Ansys Fluent License
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The Ansys Fluent Visualization Python Module is a dynamic client library that allows you to produce visually captivating depictions of fluid dynamics simulations using Ansys Fluent. This tool employs the features of PyVista and Matplotlib to offer a user-friendly interface for generating 3D plots, contour plots, and interactive visualizations. Regardless of whether you're a computational fluid dynamics student or an experienced engineer, the Ansys Fluent Visualization Python Module can assist you in extracting valuable insights from your simulation data by presenting it in a manner that is both intuitive and informative. This module facilitates the export of 3D Interactive Graphics for mesh visualization, contour plots, etc., as python objects, GLTF files (GL Transmission Format), vtk, and HTML. The key advantages of 3D visualization in the browser are its ability to export the model, simulation data as HTML or other mentioned formats, making it effortless to share and embed in web pages and web applications. This capability can be leveraged to present data with modern Web Apps, Enterprise Engineering Workflows in general.
In this article, we will explore the various features of the Ansys Fluent Visualization Python Module and guide you on how to export 3D visualizations as HTML. For a basic understanding of the installation and setup process, and to delve into some of the module's advanced features, including a comprehensive example, please refer to the latest release documentation. Additionally, this article includes code snippets based on the version ansys-fluent-visualization 0.7.1, which is available on the Python Package Index (PyPI), a repository of software for the Python programming language.
To sum up, the Ansys Fluent Visualization Python Module is a highly valuable resource for individuals engaged in computational fluid dynamics. It offers a robust and easy-to-use Python interface for producing striking visualizations of fluid dynamics simulations. The capability of exporting 3D visualizations as HTML or other formats makes it exceptionally convenient to share and embed simulation data in web pages and applications. By delving into the essential features of this module, we trust that this article has equipped you with a deeper comprehension of its possibilities and how it can enhance your work in fluid dynamics visualization.
This tutorial shows you how to deploy an HPC cluster and run an Ansys Fluentworkload. The HPC cluster deployment is done by using Cluster Toolkitand this tutorial assumes that you've alreadyset up Cluster Toolkitin your environment.
Cluster Toolkit is open-source software offered by Google Cloud which makesit easy for you to deploy high performance computing (HPC) environments.Ansys Fluent is simulation software that is used to create advanced physics models.
Important: In this document, you use the following billable components of Google Cloud:
- Compute Engine
- Filestore
- Cloud Storage
- Set up Cluster Toolkit.During the setup ensure that you enable all the required APIs, and permissions,and grant credentials to Terraform. Also ensure that you clone and build theCluster Toolkit repository in your local environment.
At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.
From the CLI, upload both the installation file for Ansys Fluent and theinput file, that you got from Ansys, to Cloud Storage. After you upload thefiles, the files are then available to be copied to the VMs in your cluster in alater step.
Create the HPC deployment folder.This tutorial uses thefluent-tutorial.yamlHPC blueprint that is located in the Cluster Toolkit GitHub repositorythat you cloned during the set up ofCluster Toolkit. Tocreate a deployment folder from the HPC blueprint, run the following commandfrom the CLI:
ghpc reports the changes that Terraform is proposing to make for yourcluster. Optionally, you may review them by typing d and pressing enter.To deploy the cluster, accept the proposed changes by typing a and pressingenter.
From the login VM, run the following command to setup passwordless SSH. Thisallows Intel MPI to run on all the hosts in your cluster. There is a hostsfile that was automatically created by the HPC blueprint.
Set environment variables for the Ansys Fluent licenseconfigurations. The license configuration is dependent on yourinstallation and is provided by Ansys.Replace YOUR_LICENSE_SERV with your license server IP.
To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.
To delete the project:
- Caution: Deleting a project has the following effects:
- Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
- Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.
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Once done that you must create a template with the commands to initialize the calculations. The easiest way is to search in the net and try everything in the TUI at fluent. Once everything has been validated, you create a template (The easiest way is to use jinja2)
First make sure your case runs and gives sensible results setting the boundaries as numbers. Then, the two components of velocity in the boundary conditions can be set to parameters from the downwards arrow on the right, choose New input Parameter for each and give sensible names,
so this appears on the Parametric study tab.Then provided the individual case ran, the Update All button should give the results of the parameter study. It's worth noting, in my case at least, reported drag and lift forces assume X=1 Y=0 and X=0 Y=1 respectively so may need some rotation base on the inflow angle...
A Fluent journal file is a series of TUI commands stored in a text file. The file can be written in a text editor or generated by Fluent as a transcript of the commands given to Fluent during your session.
A journal file generated by Fluent will include any GUI operations (in a TUI form, though). This is quite useful if you have a series of tasks that you need to execute, as it provides a shortcut. To record a journal file, start recording with File -> Write -> Start Journal..., perform whatever tasks you need, and then stop recording with File -> Write -> Stop Journal...
You can also write your own journal file into a text file. The basic rule for a Fluent journal file is to reproduce the TUI commands that controlled the configuration and calculation of Fluent in their order. You can add a comment in a line starting with a ; (semicolon).
Then we can use Fluent to run this journal file by simply using:fluent 3ddp -t$NTASKS -g -i testJournal.jou in the terminal. Here, 3d indicates this is a 3d model, dp indicates double precision, -t$NTASKS tells Fluent how many Solver Processes it will take (e.g. -t4), -g means to run without the GUI or graphics, -i testJournal.jou tells Fluent to read the specific journal file.
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