Code Framework

PyTurbSim is a Python ‘package’ organized into ‘modules’ and ‘sub-packages’ that provide a logical and functional framework for the code and its use. The primary components of this framework are:

  1. The modules in the pyts root folder, especially:
    1. pyts.main, which contains the high-level classes and functions that control the flow of PyTurbSim simulations. Most importantly, this module defines:
    2. pyts.base, which contains abstract base classes for other components of the code (this is the foundation of the code structure).
  2. The model packages, which contain predefined models of the statistics PyTurbSim is capable of simulating.
    1. pyts.profModels, contains the mean velocity profile models.
    2. pyts.specModels, contains the TKE spectral models.
    3. pyts.stressModels, contains the Reynold’s stress profile models.
    4. pyts.cohereModels, contains the spatial coherence models.
More details on model packages can be found in the Models vs. run-specific ‘statistics’ section.

  1. The pyts.io package supports reading and writing of TurbSim input (.inp) and output files (e.g. .bl, .wnd, etc.).

  2. Wrapper packages that implement different user interfaces of PyTurbSim:

    1. pyts.runConfig contains all the software for running PyTurbSim using input files (to mimic O-TurbSim).
    2. pyts.gui contains the graphical user interface.

    For further information on how to use these interface see Usage.

  3. The ‘tslib’ folder and library. This folder contains the Fortran code that implements the processor-intensive pieces of PyTurbSim. Having this library compiled for your system significantly speeds up PyTurbSim. For information on properly installing this library, consult the Download and Install page.

Models vs. run-specific ‘statistics’

PyTurbSim makes a distinction between ‘models’ (PyTurbSim objects that partially define a statistic) and run-specific ‘stat-objects’ which are the output of these model objects (these are numpy array wrappers). Models are independent of the spatial grid and other models in a PyTurbSim run. The statistics are the values of the statistic that will be reproduced in the PyTurbSim output.

PyTurbSim is primarily designed to produce output that matches four statistics for each component of velocity (u,v,w):

  1. The mean velocity profile (prof),
  2. The turbulence energy spectrum (spec),
  3. The spatial coherence (cohere), and
  4. The Reynold’s stresses (stress).

Within PyTurbSim each of these statistics may be defined in two distinct ways. They may be defined using ‘models’ or using ‘stat-objects’ (essentially array wrappers). Models define a statistic in terms of input parameters to a model and other variables of other models. For more information on specifying a statistic, see the four statistic properties (prof, spec, cohere, and stress) of a tsrun object.

Models

Models define the functional form of a statistic and can depend on the spatial grid or other stat-objects defined before it. When models are used to compute statistics they are computed the order above (1-4). This means that a turbulence energy spectrum model can depend on the mean velocity profile, but not the coherence or stress. Likewise the coherence can depend on the mean velocity and turbulence spectrum, but not the stress, and so on. (Even though models cannot depend on the values of an array defined later, they can depend on ‘parameters’ of that model.)

Stat-objects/arrays

Stat-objects, on the other hand, contain the values of the statistic (an array) that will be reproduced in the PyTurbSim output. However, because stat-objects are essentially wrappers for an array of the values of the statistic, they do not have the flexibility to depend on the values of other statistics.

Customizing PyTurbSim

Need to add some examples here…