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A Customized Python Module for CFD Flow Analysis within VisTrails
by Joel E. Tohline, Jinghua Ge, Wesley Even, & Erik Anderson

A relatively simple, customized Python module that plugs smoothly into an otherwise standard workflow within VisTrails facilitates a quantitative analysis of complex fluid flows in simulations of merging binary stars.



Researchers in the open source community are steadily improving scientific visualization tools. These tools are providing a wider array of sophisticated probes for data analysis and a wider assortment of effective user-friendly interfaces. They're also making it easier for researchers in the computational science community – across many disciplines – to effectively analyze huge datasets by drawing on the human brain's acute ability to sort through complex and time-varying visual patterns. The astrophysics group at Louisiana State University (LSU), for example, routinely uses volume-rendering and ray-tracing algorithms in conjunction with animation techniques to examine the time-varying behavior of isodensity surfaces that arise in computational fluid dynamic (CFD) simulations of mass-transferring and merging binary star systems. Although such analyses generally provide only a qualitative identification and assessment of structure within a given dataset, the insight gained from visual inspection can nevertheless be extremely valuable. For example, it was through visual inspection that researchers at LSU initially spotted the nonlinear development of triangular-, square-, and pentagonal-shaped tidal resonances in recent simulations.

LSU's astrophysics group has begun to incorporate VisTrails into its arsenal of scientific visualization and data analysis tools. VisTrails primarily interested the group a few years ago because it provides a user-friendly workflow interface to the extensive VTK software library. It also automatically tracks the provenance of data analysis efforts. However, what most impresses us now is the ease with which VisTrails facilitates the insertion of home-grown analysis modules into an otherwise VTK-based workflow. Taking advantage of this additional programming versatility, we have gained a greater appreciation of the role that visualization tools can play in the quantitative assessment of results from large-scale simulations. In this article, we first describe the VTK-based workflow that we initially constructed in VisTrails to view streamlines within each binary mass-transfer simulation. We then describe the Python module, whose insertion into this workflow has permitted us to identify values of key rotational frequencies associated with such flows.


Before diving into a discussion of the VisTrails workflows that have been developed for this study, it will be useful to download the relevant .vt module from the VisTrails database.

vt_id:19 version:1834

Base Workflow

Figure 1.  Screenshots of the window within the VisTrails builder that displays user-designed visualization workflows. (a = left panel) The base workflow we constructed from standard VTK-based modules.  (b = inset panel) A segment of the workflow that's hidden inside the Draw_Streamlines group module. (c = right panel) The customized workflow we used to create Figure 2, in which we inserted the SwitchCoord module containing our customized Python script into the base workflow.
Figure 1: (scroll over image for caption)

Within VisTrails, we initially selected various VTK-based modules to do the following, in sequence (see Figure 1a):

  • Read simulation data. We used vtkPLOT3DReader to read in one file containing the (x,y,z) coordinate locations of every vertex on our 3D cylindrical coordinate mesh and a separate file containing the fluid's mass-density (scalar) and momentum-density (3D vector) at every grid vertex.
  • Outline cylindrical domain boundary. As shown, we enlisted vtkStructuredGridOutlineFilter, vtkPolyDataMapper, and vtkActor.
  • Define isodensity surfaces. We rendered two nested isodensity surfaces to outline high- (red) and low-density (blue) flow regions. The Red_contour and Blue_contour module groups each contain vtkContourFilter, vtkDataSetMapper, vtkProperty, and vtkActor.
  • Draw streamlines. As Figure 1b shows, each of the eight separate Draw_Streamlines module groups uses vtkStreamLine, vtkTubeFilter, vtkDataSetMapper, vtkProperty, vtkActor, vtkSphereSource, vtkPolyDataMapper, and vtkLODActor to trace an individual streamline within the flow. Streamline lengths are set by feeding a common Propagation_Time into all eight module groups.

Vistrails renders the output from the various workflow actors in a composite scene using vtkRenderer as viewed by an observer located at a position that vtkCamera specifies. Finally, the module vtkCell directs this scene to the VisTrails interactive spreadsheet.

In this initially constructed base workflow, VisTrails pipes the 3D vector field representing the momentum density distribution from the vtkPLOT3DReader module directly into each of the eight Draw_Streamline module groups. This base workflow – which VisTrails assembles using generically available vtk modules – lets us examine the behavior of streamlines in our binary mass-transfer simulations, but only from the frame of reference, Ω0, in which we originally performed each simulation (see Figure 2b, labeled ΔΩ = 0.00).

One well-defined characteristic of a binary star system is it orbital period, P. If the stars are in circular orbit around one another, a binary system will appear to be stationary when viewed from a frame that's rotating with an angular frequency Ωframe = 2π / P. When modeling mass-transferring binary star systems, we've found it advantageous to perform each computational fluid dynamic (CFD) simulation on a cylindrical-coordinate grid that rotates with a frequency Ω0 = 2π / P0, where P0 is the binary system's orbital period at the beginning of the simulation. As mass and angular momentum are transferred from one star to the other throughout the simulation, however, the binary system's orbital period – and associated value of Ωframe – will vary. As explained in the main text, we used VisTrails to measure ΔΩ = (Ωframe − Ω0) and, hence, the instantaneous orbital period P = P0 / (1 + P0ΔΩ / 2π) at any time during a simulation.

Customized Python Module

To make it possible for us to examine the properties of binary mass-transfer flows from reference frames that have a range of different angular frequencies of rotation, Ωframe = (Ω0 + ΔΩ), we wrote a Python-based module – SwitchCoord – for insertion into the base VisTrails workflow. Figure 1c shows our resulting customized VisTrails workflow; it differs very little from the base workflow. The sidebar shows the complete Python source code from our customized module. The code segment that performs the required physics analysis is short and straightforward. In particular, the SwitchCoord module performs the following operations at each grid vertex:

  • converts the (x,y) Cartesian to (R,φ) cylindrical coordinates;
  • divides the momentum components by density to obtain the velocity components if the density is greater than minp (otherwise, it sets the velocity components to zero);
  • shifts the azimuthal velocity component vφ to a new, rotating frame of reference by adding R\times \Delta\Omega;
  • converts the cylindrical velocity components to Cartesian velocity components; and
  • normalizes the velocities to the maximum velocity maxnorm found across the domain where densities are greater than minp.

We designed the output ports on SwitchCoord to provide access to the same type of structured arrays that vtkPLOT3DReader generates. But, in our customized VisTrails workflow, which includes SwitchCoord (see Figure 1c), the 3D vector field that VisTrails pipes into each of the eight Draw_Streamline module groups represents the fluid's velocity distribution as viewed from the rotating frame of reference that the floating-point scalar, Omega_frame, specifies.

Interpretation of Results

Figure 2.  Screenshot of the VisTrails spreadsheet after we used five different values of Omega_frame (ΔΩ) to execute our customized workflow.  Each 3D-rendered image displays eight equatorial-plane streamlines and a pair of isodensity surfaces (lower density surface colored blue; higher density surface colored red) that outline the structure of both stars as well as the connecting mass-transfer stream.  The propagation time is the same for all eight streamlines; along six streamlines, VisTrails carries out the integration in both directions from the location marked by a small colored sphere.
Figure 2 (scroll over image for caption)

Figure 2 displays 3D renderings of the flow from one of our binary mass-transfer simulations as generated by our customized VisTrails workflow. We have generated images assuming five different frame rotation frequencies, as specified by ΔΩ. Aside from labeling ΔΩ values under each image, we produced Figure 2 by simply taking a screenshot of the VisTrails interactive spreadsheet. The spreadsheet feature has proven to be extremely useful in this analysis because it facilitates the side-by-side comparison of scenes that VisTrails has rendered using different parameter values. And, although we can't demonstrate it here in print, VisTrails lets users zoom, pan, and interactively rotate all 3D-rendered scenes simultaneously.

We'd like to determine which value of ΔΩ provides the best measure of the binary star system's true orbital period. As expected, for all five choices of ΔΩ, we found the highest velocities (marked by the longest streamlines) along the relatively low-density mass-transfer stream that connects the two stars. Material from the donor star (in the lower half of each rendered image in Figure 2) flows toward its stellar companion, reaching supersonic velocities before impacting the companion. An oblique shock front – whose location is delineated by kinks in the pink, blue, and orange streamlines – terminates the component of motion perpendicular to the companion's surface. Motion transverse to the shock becomes orbital motion in a thick, low-density disk that surrounds the companion star.

For all five choices of ΔΩ, the flow's behavior in the vicinity of the mass-transfer stream very closely resembles the behavior that Stephen Lubow and Frank Shu predicted more than 30 years ago. Figure 3b shows the magnified view of this region of the flow from our simulation assuming ΔΩ = − 0.041. We've reoriented this magnified image and numbered the streamlines to facilitate comparison with the Lubow and Shu illustration, which we've reprinted with permission here (see Figure 3a). Rather than conducting a fully self-consistent 3D simulation – which was computationally impractical at the time – Lubow and Shu used a mathematical perturbation analysis to estimate what the flow should look like in the vicinity of the "L1" Lagrange point, as viewed from a frame of reference rotating with the correct instantaneous orbital frequency, Ωframe. The close resemblance between our 3D simulation results in the vicinity of the L1 Lagrange point, and the behavior that Lubow and Shu predicted provides a useful point of verification for our work.

Each star's center of mass should lie near the center of the highest density region inside each star (outlined by the nearly spherical, red isodensity surfaces in Figure 2). When we've assigned ΔΩ a value that properly identifies the frequency at which the centers of mass of the two stars are orbiting one another, we should see very little residual motion near the donor star's center – that is, streamlines rendered in white and green in Figure 2 should be quite short. Furthermore, we expect that this residual motion should translate into concave streamline segments, mapping out simple circular motion around the center of the donor star. When we've identified the correct value of ΔΩ, we also should expect the returning streamline nearest the mass-transfer stream (colored yellow) to remain inside the donor. With these ideas in mind, we judge ΔΩ = − 0.041.

Finally, we note that as the pink and blue streamlines curve around the companion star, they extend outside the companion's disk (as outlined by the blue isodensity surface) in the rendered images with the most negative specified values of ΔΩ. These two streamlines appear to align most neatly with the distribution of material in the disk in Figure 2a, that is, for ΔΩ = + 0.02. This suggests that there's a characteristic frequency associated with motion in the companion's disk that's different from the binary orbital frequency.

Figure 3.  A magnified view of multiple streamlines in the region of the flow where the mass-transfer stream originates.  (a = left panel) Reproduction of Figure 3 from the published work of Stephen Lubow and Frank Shu (used with permission).  (b = right panel) A magnified segment of the flow depicted in Figure 2, image D (ΔΩ =  − 0.041) from this work; we've numbered the colored streamlines to aid in our comparison with the Lubow and Shu image.
Figure 3 (scroll over image for caption)

Sidebar: SwithCoord Python Module

Here we present a complete listing of the python code form our customized SwitchCoord program module. At the beginning of the "Physics Analysis" segment of the code, we assign names to the data arrays that have been acquired as input from vtkPLOT3DReader: pcoords is a tuple that identifies the Cartesian-based coordinate location of each grid vertex, density is a scalar that specifies the mass density, and momentum is a tuple that specifies the values of the cylindrical-coordinate-based vector momentum at each grid vertex. We detail the remaining logic of the physics analysis in the main text.

import core.modules.module_registry
from core.modules.vistrails_module import Module, ModuleError
import vtk, math

class SwitchCoord(Module):
    def compute(self):
        minp = self.getInputFromPort("min_density")
        Domega = self.getInputFromPort("Domega")
        output = self.create_instance_of_type('edu.utah.sci.vistrails.vtk', 'vtkStructuredGrid')
        output.vtkInstance = vtk.vtkStructuredGrid()
        self.op(mydata, minp, omega)
        self.setResult("changed_dataset", output)

    ## Begin:  Physics Analysis

    def op(self, mydata, minp, omega):
        pcoords = mydata.GetPoints().GetData()
        density = mydata.GetPointData().GetScalars("Density")
        momentum = mydata.GetPointData().GetVectors("Momentum")

        maxnorm = 0.0
        for i in range(0, mydata.GetNumberOfPoints()):
            [x, y, z] = pcoords.GetTuple3(i)  
            [_v1, _v2, _v3] = momentum.GetTuple3(i)
            p = density.GetValue(i)
            r = math.sqrt(x*x + y*y)         
            phi = math.atan2(y, x)
            if p < minp: 
                vr = _v1 / p
                vphi = _v2 / (p) + r * omega
                vz = _v3 / p
                vx = vr * math.cos(phi) - vphi * math.sin(phi)
                vy = vr * math.sin(phi) + vphi * math.cos(phi)

                norm = math.sqrt(vx*vx + vy*vy + vz*vz)
                if norm > maxnorm:
                    maxnorm = norm
            momentum.SetTuple3(i, vx, vy, vz)

        for i in range(0, mydata.GetNumberOfPoints()):
            [vx, vy, vz] = momentum.GetTuple3(i)
            vx = vx/maxnorm
            vy = vy/maxnorm
            vz = vz/maxnorm
            momentum.SetTuple3(i, vx, vy, vz)

    ## End:  Physics Analysis

def initialize(*args, **keywords):
    reg.add_input_port(SwitchCoord, "scalar_range", 
    reg.add_input_port(SwitchCoord, "min_density", core.modules.basic_modules.Float)
    reg.add_input_port(SwitchCoord, "omega", core.modules.basic_modules.Float)
    reg.add_input_port(SwitchCoord, "dataset", 
                'edu.utah.sci.vistrails.vtk', 'vtkStructuredGrid').module) )
    reg.add_output_port(SwitchCoord, "changed_dataset", 
                'edu.utah.sci.vistrails.vtk', 'vtkStructuredGrid').module) )

def package_dependencies():
    import core.packagemanager
    manager = core.packagemanager.get_package_manager()
    if manager.has_package('edu.utah.sci.vistrails.vtk'):
        return ['edu.utah.sci.vistrails.vtk']
        return []
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