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- Creator:
- Troesch, Armin, W. and Kang, C.-G.
- Description:
- This scaled acceleration time series has been used in the graduate class, NA540, as an example of hydrodynamic impact. For a more detailed description of the tests, please see: Troesch, A.W. and Kang, C.-G., "Hydrodynamic Impact Loads on Three Dimensional Bodies," Proceedings of the 16th Symposium on Naval Hydrodynamics, Berkeley, July 1986, National Academy Press, Washington, D.C., 1987, pp. 537-558.
- Keyword:
- hydrodynamic impact
- Citation to related publication:
- Troesch, A.W. and Kang, C.-G., "Hydrodynamic Impact Loads on Three Dimensional Bodies," Proceedings of the 16th Symposium on Naval Hydrodynamics, Berkeley, July 1986, National Academy Press, Washington, D.C., 1987, pp. 537-558. This item is not available online due to copyright restrictions, but the text can be searched using Hathi Trust: https://babel.hathitrust.org/cgi/pt?id=mdp.39015040312475
- Discipline:
- Engineering and Science
-
- Creator:
- Ponder, Brandon M., Ridley, Aaron J., Goel, Ankit, and Bernstein, Dennis S.
- Description:
- This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
- Keyword:
- Thermosphere, GITM, CHAMP, GRACE, MSIS, Upper Atmosphere Modeling, and Data Assimilation
- Citation to related publication:
- Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
- Discipline:
- Engineering and Science
-
- Creator:
- Minallah, Samar and Steiner, Allison L.
- Description:
- Data format: netcdf4 , Time series duration: 2016-06-01 to 2020-10-31, Temporal resolution: Daily, and Spatial resolution: The model output was regridded to a 0.05 degree rectilinear (lat/lon) grid using the conservative remapping method ("cdo remapcon" tool).
- Keyword:
- Land surface hydrology, Great Lakes, Land surface model, NOAH-MP, WRF-Hydro, and Hydrologic modeling
- Citation to related publication:
- Minallah, S. (2022). A Study on the Atmospheric, Cryospheric, and Hydrologic Processes Governing the Evolution of Regional Hydroclimates (Doctoral dissertation, University of Michigan Ann Arbor). https://dx.doi.org/10.7302/6223
- Discipline:
- Science and Engineering
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/ , and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- Creator:
- Towne, Aaron, Jones, Anya, and Biler, Hulya
- Description:
- This dataset contains experimental measurements of a flat-plate airfoil passing through a large-amplitude transverse gust. The dataset contains an ensemble of of the airfoil-gust encounter to account for variability in the gust profile, and each realization contains time-resolved force measurements and planar PIV velocity fields. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘airfoilEXP_README.pdf’ file for more information. We recommend using the ‘airfoilEXP_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics and aerodynamics
- Citation to related publication:
- 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., Biler, H., Sedky, G., Jones, A. R., Saritas, M. and Cetiner, O. (2021) Experimental investigation of transverse and vortex gust encounters at low Reynolds numbers. AIAA Journal, 59(3):786–799., and Andreu-Angulo, I., Babinsky, H., Biler, H., Sedky, G. and Jones, A. R. (2020) Effect of transverse gust velocity profiles. AIAA Journal, 58(12):5123–5133.
- Discipline:
- Science and Engineering
-
- Creator:
- Towne, Aaron and Dawson, Scott
- Description:
- This dataset contains data from direct numerical simulations of two-dimensional stationary and pitching flat-plate airfoils at Reynolds number 100. The dataset contains time-resolved snapshots of the velocity field, lift and drag coefficients, and airfoil kinematics spanning 40-100 convective time units. Cases include a stationary airfoil and eight different pitching frequencies. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘airfoilDNS_README.pdf’ file for more information. We recommend using the ‘airfoilDNS_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics and aerodynamics
- Citation to related publication:
- 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. and Dawson, S. T. M., Floryan, D. C., Rowley, C. W., and Hemati, M. S. (2016) Lift enhancement of high angle of attack airfoils using periodic pitching. AIAA Paper 2016-2069.
- Discipline:
- Engineering and Science
-
- Creator:
- Bougher, S. W. (CLaSP Department, University of Michigan)
- Description:
- The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking systematic measurements of the densities and deriving temperatures in the upper atmosphere of Mars between about 140 to 240 km above the surface since late 2014. Wind measurement campaigns are also conducted once per month for 5-10 orbits. These densities, temperatures and winds change with time (e.g. solar cycle, season, local time) and location, and sometimes fluctuate quickly. Global dust storm events are also known to significantly impact these density, temperature and wind fields in the Mars thermosphere. For the current project, the inert light species helium is used to trace the circulation patterns and constrain wind magnitudes throughout the Mars thermosphere. Presently, more than 6 years of Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements of helium densities have been obtained by the MAVEN team (e.g. Elrod et al., 2017; 2021; Gupta et al., 2021). Measured helium distributions are compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. Since the global circulation plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere of Mars also provides the needed context for understanding helium distributions and how the atmosphere behaves as a whole system. Three dimensional M-GITM simulations for the Mars four cardinal seasons (Ls = 0, 90, 180, 270, for Mars Year 33) were conducted for detailed comparisons with NGIMS helium and CO2 distributions (Gupta et al. 2021). The M-GITM datacubes used to extract these densities (plus winds) along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. README files are also provided for each datacube, detailing the contents of each file. In addition, a general README file is provided that summarizes the inputs and outputs of the M-GITM code simulations for this study. Finally, a basic version of the M-GITM code can be found on Github at https:/github.com/dpawlows/MGITM.
- Keyword:
- Mars, MAVEN Spacecraft Mission, Mars Thermosphere, Helium Density Distributions, and Neutral Gas and Ion Mass Spectrometer (NGIMS)
- Citation to related publication:
- Gupta, N., N. V. Rao, S. W. Bougher, and M. K. Elrod, Latitudinal and Seasonal Asymmetries of the Helium Bulge in the Martian Upper Atmosphere J. Geophys. Res., 126, XXXX-XXXX. doi:10.1002/2021JEXXXXXX
- Discipline:
- Engineering and Science
-
- Creator:
- Bougher, Stephen W. (CLaSP Department, U. of Michigan) and Roeten, Kali J. (CLaSP Department, U. of Michigan)
- Description:
- The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking monthly measurements of the speed and direction of the winds in the upper atmosphere of Mars between about 140 to 240 km above the surface. The observed wind speeds and directions change with time and location, and sometimes fluctuate quickly. These measurements are compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. This is the first comparison between direct measurements of the winds in the upper atmosphere of Mars and simulated winds and is important because it can help to inform us what physical processes are acting on the observed winds. Some wind measurements have similar wind speeds or directions to those predicted by the M-GITM model, but sometimes, there are large differences between the simulated and measured winds. The disagreements between wind observations and model simulations suggest that processes other than normal solar forcing may become relatively more important during these observations and alter the expected circulation pattern. Since the global circulation plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere of Mars provides key context for understanding how the atmosphere behaves as a whole system. A basic version of the M-GITM code can be found on Github as follows: https:/github.com/dpawlows/MGITM and About 30 Neutral Gas and Ion Mass Spectrometer (NGIMS) wind campaigns (of 5 to 10 orbits each) have been conducted by the MAVEN team (Benna et al., 2019). Five of these campaigns are selected for detailed study (Roeten et al. 2019). The Mars conditions for these five campaigns have been used to launch corresponding M-GITM code simulations, yielding 3-D neutral wind fields for comparison to these NGIMS wind observations. The M-GITM datacubes used to extract the zonal and meridional neutral winds, along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. README files are provided for each datacube, detailing the contents of each file. A general README file is also provided that summarizes the inputs and outputs of the M-GITM code simulations for this study.
- Keyword:
- Mars, MAVEN spacecraft, Mars thermosphere, and Mars global upper atmosphere winds
- Citation to related publication:
- Roeten, K. J., Bougher, S. W., Benna, M., Mahaffy, P. R., Lee, Y., Pawlowski, D., et al. (2019). MAVEN/NGIMS thermospheric neutral wind observations: Interpretation using the M‐GITM general circulation model. Journal of Geophysical Research: Planets, 124, 3283– 3303. https://doi.org/10.1029/2019JE005957
- Discipline:
- Science and Engineering