UTIAS Seminar Series

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

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 degree in Physics and Mathematics from the National University “Kyiv-Mohyla Academy” in Kyiv, Ukraine, and Masters 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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