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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:
- Science and Engineering
5Works -
- Creator:
- Huang, Cheng MI
- Description:
- This collection contains a hierarchy of test problems for turbulent reacting flow simulations. It is meant to provide a testbed to build reduced model for relevant challenging reacting flow problems using different methods. In addition, this collection also serves to engage a broad community of experts in computational science and the field of engineering to address certain challenges in constructing reduced models for reacting flow simulations. All the datasets in this collection were generated under the Air Force Center of Excellence on Multi-Fidelity Modeling of Rocket Combustion Dynamics and the goal of the center is to advance the state-of-the-art in Reduced Order Models (ROMs) and enable efficient and accurate prediction of instabilities in liquid fueled rocket combustion systems.
- Discipline:
- Engineering
2Works -
- Creator:
- Figueroa, Carlos A., Computational Vascular Biomechanics Lab, University of Michigan, and et al.
- Description:
- This collection concerns the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. , The minimum specifications to run CRIMSON are: Any AMD64 CPU (note: Intel Core i series are AMD64), Windows (only tested on Windows 10 but might work on Windows 7), 8 GB of RAM , If you are running non-trivial models you will want to have: Quad core CPU or higher, Solid state drive for storing data, Windows, 16 GB of RAM, Dedicated discrete GPU for rendering models. , and Software in this collection is a snapshot; please visit https://github.com/carthurs/CRIMSONGUI & www.crimson.software for more general information and the most up to date version of the software.
- Keyword:
- Blood Flow Simulation, Patient-specific, Open-source Software, Image-based simulation, Cardiovascular Medical Image, Segmentation, and Finite Element Simulation
- Citation to related publication:
- CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation C.J. Arthurs, R. Khlebnikov, A. Melville, et al. bioRxiv 2020.10.14.339960; doi: https://doi.org/10.1101/2020.10.14.339960
- Discipline:
- Health Sciences and Engineering
4Works -
- Creator:
- Towne, Aaron
- Description:
- This database contains six datasets intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The six datasets are: large-eddy-simulation data for a turbulent jet, direct-numerical-simulation data for a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry data for the same boundary layer, direct-numerical-simulation data for laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force data for an airfoil encountering a gust, and large-eddy-simulation data for the separated, turbulent flow over an airfoil. All data are stored within hdf5 files, and each dataset additionally contains a README file and a Matlab script showing how the data can be read and manipulated. Since all datafiles use the hdf5 format, they can alternatively be read within virtually any other programing environment. An example.zip file included for each dataset provides an entry point for users. The database is an initiative of the AIAA Discussion Group on Reduced-Complexity Modeling and is detailed in the paper listed below. For each dataset, the paper introduces the flow setup and computational or experimental methods, describes the available data, and provide an example of how these data can be used for reduced-complexity modeling. All users should cite this paper as well as appropriate primary sources contained therein. Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Keyword:
- fluid dynamics, reduced-complexity models, and data-driven models
- Discipline:
- Engineering and Science
6Works -
Novel Sensors for Autonomous Vehicle Perception
User Collection
- Creator:
- Skinner, Katherine A, Vasudevan, Ram, Ramanagopal, Manikandasriram S, Ravi, Radhika, Buchan, Austin D, and Carmichael, Spencer
- Description:
- The Novel Sensors for Autonomous Vehicle Perception Collection of datasets are sequences collected with an autonomous vehicle platform including data from novel sensors. The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. Sequences include ~8 km routes, driven repeatedly under varying lighting conditions and/or opposing viewpoints. Further information and resources are available on the project website: https://umautobots.github.io/nsavp
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://umautobots.github.io/nsavp, https://github.com/umautobots/nsavp_tools, and https://sites.google.com/umich.edu/novelsensors2023
- Discipline:
- Engineering
12Works -
Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection
- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works -
SHIFDR: Sub-metered HVAC Implemented For Demand Response
User Collection
- Creator:
- Lin, Austin and Mathieu, Johanna
- Description:
- The Sub-metered HVAC Implemented for Demand Response (SHIFDR) dataset is a massive dataset that captures the response of individual commercial building HVAC system components to demand response events. The dataset includes device-level power consumption during demand response events as well as during normal operation. We have organized the data into subsets, with each subset containing data from buildings in different parts of the world. Kindly refer to the README file within each subsection for specific information about how data is organized. Please reach out if you have data that you would like to share, find any mistakes in the data, or have any questions. We are always trying to improve SHIFDR.
- Discipline:
- Engineering
1Works -
- Creator:
- Sun, Hu
- Description:
- Complete Global Total Electron Content Database based on the VISTA Algorithm
- Keyword:
- Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
- Discipline:
- Engineering
2Works