Merging Multiple Time Periods of a Converge Result into One

Are you a ConvergeCFD user with EnSight?

Do you have a Single Dataset, but multiple time periods which you translated to Case Format? Ie you export results from Time T = 0 to T1 in one operation, then later exported from Time T2 to Time T3, and then again exported reults from Time T4 to T5?

If so, then currently you have multiple different time ‘sets’ or ‘periods’, which can’t easily read into a single session of EnSight (other than using Multiple Cases, but that is not what Cases are meant for).

Would you then like to be able to operate on this solution as though it were one complete time period from T = 0 to T5? Well, I think we have a little tool for you. This tool should provide you back with a single coherent time period (From T = 0 to T = T5) as a single case.

This little python tool takes a look at all of the .case files in your current directory, and gathers up a list of all of the files that are referenced, constants written, and time values. In then creates a internal global list of all of that infomation, and sorts it in ascending time order. It then renames all of the associated files so that they fall into a single progression in time order, and generates a NEW case file, referencing the global total time, global total constants, and the newly renamed set of variable and geometry files. The routine takes only a second to run (renames are quick), and you should be able to load up the NEW case file into EnSight.

As renaming files is perhaps a dangerous thing, the routine also writes an “undo” file as well. You can run the “undo”, and you will get back to your original set of files (if you need to for some reason).

As with any routine, there are some assumptions, limitations, and caveats. They shouldn’t be too restrictive, but here they are, so you have a better idea about what the routine is expecting:

a. All of the case files are in the same directory (the routine doesn’t go rooting around in sub directories looking for .case files to merge).

b. All the .case files in the current directory are meant to be “merged” into one. If there are some which aren’t meant to be merged, just move them temporarily to somewhere else.

c. When you exported the results (using post_convert), you ALWAYS exported the same variables. No exceptions allowed, no if’s, but’s, no coconuts. (EnSight can’t deal with some variables existing at some times, and not others…. rules are rules).

d. The time values can overlap, and be of varying delta T. The routine should handle that okay.

e. The routine assumes you have measured data (default for converge post_convert us).

f. Due to internal coding assumptions/methods, this routine is for CONVERGE users only. If you have results from OpenFOAM, or Fluent, CFx, or other solvers, please contact CEI ( separately. We have some prototypes for other solvers.

To run the routine, simply download the python file, and place it into the directory you want to merge Converge .case files together. Then, type :


After running, should you need to undo, there will be a new python file created in your directory, and that can be run via:


This works for MAC/Linux/Win. Use a cmd shell in Windows to run the python routine.

As always, please contact if you have questions, comments, requests. We’d love to hear from you, and work with you to provide you with the best post processing experience.


Download Link for file:


Once downloaded, simply unzip the file to find the python .py routine.

Fluent Particle Data with Multiple Injections

Currently (up to at least Fluent 15), when you attempt to export multiple injections from Fluent to the EnSight format, each injection is written as its own set of Measured Data files **SEPARATELY**, with separate .encas files written.

The result is that if you were to attempt to load any of the separate .encas files into EnSight, you would only see particles from a single injection. You could perhaps load in the multiple .encas files as multiple cases, but this would perhaps multiply the amount of memory used, result in lots of extra parts, and is not a suggested method (although it might work for you).

Fluent are aware of this issue, and have been requested to correctly export multiple injections to a single set of Measured Data files. Their schedule for that improvement is currently unknown.

However, in the meantime, there is a potential work around for this. With a short python script, one can merge the various injections into a single set of measured data files, so that you can visualize all of the injections in a single case. In addition to merging the injections, the routine also creates one additional variable called “inj_id” for the injection ID. Thus, you can still analyze, interrogate, visualize separate injections within EnSight, even though it is contained within a single Measured Data Part.

The ptyhon routine does require a very minor edit, based on your particular dataset, variable export, and timesteps.

A short video tutorial on using this python utility (including the basic editing for your dataset) is provided below.

Tutorial Video on Handling Multiple Injection Particle Data from Fluent

Click here to Download the Python Utility

Releasing Streamlines from Centroids of Elements

imgoutA request came in the other day to release streamlines from the centroids of all of the 2D elements of the selected parts. Now, EnSight, the default seed locations are from the nodes of the parts, not the centroids. However, this user would like to release from the centroids of the parts selected.

Python to rescue. We can utilize the Calculator Capability in EnSight to compute the X,Y,Z coordinates of the selected parts, and use the “NodeToElem” to convert those three scalar variable field values to be at the centroids of each element. With Elemental Values at the centroids of the parts, we use the FlatFile export with the “CELLID” parameter and ascii format to write out the elements of selected part(s) and their elemental variables to a text file. We can then just reformat this text file into a qualified “Particle Emitter File”. Voila. Within the standard streamline creation of EnSight, we can use a “Particle Emitter File” as the seed locations for the Streamlines, and therefore generate streamlines from the centroids of all elements in the selected part(s).

See below a short little Python Code to do this operation, as well as an image of what such a release would look like.

Python Utility to create Particle Emitter File from Centroids of Parts Selected

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 ( 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:


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:



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


STAR-CCM+ Particle Track File Conversion into EnSight 10

** Updated 01-May-2014: Users of EnSight 10.1 will have version2.3 of this tool automatically installed in the UserDefinedTools — > File Import/Export section. **

Users of STAR-CCM+ who are also using the lagrangian or discrete element modeling capability, there is a new translation utility from CEI to convert the STAR-CCM+ based particle track file into EnSight’s Measured Data format, to visualize and analyze within EnSight.

This translation routine takes an existing EnSight Case Format files (the continuous domain) exported from STAR-CCM+, along with the STAR-CCM+ based Particle Track files (.trk). The routine converts the .trk file contents onto a single timeline, creates the appropriate EnSight Measured Data files (in a “trk_mea_files” directory), and modifies the EnSight Case file to include the appropriate references for the Measured Data Files. Once loaded, the user can visualize, analyze, and communicate the information contained within both the lagrangian/discrete phase along with the continuous phase.

To utilize this new translation routine, please follow the instructions below:

1. Download the .zip file containing the new translator. Unzip. To make this available to all users on your installation, place the “ccm_trk_ensext” directory into your installation Tools sub directory as follows:


(Please ensure that you have proper write privileges to place this directory here).

Now start up EnSight as follows (example assumes you are running EnSight 10.0)

ensight100 -no_prefs


for personal use, install into your local Tools directory as described in the How To Manual (in HOW TO PRODUCE CUSTOMIZED ACCESS TO TOOLS & FEATURES)

Local directory – python files for your own personal use

Linux: ~/.ensight100/extensions/user_defined/Tools

Mac: ~/Library/Application Support/EnSight100/extensions/user_defined/Tools Windows

Vista, Windows 7: C:\Users\username\.ensight100\extensions\user_defined\Tools

Windows XP: %HOMEDRIVE%%HOMEPATH%\(username)\.ensight100\extensions\user_defined\Tools

To find out your %HOMEDRIVE%%HOMEPATH% do Help>Online Support, click the System Into tab, and look at the Prefs Dir. This is where EnSight looks for user defined tools.

If you don’t see your tool, try

ensight100 -no_prefs

Download version 2.3 of STAR-CCM+ Track File Conversion Tool Here

2. Start EnSight 10. You should notice a new tool under the “UserDefinedTools — > File Import/Export” denoting STAR-CCM+ .trk file translation:

3. Ensure that you have exported the continuous domain from STAR-CCM+ into EnSight format.

4. Double click the “STAR-CCM+ .trk import” tool from the UserDefinedTools area.

4a. Specify the EnSight Case format “.case” file. (Exported from STAR-CCM+)

4b. Specify the STAR-CCM+ Particle/Discrete Element file (.trk)

4c. Specify the number of timesteps to re-sample the particle track information into.

4d. Specify Time option for conversion of the Particles. The Default of “Max Trace” will resample all of the particles based on the min and max time of all the particles.  If you choose “User Specified”, then please specify the minimum and maximum times in section 4e (below). Use this option if you have rogue particles which may “hang around” for long periods of time, or you want to specifically control the time period over which the particles are converted.

4e. If you choose the “User Specified” Time option, then provide the minimum and maximum time.

4f. Toggle to keep particles at their last known location if the time is greater than defined for that particle. Particularly useful to “stick” particle where they exit the domain.

5. Click on the “Convert” button to convert the STAR-CCM+ based Track File into EnSight Measured data.

You will see a progress bar indicating the progress through the process of reading, re-sampling, and writing out the new EnSight Measured data. Two new items are written in the same directory : “trk_mea_files” directory containing the EnSight format Measured Data, and a modified .case file “<original_name>”

Once complete, you will get a “Done” Dialog, after which the new case file will be automatically loaded in. You can now change the visible aspects of the Measured Data via the “Node Display” icon :

Since the Translation routine writes new Measured Data files, subsequent loading of this case with Measured data, the user can simply specify to load the “” file directly, without the need to re-translate the Particle Track information each time.

Should you have any questions, please do not hesitate to contact CEI ( with questions, comments, praise.

Example Dataset:

An example STAR-CCM+ based dataset containing Particle track information can be downloaded here.


Video Tutorial:

To further assist your use of STAR-CCM+ Particles in EnSight, please consult this video tutorial here:



Users of STAR-CD v3.x and v4.x:

Note: This routine mentioned here is meant to work for the specific “STAR-CCM+” based track file format, which is different to that of STAR-CD (v3.x and v4.x) particle track routines. If you are trying to convert “STAR-CD” based particle track files, please see our existing “f33toparticle” conversion routine.

Revision History

** Version 2.3:  Updated 01-May-2014 for mint & maxt calculation, as well as sorting trks based on time prior to re-sampling/interpolation. **

Creating Custom Color Palette from Image

Suppose you have an image that was created in another package, and you would like to replicate the color palette used in the image within EnSight? Well, EnSight can easily accept custom Color Palettes (see UserManual). The only “tough” part is getting from original image file the RGB values used and into a form of the EnSight Color Palette (.cpal) file. Well, guess what, Python (and more specifically EnVe) can be used to grab out the color legend information, and create your own color palette file (.cpal) for EnSight.

So, how you go about doing this:

1. Start with an image created in another package, which has a color legend. Crop the image to just the color legend palette. You now have an image file which has (in this example) a vertical specification of the color palette.

2. Use the attached python routine below on this file image file. This python routine uses EnVe to query the image file for the RGB values of the pixels in the image, and grab those values off, and write out an EnSight Color Palette file (.cpal) with the appropriate information in it. (If your image has a horizontal legend, then you can switch the x,y values around to perform the same operation).

3. You can thus create a new EnSight Color Palette File (.cpal) from the original image created in another package. EnSight has a limit of 21 bands, but handles 16 colors per band for smooth palette range.


As always, please contact us here at CEI should you have any questions, comments, having trouble, or would like routines this customized to your needs. We are here to help.

Click here for Color Palette Create Tool


Dynamic Range Plotter

Suppose that you have a transient domain, where you have some information at a high temporal frequency, but your time domain is relatively large. If you make a default plotter, the temporal range is too large to see the high frequency information. But, if you zoom in the time range, you are “fixed” to a particular time. Suppose you’d like to see both the higher frequency information, but also the whole time domain?

Python to the rescue again. Using Python, we can dynamically adjust the time range of the graph at each timestep… and thus create a plotter with a relatively small time “window”, but that window moves with the current timestep, so that you can see the whole time domain.

Here is a short example of what such a “dyanmic range plotter” would look like:


Here is a short tutorial on using this python tool:

And here is the tool itself:

dynamic range plotter python

In-Cylinder Tools

In-Cylinder Engine (ICE) simulations often contain specialized requirements for analysis. EnSight’s Python tool capability allows users to develop their own custom operations to fulfill the requirements of the analysis. I have written an initial set of tools here called “In-Cylinder Tools”, which can be installed as UserDefinedTools in EnSight 10

In this set of tools, we have the following:

1. Calculate Swirl. This routine takes the currently selected parent part(s) (typically the fluid domain of the cylinder), and calculates Swirl Velocity based about the Z axis. Using the parent part, it also calculates an Constant Variable which is the Spatial Mean of Swirl, so that you could easily create a plot vs. time for the average swirl.




2. Calculate Tumble. This routine takes the currently selected parent part(s) (typically the fluid domain of the cylinder), and calculates a tumble velocity, using the current average height of the parent part. The routine automatically works through time to determine this tumble velocity using the new center reference point at each timestep, and creates a graph of tumble vs time.




3. Crank Angle Conversion tool. This converts an EnSight .case file which has been setup with Analysis_Time specified as degrees crank angle, and converts this to Analysis_Time in seconds (user provides RPM). This allows EnSight to compute Pathlines correctly, as all of the constituent variables have consistent units.

4. Spray.out reader. For users of Converge, this tool will read in the Spray.out file information into a series of queries that you can then automatically plot. This reader will also read other Converge .out files which confirm to the save format.






5. Particle Distribution Function. This routine operates on the Measured Data within EnSight, to determine a mass-based distribution of any measured data variable (like radius or temperature) within the time domain. Please refer to this previous Python Exchange article for further information on the intended uses and application of this routine. Previous article.




Please download these tools from the link below. Unzip the file, and place the directory into your .ensight100/extensions/user_defined/Tools/ directory, and restart EnSight. You should then see a new tool folder in your UserDefinedTools area with the above tools.

Should you require any assistance with the tools or modification of them to suit your particular needs, please do not hesitate to contact CEI.

Click here to download In_Cylinder_Tools


Importing Converge .out files

The Converge Solver exports out additional quantitative information into various “*.out” files. The information contained within these .out files can be very useful in visualizing along with the fluid domain in EnSight, to quantify against other extracted values in EnSight, qualitative comparison, or just to easily visualize versus time in EnSight.

This very small/short routine was developed to work on the spray.out file which contains information regarding the spray computed by the solver (injection pressure, injected mass, spray penetration, etc). The routine reads the .out file, along with the user specified engine RPM (to calculate time in seconds), and places all of the values from the .out file into EnSight Queries.

The User Defined Tool should appear as :

The user simply needs to specify the Converge .out file, along with the Engine RPM, so that the routine can correctly tie the query with time.

Once executed, you should see a number of additional queries within EnSight. The name of the Query is taken from the .out file, along with the units. Each query is associated with “Time” in seconds, and should therefore play correctly and appropriately within EnSight, as well as synchronizing with the timesteps of the main dataset.

The resulting Queries can easily be plotted with the Right Mouse Button, or drag/drop onto current plotters. Query-on-query operations can also be performed to further inspect rate of change, comparisons (differences between queries).

You can download the current User Defined Tool here. Once downloaded, unzip the file, and place the contents into your .ensight100/extensions/user_defined/Tools/ directory, and restart EnSight.

Click here to download Converge .out file reader

Particle Distribution Analysis

As a follow on to the Probability Density/Distribution Function for the continuous phase domain (link here), I have created a close cousin of this routine which works on Discrete Particles to determine a Particle Distribution of the Discrete Phase.

This routine was written with the intended use for Spray Distribution in an In-Cylinder model, and built according to the typical variables and techniques used for this modeling scheme. It is common to determine and understand what the distribution of the particular spray is within the domain over time (mass distribution vs. radius).  This routine asks the user for a variable to base the Distribution on (in this case droplet radius). The routine breaks this value down into N number of “bins” (in this case 20). For each bin, the routine calculates the total mass of the spray in that bin, and reports back out a distribution. The routine then walks the transient domain to collect this information over time, and generate extracted information vs. time.

In order to base the total in each bin on Mass, the user must prescribe three items : a) the droplet radius, b) the droplet density, and c) the number of droplet per parcel. In this instance, the actual Discrete/Particle data in EnSight represents one parcel of spray (all with the same physical properties).  Therefore, the mass is represented as (number_drop_parcel)*(particle_density)*(4/3*pi*r^3).

The GUI input for this routine is similar to the previous PDF macro for the continuous phase, with the addition of variable prescription needed for the mass calculation.

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 mass of the spray 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, along with the three items needed to calculate the mass of the spray (radius, density, parcel count)


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 the total mass of spray in the domain.

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.

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 Particle Graph” icon available in your UDT area, and you can double click to execute.


 Video Tutorial:

Please view this video tutorial for a detailed walk through of using this tool for Spray Analysis.

Screencast Tutorial

Please use the following link to download the UserDefinedTool:

Click here to download Particle Distribution Tool