Alanah Cardenas-O’Toole

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Year
2021-2022

Research Description
Scientific computing techniques to understand the solar atmosphere dynamics and its impact on the Earth’s ionosphere. She will utilize both advanced data analysis methods and numerical models in her research.

Mentor
Shasha Zou, Climate and Space Sciences and Engineering

Yifu An

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Year
2021-2022

Research Description
Modeling space physics and predicting space weather with a combination of first-principles models, machine learning and data assimilation.

Mentor
Gabor Toth, Climate and Space Sciences and Engineering

Portrait of Jeremy Bricker

Jeremy Bricker

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Jeremy Bricker is an Associate Professor in the department of Civil and Environmental Engineering. His research is focused on hydraulic engineering to investigate the resilience of structures and infrastructure exposed to both increasing hazard due to climate change and increasing consequences due to expansion of development in coastal and flood-prone areas.

Computational methods are useful in hydraulic engineering for assessing the safety of coastal and hydraulic structures, estimating the flood risk experienced by communities, and predicting damage to buildings during floods, hurricanes, and tsunamis. At a large scale of hundreds to thousands of kilometers, shallow water equation models simulate tsunami propagation, storm surge and wave generation, and river flood occurrence. At scales of kilometers to tens of kilometers, these models resolve overland inundation due to flood events, allowing empirical or analytical estimates of forces on structures and damage to buildings and infrastructure. At a small scale of tens to hundreds of meters, computational fluid dynamics (CFD) directly calculates pressures and forces on submerged and emergent structures from floodwaters and waves. This can be linked with a dynamic response model to assess whether resonance could lead to structural failure, or linked with a Finite Element Method (FEM) model to assess stresses within the structure. Such modeling is useful for forensic analysis of the failure of bridges, buildings, and other infrastructure after floods, as well as for planning and design of new structures.

 

Streamlines around the cross-section of a 3-girder bridge deck submerged by a river flood, from Oudenbroek et al. (2018).

 

 

Hugo Casquero

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Hugo Casquero is an Assistant Professor in the Mechanical Engineering Department at University of Michigan – Dearborn. His research is focused on developing accurate, robust, and efficient computational methods and using them to solve a myriad of open problems in fluid mechanics, solid mechanics, fluid-structure interaction, biomechanics, and multiphysics. The overarching theme of the computational methods that Dr. Casquero develops is to solve partial differential equations exploiting the new advantages that splines bring to computational mechanics. Dr. Casquero is particularly interested in developing computational frameworks for real-world applications in which experimental measurement of the quantities of interest is too costly or not currently available. Current research activities in his group include achieving a seamless integration between design and analysis of thin-walled structures, studying the dynamics of vesicles, capsules, red blood cells, and droplets under different types of flow, and developing structure-preserving spline discretizations of magnetohydrodynamics to solve problems in fusion energy.

animation of a crash simulation plotting von Mises stress

Crash simulation plotting von Mises stress. A discretization of Kirchhoff-Love shells based on analysis-suitable T-splines is used. This simulation includes elastoplastic material behavior, fracture criteria, contact algorithms, and spot-weld modeling. Material failure takes place around the largest hole of the B-pillar.  

Yin Lu (Julie) Young

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Yin Lu (Julie) Young is a Professor in the department of Naval Architecture and Marine Engineering. Her research focuses on the dynamic fluid-structure interaction response and stability of smart/adaptive multi-functional marine structures such as marine propulsors, turbines and control surfaces. One of her research focus is the fluid-structure interaction response and stability of marine and coastal structures. She is the current director of the Aaron Friedman Marine Hydrodynamics Laboratory. Her research has been supported by the Office of Naval Research (ONR), the Naval Surface Warfare Center (NSWC), and the National Science Foundation (NSF).

Shasha Zou

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Shasha Zou is an Associate Professor of Climate and Space Science and Engineering. Her general research interest is about studying the dynamic interaction between the Sun’s extended atmosphere, i.e., solar wind, and the near-Earth space environment. In particular, she is interested in the physical processes of formation and evolution of ionospheric structures and their impact on technology, such as global navigation and communication satellite system (GNSS), during space weather disturbances using multi-instrument observations and numerical models. Numerical models often used include magnetohydrodynamic (MHD) model of the global magnetosphere, and physics-based global ionosphere and thermosphere model.

 

Global ionosphere total electron content distribution and the plasma convection contours from BATSRUS model.

Camille Avestruz

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Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.

[Click on image to see video] Image projection of various components and properties of a simulated galaxy cluster in its last 8 gigayears of formation. The top left panel shows the underlying dark matter content, the top middle image shows the distribution of stars, and the remaining four panels are properties of the gas content: density, temperature, entropy, and metallicity. To model the evolution of galaxy clusters in a cosmological volume, the simulation uses adaptive refinement in space and time in order to span the relevant dynamic range of the system.