Element Metrics and Histogram Tool

elemetric_histogramAs part of the core capability of EnSight to not only visualize, but analyze the domain and solution results, we have implemented the Element Metrics routines from Sandia Verdict Library (http://cubit.sandia.gov/public/documents/Verde_UG-2.5B.pdf). There are approximately 30 different element metrics that this routine will calculate for you, depending upon the grid types used. Again, we have implemented this function as a general purpose calculator function, as well wrap this fundamental capability with a Python-based macro to add further ease of use and end-user functionality. This macro is called “EleMetric_Histogram”, and performs the following on the selected parts in EnSight:

1. Determines the element types selected, and only calculates EleMetric quantities which apply.
2. Creates separate scalar variables for each Metric computed. Users can color by any of these variables, or further query where maximum or minimum values occur.
3. Determines a Histogram Distribution through the domain, and reports this to a text file (EleMetric_Histogram.txt) in the directory where your case is. This file is simple ascii, and can be inspected with any text editor.
4. An EnSight Query is generated for each Metric Histogram, so that you can visually graph the distribution of that Metric for the domain.

elemetrics_guiDouble clicking on this utility will launch the following short GUI for some basic user controls:
The first option allows the user to utilize the current selected parts only, or to calculate the variables for ALL parts in the model.
The histogram options provide the ability to control the number of bins for the histogram function, as well as where to write this information (text file and/or EnSight query registers).
The typical output to the text file should provide a nice overview of the variables computed, as well as quantification of the variable distribution in the domain.

The “EleMetric_Histogram.txt” file list all of the variables computed. If the minimum and maximum are the same, we simply report that. Else, the distribution is given as well:

txt_file_grab

In EnSight, the user now has access to the variable information computed, and can clip the domain (perhaps a crinkly clip would be best), and color the domain by the metric of interest:

image_quality1_lrg

 

The following link can be used to download the user defined tool for “EleMetric_Histogram”

Download here for elemetric_histogram

Current version is 4.0, dated 23-Oct-2014, updated by Marina Galvagni.

Kevin Colburn, CEI

 

Spatial Probability Density Function (PDF) in EnSight

In analyzing the variable distribution within a solution, it is quite helpful to look at some type of Probability Density Function for the variable. There are a few interpretations of this function depending upon whether you are looking at the probability across time, space, or different datasets. In essence, the spatial probability density function indicates to the engineer how much of the domain (% of total volume in this case) which contains particular variable range. “How much of the domain has Species 1 near 0.01, or How much of the domain has Temperature near 900 degrees K”.

This particular tool I wrote is to analyze the distribution of a function spatially, sometimes referred to a volumetric probability density function, or a volumetric histogram. In a very basic interpretation, it is similar to the histogram display in the Color Palette Editor, normalized based on element volume.

This routine takes the parent part(s) that you have selected along with the variable that you’d to interrogate, and a user specified number of bins (or bars) in the histogram. The routine automatically finds the minimum and maximum variable range for the part(s). Based on this range, it then divides the volume into N number of IsoVolumes (number of bins) based on this variable range. The routine then determines the fraction of the total volume which is contained within each of these variable constrained ranges. The result is placed into a query register and automatically plotted on the screen.

The Tool presents the user with the simple Window to select the variable, and number of bins (or bars) for the distribution function.

 

After executing, you will then get a graph of distribution of the variable within the parent part(s) selected.

The values on the graph should always sum to 1.0, as it is presented as a fraction of the domain which contains that variable.

Note: As users increase the number of bars( or bins) for the graph, the shape of the curve will increase in resolution, although values on the Y-axis of the graph will adjust (as more bins used will still sum to 1.0).

This Tool can be downloaded from the link below. Please unzip the file and place both the Python Script and Icon PNG file into your UserDefinedTools area and restart EnSight. You should see a “PDF Graph” icon available in your UDT area, and you can double click to execute. Note, in EnSight 10, you can now place these UDT

Update 16-April-2012:

Based on User Requests, I’ve updated the routine to create a Stair-Stepped Graph rather than the smooth graph to represent the “binning” concept further in the graph.

Updated 22-April-2012:

Based on User Requests, I’ve updated the routine to handle transient models now. It does this by exporting out each timestep as a 1D bar part that is then automatically read back into EnSight at the end. This results in the PDF function correctly sitting in the time domain, and allows the user to normally control the timestep & all other features of EnSight while the PDF Graph correctly dove-tailed into the heirarchy of EnSight. This also allows the user to further interrogate and  and operate with the data as though it were normal Elemental Data (filtering, elevated surfaces, coloring, etc.)

I also updated the routine to correctly work with 2D parent parts (using the EleSize() and StatMoment() together rather than Vol().)

In addition, I also updated the routine to select the original Parent Parts for ease of repeat usage (rather than leaving the IsoVolume part selected).

Here is a example movie made from the Transient PDF function:

test_anim_out_sm

 

Update 05-Nov-2013: Updated routine for bug regarding the variable used for the IsoVolume. Previously, the variable selected in the GUI might not have been the variable used in the IsoVolume. Fixed in version 2.3

Update 03-Nov-2015 : Updated the explicit setting for the IsoVolume to “Band”, rather than default (not explicitly set). This would cause problems if previous IsoVolumes were not of type == Band.

Full Change log within header of Python File

Download:

Click here to Download PDF Tool v2.3 (pdf_graph_v2.3) Dated 05-Nov-2013

Click here to Download PDF Tool v 2.6 (pdf_graph_v2.6) Dated 03-Nov-2015