AEM Seminar: Friday, November 1st - Prof Raul Radovitzky, Dept of Aeronautics and Astronautics, Institute for Soldier Nanotechnologies, MIT

[Molly Schmitz]

University of Minnesota

Aerospace Engineering and Mechanics
Fall 2024 Seminar Series

Friday, November 1, 2024

43 Rapson Hall

2:30pm-4:30pm

AEM Seminar: 

A unified framework for large-scale simulation of the thermo-chemo-mechanical response of thermal protection systems in hypersonic environments

Abstract: 

I will present a thermo-chemo-mechanics computational framework for the analysis of material and  structural degradation and failure resulting from extreme conditions encountered in hypersonic flight. The approach is based on a unified discontinuous-Galerkin finite-element formulation of the coupled equations describing the solid mechanics, heat transfer, mass transport, and chemical reaction problem. The resulting computational framework supports general models of thermo-chemical reactive transport (convection, diffusion, oxidation, pyrolysis), thermo-chemically-induced mechanical stresses and material fracture, and thermal and chemical diffusion resistance across crack surfaces, as well as surface ablation. We demonstrate the versatility of the computational framework with simulations of: 1) pyrolysis and ablation of Phenolic-Impregnated Carbon Ablator (PICA) material, resulting in thermo-chemically induced mechanical stresses and surface recession, 2) thermally-induced oxidation, growth, and swelling stresses leading to fracture of silicon carbide.

Bio: 

Raul Radovitzky is a Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He also serves as the Associate Director of the MIT Institute for Soldier Nanotechnologies. He received a Civil Engineer degree from the University of Buenos Aires in 1991, A S. M. in Applied Mathematics from Brown University in 1995 and a Ph D in Aeronautical Engineering from the California Institute of Technology in 1998. His research interests are in the development of computational methods for multi-scale modeling of complex material response as well as in the formulation and implementation of algorithms for large-scale simulation of the dynamic response of materials  subject to hypersonic flight environments. His main emphasis is on the analysis of material and structural failure. His group has pioneered the development of massively-scalable algorithms for the simulation of dynamic fracture. The methods his group has developed have also led to significant advances in our understanding of the physical effects of blast waves on structures and on the human brain. This has helped to develop strategies to protect against Traumatic Brain Injury. As part of his devotion for education and student life, he and his wife Flavia have been the Heads of House at McCormick Hall, the only all-women dormitory at MIT, since 2015, where they have contributed to building a thriving community of young scholars. As a recognition of his dedication to students, he has received the following awards: The 2021 Arthur C. Smith Award, the 2014 Student Champion (Freshman Advising) Award, the
2016 AIAA Aeronautics and Astronautics Teaching Award, the 2018 Alan J. Lazarus (1953) Excellence in Advising Award, the 2021 AIAA Aeronautics and Astronautics Best Professor Award, and the 2021 Arthur C. Smith Award for meaningful contributions and devotion to undergraduate student life and learning at MIT. Dr. Radovitzky is an Associate Fellow of the American Institute of Aeronautics and Astronautics and a member of the National Football League Engineering Committee.

*Refreshments to follow in 227 Akerman Hall 

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Molly Schmitz (She/Her/Hers)

Graduate Program Coordinator & Executive Accounts Specialist

Department of Aerospace Engineering & Mechanics

University of Minnesota - Twin Cities

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[Molly Schmitz]

University of Minnesota

Aerospace Engineering and Mechanics
Fall 2024 Seminar Series

Friday, November 8, 2024

43 Rapson Hall

2:30pm-4:30pm

AEM Seminar: 

Honeywell Alternate Navigation Research Overview

Abstract: 

We will discuss the current methods of GPS-denied (or Alternative) navigation currently being developed by Honeywell.  These include celestial navigation, vision aided navigation. Magnetic anomaly navigation, and LEO RF navigation.

Bio: 

Brian is currently a research fellow at Honeywell has 37 years of experience with navigation system research and development.  After focusing on GNSS technology, Brian has spent the last 8 years working on forms of navigation that could be used when GNSS is not available.

[Molly Schmitz]

University of Minnesota

Aerospace Engineering and Mechanics
Spring 2025 Seminar Series

Friday, January 31, 2025

209 Akerman Hall

2:30pm-4:30pm

AEM Seminar: 

Differentiable Computational Mechanics: Neural-Integrated and Data-Driven Modeling for Inelastic Solids and Geophysical Applications

Abstract:
We present a recent development in the hybrid computational framework that integrates physics-based numerical schemes with machine learning methods to address various forward and inverse problems in computational mechanics. Our focus is on applications involving complex material behaviors and coupling effects, exploring how physical laws can be effectively incorporated within these methods across varying levels of data availability. We introduce a variationally consistent physics-informed machine learning approach, termed the Neural-Integrated Meshfree (NIM) method, designed to improve accuracy and training efficiency for simulating large deformations and material nonlinearities. To this end, the NIM method employs a hybrid approximation strategy that combines neural network representations with customized basis functions. 

The effectiveness of the NIM method is demonstrated through a series of linear and nonlinear benchmark mechanics problems, including applications in identifying heterogeneous biological materials. We also extend this framework to model Lagrangian particle flow problems, showcasing its potential to handle complex material behaviors under extreme conditions. Additionally, in data-rich scenarios, we introduce a hybrid scheme that leverages data-driven
learning models for solving coupled systems. Our results show that the proposed machine learning models can reliably learn operators to capture underlying physical processes, enabling efficient dimensionality reduction. Examples from geophysics and biology will be presented to highlight the versatility of these machine learning techniques in advancing scientific computing.

Bio:
Dr. Qizhi (“KaiChi") He is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota (UMN). He received his M.A. in Applied Mathematics (2016) and Ph.D. in Structural Engineering and Computational Science (2018) from the University of California, San Diego. From 2019 to 2021, he worked as a postdoctoral research associate in Scientific Machine Learning Group at Pacific Northwest National Laboratory. His research focuses on developing advanced numerical methods and physics-integrated machine learning algorithms to predict complex mechanics in porous and composite material systems under extreme conditions, as well as advancing inverse modeling and data assimilation for large-scale multi-physics applications in solid mechanics, material design, and geophysics. Dr. He is a member of the ASCE/EMI technical committees on Computational
Mechanics and Machine Learning in Mechanics and serves on the editorial board of Computers and Geotechnics.