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Filtering by: Creator Towne, Aaron Remove constraint Creator: Towne, Aaron Depositor ssim sborda@umich.edu Remove constraint Depositor ssim: sborda@umich.edu

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  • A database for reduced-complexity modeling of fluid flows: the online collection

    User Collection
    collection
    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