UTIAS Seminar Series

Registration Instructions:
Please register for one or more instances of this seminar.

August 23, 2021

14:00 - 15:00 PM

CCSE-UTIAS Joint Seminar: High-order and reduced-order numerical methods based on implicit tracking for shock-dominated flows

Matthew Zahr

Abstract:

We introduce a high-order numerical method for approximating solutions of shock-dominated flows without requiring nonlinear stabilization, e.g. limiting or artificial viscosity, by tracking these features with the underlying high-order mesh. Central to the framework is a high-order discontinuous Galerkin (DG) discretization of the governing equations and an optimization problem whose solution is the nodal coordinates of a feature-aligned mesh and the corresponding DG approximation to the flow; in this sense, the framework is an implicit tracking method, which distinguishes it from methods that aim to explicitly mesh relevant features. The optimization problem is solved using a novel sequential quadratic programming method that simultaneously converges the mesh and DG solution, which is critical to avoid nonlinear stability issues that would come from computing a DG solution on an non-aligned mesh. We extend the method to define an optimization-based model reduction framework to further reduce the cost of shock-dominated flow simulations. The proposed reduction framework seeks to align discontinuities in the solution with discontinuities in the reduced basis by deforming the underlying domain, which effectively removes the convection-dominated nature of the solution and circumvents the Kolmogorov n-width reducibility limitation by defining a nonlinear approximation manifold. We use the proposed methods to solve a number of relevant two- and three-dimensional compressible flows with complex discontinuity surfaces and demonstrate the potential of the method to provide accurate approximations with very few degrees of freedom.

Biography:

Matthew Zahr is an assistant professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He received his PhD in Computational and Mathematical Engineering from Stanford University in 2016 and from 2016-2018 was the Luis W. Alvarez Postdoctoral Fellow in the Department of Mathematics at Lawrence Berkeley National Laboratory. His research interests include high-order methods for computational physics, PDE-constrained optimization, model reduction, computational methods for resolving shocks and discontinuities, and multiscale methods.

July 29, 2021

14:00 - 15:00 PM

Dynamic Geometry Control for More Automated Aerodynamic Shape Optimization

Gregg Streuber

Abstract:

Aerodynamic shape optimization is a field coupling computational fluid dynamics with numerical optimization.  When designing an aircraft (or components thereof) aerodynamic shape optimization permits the user to define an initial geometry and a set of requirements the design must satisfy, and then iteratively locate the best-performing geometry satisfying all of these conditions. As a result of growing interest in unconventional aircraft offering step reductions in carbon emissions, aerodynamic shape optimization is being applied to increasingly difficult problems at the same time that the rising costs of increasingly high-fidelity CFD simulations increase the penalty associated with slower convergence. Such issues impede automation by requiring increased user skill and intervention, as well as larger computational resources, to achieve satisfactory results. This work seeks to improve the automation of aerodynamic shape optimization through the application of a novel dynamic geometry control (DGC) algorithm.  This partially automates the design of the geometry control, a critical component of any aerodynamic shape optimization problem, and permits faster, smoother convergence even in difficult design spaces.  The algorithm is then demonstrated to offer deeper convergence, up to 70%, with reduced requirements on the user, compared to conventional approaches across widely varying classes of problems.

Biography:

Gregg Streuber received his Bachelor of Mechanical Engineering from the University of Manitoba in 2014, his Master of Applied Science in Aerospace Engineering from the University of Toronto Institute for Aerospace Studies (UTIAS) in 2017, and is currently in his final year of a PhD at UTIAS under the advisement of Prof. David Zingg.

June 24, 2021

14:00 - 15:00 PM

Efficient Flutter Prediction Using Reduced-Order Modeling with Error Estimation

Brandon Lowe

Abstract:

Aeroelastic flutter poses a significant challenge for the design of safe unconventional aircraft. Nonlinear flow behavior in the transonic regime is not captured by traditional flutter analysis techniques based on linear aerodynamic methods. Alternatively, accurate flutter predictions can be obtained with the use of high-fidelity computational fluid dynamics (CFD). But CFD-based flutter analysis is impractical due to the computational costs associated with the large number of degrees-of-freedom in the aerodynamic model. In this presentation, we will discuss a model order reduction approach for flutter analysis. Using a projection-based model reduction approach, we create an aerodynamic reduced-order model (ROM) from the linearized high-dimensional equations. The aerodynamic ROM contains far fewer degrees-of-freedom relative to the original CFD-based model. By coupling the aerodynamic ROM to a structural model, the challenge of flutter analysis is reduced to a tractable eigenvalue problem. Additionally, a dual-weighted residual-based error estimator will be presented which can approximate the error in the eigenvalues obtained from the reduced aeroelastic model. This error estimator is used to automate the construction of the aerodynamic ROM and provides the user with a level of certainty in the results.

Biography:
Brandon Lowe received a Bachelor of Applied Science in Mechanical Engineering from the University of Ottawa. He is currently a PhD candidate at the University of Toronto Institute for Aerospace Studies in the Computational Aerodynamics Group under the supervision of Professor David W. Zingg. His research is focused on applying reduced-order modeling techniques for dynamic aeroelastic analysis. In 2019, Brandon received the Best Student Abstract award at the CASI AERO 19 Aeronautics Conference.

14:00 - 15:00 PM

Efficient Flutter Prediction Using Reduced-Order Modeling with Error Estimation

Brandon Lowe

Abstract:

Aeroelastic flutter poses a significant challenge for the design of safe unconventional aircraft. Nonlinear flow behavior in the transonic regime is not captured by traditional flutter analysis techniques based on linear aerodynamic methods. Alternatively, accurate flutter predictions can be obtained with the use of high-fidelity computational fluid dynamics (CFD). But CFD-based flutter analysis is impractical due to the computational costs associated with the large number of degrees-of-freedom in the aerodynamic model. In this presentation, we will discuss a model order reduction approach for flutter analysis. Using a projection-based model reduction approach, we create an aerodynamic reduced-order model (ROM) from the linearized high-dimensional equations. The aerodynamic ROM contains far fewer degrees-of-freedom relative to the original CFD-based model. By coupling the aerodynamic ROM to a structural model, the challenge of flutter analysis is reduced to a tractable eigenvalue problem. Additionally, a dual-weighted residual-based error estimator will be presented which can approximate the error in the eigenvalues obtained from the reduced aeroelastic model. This error estimator is used to automate the construction of the aerodynamic ROM and provides the user with a level of certainty in the results.

Biography:
Brandon Lowe received a Bachelor of Applied Science in Mechanical Engineering from the University of Ottawa. He is currently a PhD candidate at the University of Toronto Institute for Aerospace Studies in the Computational Aerodynamics Group under the supervision of Professor David W. Zingg. His research is focused on applying reduced-order modeling techniques for dynamic aeroelastic analysis. In 2019, Brandon received the Best Student Abstract award at the CASI AERO 19 Aeronautics Conference.

April 22, 2021

14:00 - 15:00 PM

Impact of ethanol blending on soot in turbulent swirl-stabilized Jet A-1 spray flames in a model gas turbine combustor

Taylor Rault

Abstract:

Ethanol is commonly known to be a biofuel. As it is often used as an additive to gasoline in internal combustion engines for automotive applications, much research has been performed to understand the combustion and general fuel characteristics of ethanol. In addition to its renewability and ability to be used as a fuel additive, the oxygenated nature of ethanol is thought to contribute to reductions in soot emissions. However, the physical properties of ethanol differ substantially from typical hydrocarbon fuels; when added to another fuel, even for small ethanol concentrations, blend properties can differ significantly from the base fuel. Moreover, there is conflict in the literature as to the effects of ethanol addition on the sooting tendencies of fuels. The research being presented helps to address this issue by measuring and accounting for the effects of fuel physical properties on soot formation in a well-characterized swirl-stabilized combustion platform. This talk will discuss the effects of ethanol addition to hydrocarbon fuels, describe the benefits of a swirl-stabilized combustion platform, and show the need for model-validation suited data prior to presenting the experimental methods and results. After examining the results of the study, the broader implications of the work and the viability of ethanol as an aviation fuel additive will be discussed.

Biography:
Taylor Rault received his BSc in Mechanical Engineering from the University of Alberta and his MASc from the University of Toronto Institute for Aerospace Studies (UTIAS). He is currently a research associate in Prof. Gülder’s Combustion and Propulsion Lab at UTIAS and is about to begin his PhD in Mechanical Engineering at Stanford University.

April 1, 2021

14:10 - 15:10 PM

An In-Depth Look at Shape Gradient Calculations for Topology Optimization

Adrian Butscher

Abstract:

In this talk, I will delve into the mathematical details of the derivation of the shape gradient in non-parametric, boundary-based topology optimization approaches (such as the level set method). The shape gradient of an objective function that depends on the solution to a PDE in the shape, such as linear elasticity, is the rate of change of the objective function with respect to changes in the shape’s boundary. Although the calculation of the shape gradient is quite similar in nature to the more familiar sensitivity calculations in PDE-constrained optimization, there are many important differential geometric subtleties that make the calculation much more challenging – and very interesting

Biography:
Adrian Butscher is a Senior Principal Research Scientist in geometry processing. His research focuses on the design of algorithms for analysis, synthesis and simulation of discretized 3D geometry.

Adrian began his career as an academic mathematician specialized in differential geometry, partial differential equations, and the calculus of variations. He has conducted fundamental research in several fields related to area-minimizing surfaces. He has held positions at the Max Planck Institute for Gravitational Physics and Geometric Analysis, and in the mathematics departments at the University of Toronto and at Stanford University. More recently, as an assistant professor in the Max Planck Centre for Visual Computing and Communication (jointly at Stanford University and at the Max Planck Institute for Informatics), he has developed expertise and conducted research in geometry processing.
Projects he has contributed to include: intrinsic symmetry detection, segmentation and deformation of 3D models, automatic generation of correspondences between 3D models, and applications of optimal transportation distances in shape analysis. Prior to coming to Autodesk, Adrian has also held a visiting scholar position at Pixar Animation Studios.

Adrian received his B.Sc. in mathematics and physics at the University of Toronto and his Ph.D. in mathematics at Stanford University.Adrian received his B.Sc. in mathematics and physics at the University of Toronto and his Ph.D. in mathematics at Stanford University.

More about Adrian Butscher

March 25, 2021

14:10 - 15:10 PM

Uncertainty Quantification of Additively Manufactured Material using Digital Image Correlation

Daniel Pepler

Abstract:

Additive manufacturing is an enticing technology, it is very effective for manufacturing custom and complex structures, and it has the potential to play a major role in modern manufacturing. However, there are still major barriers keeping this technology from being adopted, namely the difficulty in accessing the material properties. Currently, composite models are used to describe the anisotropic nature of additively manufactured material, but there is large variation in reported material properties based on the printing path and sample preparation. Additionally, there are no methods which can quantify the stochastic properties associated with additive manufacturing. The research being presented addresses this issue, developing a model which can predict the local Young’s moduli based on digital image correlation (DIC) strain data. This talk will cover the sample preparation, testing procedure, random field modelling, and stochastic property extraction. Finally, it will illustrate how well different finite element models can replicate the experimental results.

Biography:
Daniel Pepler received his Bachelor’s degree in Nanotechnology Engineering at the University of Waterloo, in Waterloo Canada, and is currently a PhD Candidate at the University of Toronto Institute for Aerospace Studies, in Toronto Canada. His current research focus is on quantifying additively manufactured material for applications in topology optimization, supervised by Craig Steeves at the Advanced Aerospace Structures Lab. Daniel Pepler received a Best Paper award at the International Conference on Aerospace System Science and Engineering 2019.

Aerodynamic Shape Optimization of Boundary Layer Ingesting S-Duct Intakes

Chris Chiang

Abstract:

Future improvements in overall aircraft efficiency require an increasingly closer coupling of airframe and engine components. Boundary layer ingesting (BLI) embedded engines are a highly integrated aeropropulsive system that can potentially improve the propulsive efficiency of aircraft compared to conventional podded engines. For embedded engines with offset intakes, serpentine ducts (S-ducts) are often used to direct air into the engine. Duct curvature, along with BLI, are the main contributors to flow distortions that negatively impact engine performance and reduce the potential gain in propulsive efficiency. Hence for BLI S-ducts to be practical, they must overcome the challenge of reducing flow non-uniformities. The discussion will present the use of aerodynamic shape optimization to reduce flow distortion within an S-duct intended for a high-subsonic, unmanned flight vehicle application.

Biography:
Chris received his Bachelor’s degree in Mechanical Engineering at the University of Waterloo, in Waterloo Canada. He is currently in the second year of his MASc degree conducting research for the Computational Aerodynamics Group under the supervision of Professor David W. Zingg.

February 25, 2021

14:00 - 16:00 PM

Supersonic Turbulent Combustion: From a Detonation Engine to an Exploding Star

Alexei Poludnenko

Abstract:

Turbulent reacting flows are pervasive both in our daily lives on Earth and in the Universe. They power modern society being at the heart of many energy generation and propulsion systems, such as gas turbines, internal combustion and jet engines. At the same time, they also power the Universe through the energy produced in stellar interiors – both quiescently, as in the Sun, and also violently, as in the most powerful explosions in the Universe known as Type Ia supernovae. Despite this ubiquity in Nature, turbulent reacting flows still pose a number of fundamental questions concerning their structure and dynamics often exhibiting surprising and unexpected behavior. In recent years, the advent of large-scale direct numerical simulations (DNS) has allowed the detailed exploration of the reacting flow dynamics in extreme, previously inaccessible regimes characterized by high flow speeds, significant compressibility effects, and strong coupling between exothermic reactions and the turbulent flow. Such combustion regimes are fundamental to the operation of many modern propulsion applications from scramjets to detonation-based engines. Furthermore, in certain cases these regimes can now be studied with remarkable realism using full-scale systems, realistic fuels, and engine-relevant conditions. This talk will present an overview of a range of phenomena recently discovered in DNS of high-speed, premixed, turbulent reacting flows. These include intrinsic instabilities of reacting turbulence, onset of catastrophic transitions, e.g., spontaneous detonation formation, as well as the qualitative changes in the nature of the turbulent cascade in the presence of exothermic reactions. I will discuss challenges presented by these findings both in the context of our theoretical understanding of reacting flows, and also in the context of modern modeling paradigms, such as Large Eddy Simulations.

Combustion

Biography:
Alexei Poludnenko received his Bachelor's degree in Physics and Mathematics from the National University “Kyiv-Mohyla Academy” in Kyiv, Ukraine, and Master's and Ph.D. degrees in Physics and Astronomy from the University of Rochester. Upon graduation, he was a member of the Department of Energy ASC Flash Center at the University of Chicago as a postdoctoral researcher. Subsequently, Dr. Poludnenko worked at the Naval Research Laboratory first as a National Research Council postdoctoral fellow and later as a permanent research staff member. Prior to joining the UConn Department of Mechanical Engineering in the Fall of 2019, he had served as an associate professor on the faculty of the Department of Aerospace Engineering at the Texas A&M University, where he remains as an adjunct faculty member. His research includes theoretical and computational studies of complex multi-physics reacting and non-reacting flows, numerical algorithm development for computational fluid dynamics, and high-performance computing. Dr. Poludnenko was a recipient of the Distinguished Paper Awards at the 36th and 37th International Symposia on Combustion (2017 and 2019), the 2016 François Frenkiel Award for Fluid Mechanics of the American Physical Society Division of Fluid Dynamics, and two Alan Berman Research Publication Awards of the US Naval Research Laboratory.

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