff at the Jet Propulsion Laboratory in Pasadena, CA in 1989. In 1995, he joined the faculty at Montana State.
His current research projects include studies of gas-phase and gas-surface energy transfer and reactions, including boundary layer chemistry in shock layers on hypersonic vehicles, oxidation and decomposition of heat-shield materials, the concentration of gases in rarefied planetary atmospheres, and the development of new and more durable materials for use on spacecraft in low Earth orbit.