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  • Dataset Containing Global Modeling Results Comparing Magnetopause Distances and CPCP

    Creator:
    Agnit Mukhopadhyay
    Description:
    Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
    Citation to related publication:
    Mukhopadhyay, A., et al. (2020). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Forthcoming.
    Discipline:
    Engineering and Science
    Title:
    Dataset Containing Global Modeling Results Comparing Magnetopause Distances and CPCP

  • Dataset for thesis "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction"

    Creator:
    Zhang, Kaihua and Collette, Matthew D.
    Description:
    This Ph.D. research focuses on two subject areas: experimental and numerical model, which serves as two essential parts of a digital twin. A digital twin contains models of real-world structures and fuses data from observations of the structures and scale experiment to pull the models into better agreement with the real world. Digital twin models have the promise of representing complex marine structures and providing enhanced lifecycle performance and risk forecasts. Experimentally verifying the updating approaches is necessary but rarely performed. Thus, the proposed work is designing an experiment and developing a numerical model updated by the experimental data. The dataset contains all the data collected in the experiment of a four-crack hexagon- shaped specimen is presented, designed to mimic many of the properties of complex degrading marine structural systems, such as crack interaction, component inter- dependence, redundant load path, and non-binary failure.
    Keyword:
    System Reliability, Dynamic Bayesian Networks, Fatigue Experiment, Crack Length Measurement, Experimental Validation, Reliability Prediction
    Citation to related publication:
    Kaihua, Zhang (2020) "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction" Doctoral Dissertation, University of Michigan. Deep Blue.  http://hdl.handle.net/2027.42/155231, "Evaluating Crack Growth Prediction in Structural Systems with Dynamic Bayesian Networks", submitted to Computers and Structure, and "Experimental Investigation of Structural System Capacity with Multiple Fatigue Cracks", submitted to Marine Structures
    Discipline:
    Engineering
    Title:
    Dataset for thesis "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction"

  • A Simple Method for Correcting Empirical Model Densities during Geomagnetic Storms Using Satellite Orbit Data

    Creator:
    Brandt, Daniel, A., Bussy-Virat, Charles, D., and Ridley, Aaron, J.
    Description:
    The Multifaceted Optimization Algorithm (MOA) is a tool for generating corrected empirical model thermospheric densities during geomagnetic storms. It consists of a suite of Python functions that operate around the Spacecraft Orbit Characterization Kit (SpOCK), an orbital propagator developed by Charles D. Bussy-Virat, PhD, Joel Getchius, and Aaron J. Ridley, PhD at the University of Michigan, and it estimates new densities for the NRLMSISE-00 atmospheric model. MOA generates new model densities by estimating modifications to inputs to the NLRMSISE-00 model that minimize the orbit error between modeled spacecraft in SpOCK, and their actual altitudes as described in publicly-available Two-Line Element Sets (TLEs), made available online via Space-track.org. MOA consists of three sub-process: (1) The Area Optimization Algorithm (AROPT), (2) the F10.7 Optimization Algorithm (FOPT), and (3) the Ap Optimization Algorithm (APOPT). AROPT computes the contribution to the drag of the modeled spacecraft due to their varying projected area. FOPT estimates modifications to the 10.7 cm solar radio flux in NRLMSISE-00, and APOPT estimates modifications to the Earth's magnetic activity in NRLMSISE-00. MOA finds these modifications across many spacecraft, and the medians of those modifications are then applied in NLRMSISE-00 along the orbit of another satellite to generate new densities for verification. In this instance, modifications are applied along the orbits of the Swarm spacecraft and compared to Swarm GPS-derived densities.
    Keyword:
    Orbit, Satellite, Two-line Element Set, Thermosphere, and Drag
    Discipline:
    Engineering
    Title:
    A Simple Method for Correcting Empirical Model Densities during Geomagnetic Storms Using Satellite Orbit Data

  • Data for "Comparison of Anorectal Function Measured using Wearable Digital Manometry and a High Resolution Manometry System." article (PLOS ONE) PONE-D-20-01826R1

    Creator:
    Attari, Ali
    Description:
    Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
    Keyword:
    Digital Manometry
    Citation to related publication:
    Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761.  https://doi.org/10.1371/journal.pone.0228761
    Discipline:
    Engineering, Science, and Health Sciences
    Title:
    Data for "Comparison of Anorectal Function Measured using Wearable Digital Manometry and a High Resolution Manometry System." article (PLOS ONE) PONE-D-20-01826R1

  • UofM pouch cell voltage and expansion dataset and modeling code

    Creator:
    Mohtat, Peyman, Siegel, Jason B., and Stefanopoulou, Anna G.
    Description:
    The goal here is to study the voltage and expansion response of lithium-ion batteries at different charging rates. Specifically, the goal is to capture the observation of the smoothing of the peaks in dV/dQ and retention of the peaks in d^2 \delta/dQ^2 at higher C-rates. The retention of the peaks at higher charging rates enables better estimation of the cell capacity. To achieve this goal a reduced order electrochemical and mechanical model with multiple particles with a size distribution is developed. This allows us to capture the smoothing and preservation of the phase transitions in the voltage and expansion measurements at high C-rates, respectively. The model is written in Matlab software.
    Keyword:
    Lithium-ion batteries, Modeling, Multiparticle, Mechanical response, and Electrochemical
    Citation to related publication:
     https://doi.org/10.1149/1945-7111/aba5d1
    Discipline:
    Engineering
    Title:
    UofM pouch cell voltage and expansion dataset and modeling code

  • Data pertaining to Article: Air Quality Impacts at an E-Waste Site in Ghana using Flexible, Low-Cost and Quality Assured Measurements

    Creator:
    Batterman, Stuart; University of Michigan
    Description:
    We evaluated PM levels at the Agbogbloshie e-waste and scrap yard site in Accra, Ghana, and at upwind and downwind locations. This monitoring forms part of the West Africa-Michigan Charter II for GEOHealth cohort study, which is analyzing occupational exposures and health risks at this site.
    Keyword:
    Air pollution, particulate matter, e-waste, Fires, and monitoring
    Discipline:
    Engineering and Health Sciences
    Title:
    Data pertaining to Article: Air Quality Impacts at an E-Waste Site in Ghana using Flexible, Low-Cost and Quality Assured Measurements

  • Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)

    User Collection
    Creator:
    Smith, Joeseph P., Fry, Lauren M., Do, Hong X., and Gronewold, Andrew D.
    Description:
    This collection contains estimates of the water balance of the Laurentian Great Lakes that were produced by the Large Lakes Statistical Water Balance Model (L2SWBM). Each data set has a different configuration and was used as the supplementary for a published peer-reviewed article (see "Citations to related material" section in the metadata of individual data sets). The key variables that were estimated by the L2SWBM are (1) over-lake precipitation, (2) over-lake evaporation, (3) lateral runoff, (4) connecting-channel outflows, (5) diversions, and (6) predictive changes in lake storage. and Contact: Andrew Gronewold Office: 4040 Dana Phone: (734) 764-6286 Email:  drewgron@umich.edu
    Keyword:
    Great Lakes water levels, statistical inference, water balance, data assimilation, Great Lakes, Laurentian, Machine learning, Bayesian, and Network
    Citation to related publication:
    Smith, J. P., & Gronewold, A. D. (2017). Development and analysis of a Bayesian water balance model for large lake systems. arXiv preprint arXiv:1710.10161., Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505., and Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (under revision)
    Discipline:
    Engineering and Science
    5Works

  • Self-Discovery Module (GUI) for Singular Vectors: The"Greatest Stretch" Method for 2x2 Matrices

    Creator:
    James, David A. and Lokam, Nikhil
    Description:
    The object of this project is to provide researchers and students with a tool to allow them to develop an intuitive understanding of singular vectors and singular values. 2x2 matrices A with real entries map circles to ellipses; in particular, unit circles centered at the origin to ellipses centered at the origin. It is known that the points on the ellipse farthest from the origin correspond to the singular vectors of A. Users can use the GUI to enter matrices of their choice and explore to visually self-determine the singular vectors/values.
    Keyword:
    SVD, Singular Value Decomposition, Singular Vector, Singular Value, and Matrix
    Discipline:
    Science and Engineering
    Title:
    Self-Discovery Module (GUI) for Singular Vectors: The"Greatest Stretch" Method for 2x2 Matrices

  • Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

    Creator:
    Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
    Description:
    We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
    Keyword:
    neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
    Citation to related publication:
    Swiger, B. M. et al. 2020. Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Science doi:10.3389/fspas.2020.00042
    Discipline:
    Science and Engineering
    Title:
    Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

  • Data Pertaining to Initial Simulations Using the Conductance Model for Extreme Events

    Creator:
    Mukhopadhyay, Agnit, Daniel T Welling, Michael W Liemohn, Aaron J Ridley, Shibaji Chakrabarty, and Brian J Anderson
    Description:
    An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events., Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events., and Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly.
    Keyword:
    Space Weather Forecasting, Extreme Weather, Ionosphere, Magnetosphere, MI Coupling, Ionospheric Conductance, Auroral Conductance, Aurora, SWMF, SWPC, Nonlinear Regression, and dB/dt
    Citation to related publication:
    Mukhopadhyay, A., et al. (2020). Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts. Forthcoming.
    Discipline:
    Science and Engineering
    Title:
    Data Pertaining to Initial Simulations Using the Conductance Model for Extreme Events