Computing In Engineering Forum 2021 Poster Session Links

 

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Virtual Event
September 21 – 23, 2021
Madison, WI

Attendees:

The poster presentation will be September 21st from 3-5pm. Please use the webpage linked in your email confirmation of registration to access the presentations via zoom.

Poster Presenters:

You will receive instructions for your presentation via email. Contact us with any questions.

Poster Presentations

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1. A Systems-level Analysis of Ethanol Upgrading Biorefineries

By: Juan-Manuel Restrepo-Flórez, Joonaje Ryu, David Rhotamer, and Christos T. Maravelias

https://uwmadison.zoom.us/j/8955671476

We present a superstructure-based framework to study the upgrading of ethanol toward advanced fuels (gasoline, diesel, jet fuel) with tailored properties.

2. Adaptive Mesh Augmented Lagrangian Digital Image/Volume Correlation

By: Jin Yang,  Kaushik Bhattacharya, and Christian Franck

https://uwmadison.zoom.us/j/98355159890

Digital image/volume correlation (DIC/DVC) is a widely used experimental method to measure full-eld
displacements and strains. This technique compares images of speckle patterns before and after
deformation to computationally infer the displacement and strain fields.

3. Developing Magnetic Nanotransducer Technologies for High-Resolution Visualization of Neural Activity

By: Ilhan Bok, Thor Larson, Ido Haber, Xiaofei Qu, and Aviad Hai

https://uwmadison.zoom.us/j/4498892748

We aim to develop a nanotechnology solution regarding the present lack of a high-resolution functional neuroimaging technology.

4. Fast Estimation of Ion-pairing for Screening Battery Electrolytes

By: Ajay Muralidharan & Arun Yethiraj

https://uwmadison.zoom.us/j/95487632826
Meeting ID: 954 8763 2826

My current research develops robust and fast methods to predict molecular properties for computational screening of battery electrolytes. On this poster, I describe our new simulation tool (ClusterMT) that estimates ion-pairing metrics efficiently and shows great potential for use in material screening pipelines.

5. Gene Network Analysis Identifies Functionally Enriched Pathways in Developing Mammalian Aerodigestive Tract

 

By: Kristy Wendt, Jared Brown, Vidisha Mohad, Madhu Gowda, Ziyue Wang, Jeea Choi, Christina Kendziorski,  Vlasta Lungova, Xin Sun,  and Susan Thibeault

 

6. GPU-Accelerated OpenMM Plugin Enables Anisotropic Benzene Model

By: Tesia D. Janicki, Mary J. Van Vleet, JR Schmidt

https://uwmadison.zoom.us/j/96420060724

Many interatomic potentials assume spherical symmetry which overlooks relevant interactions imparted by local geometry. Here, we summarize the development of an anisotropic interatomic potential for benzene and software development to support anisotropic expressions in the GPU-accelerated OpenMM simulation package.

7. Integer Programming Approaches to Subspaces Clustering

By: Akhilesh Soni, Jeff Linderoth, Jim Luedtke, Daniel L. Pimentel-Alarcón

https://uwmadison.zoom.us/j/96407332851

The goal of Subspaces Clustering problem is to cluster the vectors of the data matrix as per their subspace membership. We propose a novel integer programming-based method which is based on dynamically determining a set of candidate subspaces and optimally assigning points to selected subspaces.

8. Machine Learning for Surfactants

By: Shiyi Qin, Fengrui Wang, Tianyi Jin, David M. Lynn, Reid C. Van Lehn, and Victor M. Zavala

https://uwmadison.zoom.us/j/91662928045

Surfactants are amphiphiles that are commonly seen in cleaning products and biological processes. We showcase two studies on property predictions for surfactants using different data representations with derivatives of neural network architectures, including convolutional neural networks and graph neural networks.

9. Machine Learning-Based Sensitivity of Steel Frames with High-Dimensional and Highly Imbalanced Data

By: Hyeyoung Koh and Hannah Blum

https://uwmadison.zoom.us/j/91841069399

The machine learning-based feature selection approach is presented to estimate the effect of uncertainties and identify failure modes of steel frames that incorporate low failure probability and high-dimensional uncertainties.

10. Medical Image Translation Using Deep Convolutional Neural Networks

By: Ashwin K. Avula

https://uwmadison.zoom.us/j/96674014071?pwd=STJzNzlVNG5XWFc4S2wwYzRBNlZhdz09

Nowadays, radiotherapy is the primary treatment procedure for glioma patients; while this method has proven to be successful, it relies on the information obtained from several different MRI sequences. Rather than acquiring several scans of the patient, this study aims to create a low-cost Convolutional Neural Network to generate T-2 weighted MRIs from T1-weighted scans; in doing this, hospitals can create a more streamlined and cost-effective treatment process.

11. Open-Source High-Performance Computing for Applications in Engineering: DEM, SPH and Multi-Agent Vehicle Simulations with Project Chrono

By: Ruochun Zhang, Wei Hu, Simone Benatti, Luning Fang, Jason Zhou, Lijing Yang, Asher Elmquist, Jay Taves, Aaron Young, Colin Vanden Heuvel, Radu Serban, Dan Negrut

https://uwmadison.zoom.us/j/98140674401

This poster reports the recent HPC-related research progress from SBEL, UW-Madison, which includes large-scale DEM and SPH simulations for rover mobility and multi-agent autonomous vehicle simulations. They are based on GPU, FSI and SynChrono modules of Project Chrono.

12. Optimization Strategies for Plastic Waste Management

By: Dilara Goreke & Victor M. Zavala

https://uwmadison.zoom.us/j/93147051665

In this work, we present a supply chain optimization framework to assess the inherent values of products and services for the plastic waste management problem in a coordinated market setting. We include a case study of the recycling of PET bottles in the State of Wisconsin.

13. Scalable Solution of Graph-Based Models in Julia

By: David Cole and Victor Zavala

https://uwmadison.zoom.us/j/95842843017

Graph-based modeling of optimization problems allows for visualization, partitioning, and decomposition of models. We present a Julia based set of packages, Plasmo.jl and MadNLP.jl, that allow users to model and solve graph-based optimization problems.

14. Simulation of 2D Airfoils with Dynamic Mesh Morphing

By: Riley Bridges & Jennifer A. Franck

https://uwmadison.zoom.us/j/96118598652

Flow simulations involving airfoils can have large parameter spaces that are time-intensive to iterate over because of manual re-meshing. The current research provides a new algorithm to dynamically update mesh geometry during a simulation, reducing computational expense.

15. Structure, Stability, Electronic Properties, and Quantum Capacitance of Pristine and Defective Mo2CT2 (T = Cl, F, O, OH) MXene

By: Kyle Sprecker, Soledad Farber, Siby Thomas, and Mohsen Asle Zaeem

https://uwmadison.zoom.us/j/5056384481

First-principals density functional theory calculations were performed on the pristine and defective Mo2CT2 (T = Cl, F, O, OH) MXene in order to determine the viability of Mo2C for use in supercapacitors. Of the materials examined, the Mo2CCl2 MXene is the best candidate due to its large quantum capacitance, conductive nature, and elastic stability, as shown by the density functional theory calculations.

16. Simulation of Selective Nitrogen Recovery in Membrane Electrochemical Systems

By: Kai Yang and Mohan Qin

https://uwmadison.zoom.us/j/91505010938

Membrane electrochemical system (MES) is a promising approach to treat wastewater and recover nitrogen simultaneously. This study simulates the ion movement in an MES reactor and investigates the effects of three parameters, assisting further optimization of MES reactors and other electrochemical cells.

17. Supply-Chain Optimization for Thermochemical-based Plastic Waste Upcycling Infrastructure in upper Midwest Region

By: Jiaze Ma, Philip Tominac, Victor M. Zavala, Mark Mba-Wright and Craig H. Benson

https://uwmadison.zoom.us/j/6187143085

3.5 million tons of post-consumer plastic wastes are generated in the upper Midwest regions every year. Value-added chemicals can be recovered from plastic wastes through thermochemical pathways. We proposed a computational model that optimizes the supply chain for plastic waste recycling infrastructure.

18. Sensitivity of the Shear Wave Speed-Stress Relationship to Soft Tissue Fiber Alignment

By: Jonathon L. Blank, Darryl G. Thelen, Matthew S. Allen, and Joshua D. Roth

https://uwmadison.zoom.us/j/92721583257

Shear wave speeds track axial stress in tendons and ligaments. We have shown that shear wave speeds and the relationship between shear wave speed and axial stress in a soft tissue finite element model is sensitive to its embedded fiber alignment.

19. Topological Learning and its Application to Multimodal Brain Network Integration

By: Tananun Songdechakraiwut, Li Shen, and Moo Chung

https://uwmadison.zoom.us/j/99529387530

A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. This poster presents a novel topological learning framework that integrates networks of different topology through persistent homology and demonstrates its versatility in the twin imaging study where we determine the extent to which brain networks are genetically heritable.

20. Voting-by-Mail Security Analysis

By: Shaonan Wang, Carmen Haseltine, Professor Laura Albert

https://uwmadison.zoom.us/j/5270932142

Meeting ID: 527 093 2142

The 2020 General Election saw a substantial increase in Vote-by-Mail/absentee (VBM) voting, which revealed the vulnerabilities of VBM processes to various attacks. We identify the countermeasures specific to these attacks and apply an attack-defense tree model for a detailed evaluation of VBM process secure risks.

Contact us at gie@engr.wisc.edu with any questions.

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