Search Constraints
« Previous |
171 - 175 of 175
|
Next »
Number of results to display per page
View results as:
Search Results
-
- Creator:
- Klinich, Kathleen D, Hu, Jingwen, Boyle, Kyle J, Manary, Miriam A., and Orton, Nichole R
- Description:
- As part of a project to develop side impact test procedures for evaluating wheelchairs, wheelchair tiedowns and occupant restraint systems (WTORS), and vehicle-based occupant protection systems for wheelchair seating stations, we created validated finite element (FE) models to support procedure development. Models were constructed using LS-DYNA. Dynamic sled tests were performed to validate the FE models of surrogate fixtures and commercial hardware. Validated FE models were developed for the Surrogate wheelchair base (SWCB), Surrogate wheelchair for side impact (SWCSI), a manual wheelchair (Ki Mobility Catalyst 5), and a power wheelchair (Quantum Rehab Edge 2.0). Additional FE models of a heavy-duty anchor meeting the Universal Docking Interface Geometry (UDIG), surrogate four-point strap tiedowns (SWTORS), a traditional docking station, and the surrogate wall fixture were also developed.
- Keyword:
- finite element, wheelchair, transportation, and tiedown
- Discipline:
- Engineering
-
- Creator:
- Agnit Mukhopadhyay, Sanja Panovska, Raven Garvey, Michael Liemohn, Natalia Ganjushkina, Austin Brenner, Ilya Usoskin, Michael Balikhin, and Daniel Welling
- Description:
- In the recent geological past, Earth’s magnetic field reduced to 4% of the modern values and the magnetic poles moved severely apart from the geographic poles causing the Laschamps geomagnetic excursion, which happened about 41 millennia ago. The excursion lasted for about two millennia, with the peak strength reduction and dipole tilting lasting for a shorter period of 300 years. During this period, the geomagnetic field exhibited significant differences from the modern nearly-aligned dipolar field, causing non-dipole variables to mimic a magnetic field akin to the outer planets while displaying a significantly reduced magnetic strength. However, the precise magnetospheric configuration and their electrodynamic coupling with the atmosphere have remained critically understudied. This dataset contains the first space plasma investigation of the exact geomagnetic conditions in the near-Earth space environment during the excursion. The study contains a full 3D reconstruction and analysis of the geospace system including the intrinsic geomagnetic field, magnetospheric system and the upper atmosphere, linked in sequence using feedback channels for distinct temporal epochs. The reconstruction was conducted using the LSMOD.2 model, Block Adaptive Tree Solar wind-Roe-Upwind Scheme (BATS-R-US) Model and the MAGnetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model, all of which are publicly-available models. The dataset contains the raw data from each of these models, in addition to the images/post-processing results generated using these models. Paleomagnetic data produced by LSMOD.2 can be visualized using a combination of linear plotting and contour plotting tools available commonly in visualization software like Python (e.g. Python/Matplotlib) or MATLAB. Standard tools to read and visualize BATS-R-US and MAGNIT output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html). For information and details about the post-processed data, visualization and analysis, please contact the authors for details. The anthropological dataset can be visualized using a shape file reader (e.g. Python/GeoPandas) and a linear plotting tool (e.g. Python/Matplotlib).
- Discipline:
- Engineering and Science
-
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Dwyer, Tobias, Moore, Timothy C., Anderson, Joshua A. , and Glotzer, Sharon C.
- Description:
- This dataset was generated for our work: "Tunable Assembly of Host–Guest Colloidal Crystals". The data set contains data for 5 different binary systems of star particles and convex guests, and one system of only star particles. All simulation were formed at constant pressure. The data set contains GSD files for each of the simulations used in this work along with the corresponding python code used to produce the simulations. We also include the python code and jupyter notebook to produce the free volume calculations used in this work. and How to use this Data: Simulation Data: We include GSD files that can be uploaded into a visualization or analysis software such as Ovito or Freud for independent analysis. Simulation python scripts (workspaces_for_HPMC_simulations.zip): We include the python scripts used in this work for simulating host guest systems at constant pressure. Free Volume Data (Free_volume_calculations_and_analysis.zip): You can run the jupyter notebook included here to reproduce the free volume analysis for this work. We also include the python scripts for the free volume calculation python scripts that get the data for these free volume calculations.
- Citation to related publication:
- Dwyer, T, Moore, TC, Anderson, JA, & Glotzer, SC. Tunable Assembly of Host–Guest Colloidal Crystals. Soft Matter (Provisional Citation)
- Discipline:
- Engineering
-
- Creator:
- Shah, Bhavarth
- Description:
- The three approaches used three distinct datasets named as follows: Historicalwater_levels.csv, Historical_Precipitation.csv, and Bayesian Statistical dataset.csv. These files are accessible using Microsoft Office or similar software. The machine learning models are developed in Jupyter Notebook (.ipynb) files, named according to the datasets they utilize. However, for the third approach, the models are named Random Forest, LSTM Model Base, and Multivariate LSTM Models. More details are available on the Shah_Bhavarth_Readme.txt. These notebooks can be accessed through Python, Project Jupyter, or Google Colab, and dependencies include libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, Keras, and TensorFlow. The supplementary material also includes Excel files for stage-curve calculations and diversions, named Water_levels_Stage_Curve_Calculations1970-2018.xlsx and Diversions_calculation.xlsx, respectively.
- Keyword:
- Machine learning, Forecasting, Water levels, Mono lake, and Hydrology
- Citation to related publication:
- Shah, Bhavarth. 2024. "Mono Lake Water Levels Forecasting Using Machine Learning." Master’s thesis, University of Michigan, School for Environment and Sustainability. ORCID iD: 0000-0002-2391-8610. https://dx.doi.org/10.7302/22659
- Discipline:
- Science and Engineering