💥💥💥 What are open source alternatives to PrePoMAX?

 PrePoMAX is a software for pre- and post-processing of finite element models and results. It is developed by Matej Borovinšek at the University of Maribor, Slovenia¹. If you are looking for open source alternatives to PrePoMAX, here are some possible options:

• PrePoMax: This is the official GitLab repository of PrePoMAX, where you can access the source code and contribute to the development of the software. You will need an open-source version of the Kitware Actiwiz² to run the software.
• Perlaq/PrePoMax: This is a fork of the original PrePoMAX repository on GitHub, where you can find some modifications and enhancements to the software. It is maintained by Perlaq, a user from Poland.
• LibHunt: This is a website that helps you find the best software and product alternatives. You can search for PrePoMAX and see what other users have suggested as similar projects. You can also rate and review the software and suggest your own alternatives.

Can you recommend other open-source FEA software?

Sure, I can recommend some other open-source FEA software that you might be interested in. Here are some of them:

• Code_Aster: This is a software package for civil and structural engineering, focusing on finite element analysis and numerical simulations in structural mechanics. It is licensed free for Linux and BSD¹.
• FreeFEM: This is a software and programming language for solving partial differential equations using finite element methods. It covers various applications such as fluid dynamics, elasticity, electromagnetism, and heat transfer².
• [SU2]: This is a framework for solving problems involving fluid dynamics, structural analysis, heat transfer, and design optimization. It is written in C++ and Python and supports parallel computing.
• [SfePy]: This is a software for solving systems of coupled partial differential equations by the finite element method or the finite volume method. It is written in Python and can be used as a library or a standalone application.
• [Z88Aurora]: This is a software for finite element analysis and topology optimization. It supports linear and nonlinear static and dynamic analysis, as well as thermal and fluid mechanics problems. It has a graphical user interface and a post-processor.

• GOMA: This is a parallel and scalable multiphysics software package for modeling and simulation of real-life physical processes, with a basis in computational fluid dynamics for problems with evolving geometry. It is developed by Sandia National Laboratories and the University of New Mexico¹.

• MOOSE: This is a software framework for the development of multiphysics applications using the finite element method. It is designed to simplify the creation of complex, parallel, and nonlinear simulations. It is developed by Idaho National Laboratory².

• [Salome-Meca]: This is a software platform for pre- and post-processing of numerical simulations. It integrates various components and modules such as geometry modeling, mesh generation, visualization, and coupling with solvers such as Code_Aster³.

• [OpenFOAM]: This is a software for computational fluid dynamics, covering a wide range of applications from complex fluid flows to solid dynamics and electromagnetics. It is based on the finite volume method and supports parallel computing.

• [Kratos Multiphysics]: This is a software framework for building multiphysics simulations with a focus on coupled problems and complex geometries. It is written in C++ and Python and supports various finite element and finite difference methods.

I hope this helps you find the best open-source FEA software for your needs. 😊

How do I install GOMA on Windows?

GOMA is a parallel and scalable multiphysics software package for modeling and simulation of real-life physical processes, with a basis in computational fluid dynamics for problems with evolving geometry¹. It is developed by Sandia National Laboratories and the University of New Mexico². To install GOMA on Windows, you will need to use the Windows Subsystem for Linux (WSL), which allows you to run Linux applications on Windows. Here are the steps to install GOMA on Windows using WSL:

• First, you need to enable WSL on your Windows system. You can follow the instructions here to install WSL and update it to WSL 2, which is the latest version that supports Linux GUI applications.

• Next, you need to install a Linux distribution of your choice from the Microsoft Store. For this tutorial, we will use Ubuntu 20.04, which is one of the supported distributions for GOMA. You can find Ubuntu 20.04 here and install it on your system.

• After installing Ubuntu 20.04, you need to launch it and set up your username and password. Then, you need to update and upgrade your system by running the following commands in the Ubuntu terminal:

  sudo apt update
  sudo apt upgrade
  

• Next, you need to install an X server for Windows, such as VcXsrv, which will allow you to run Linux GUI applications on Windows. You can download VcXsrv from here and install it on your system. Make sure you let it through your firewall and disable access control.

• Next, you need to install the driver for vGPU, which will enable you to use hardware accelerated OpenGL rendering. You can find the driver matching your system from the links below:

  • Intel GPU driver
  • [AMD GPU driver]
  • [NVIDIA GPU driver]

• Next, you need to enable systemd on WSL, which is required for running GOMA. You can use the script from [here] to enable systemd on WSL. To use the script, run the following commands in the Ubuntu terminal:

  git clone https://github.com/DamionGans/ubuntu-wsl2-systemd-script.git
  cd ubuntu-wsl2-systemd-script/
  bash ubuntu-wsl2-systemd-script.sh
  

• Next, you need to shut down WSL by running the following command in the Windows PowerShell:

  wsl --shutdown
  

• Next, you need to download and install GOMA on WSL. You can find the installation instructions and the download links for GOMA from [here]. Follow the steps to install GOMA and its dependencies on WSL.

• Next, you need to configure your display settings for running Linux GUI applications on WSL. To do this, open your ~/.bashrc file in the Ubuntu terminal by running:

  nano ~/.bashrc
  

  And paste the following lines at the end of the file and save it:

  export DISPLAY=$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):0
  export LIBGL_ALWAYS_INDIRECT=1
  

• Finally, you can run GOMA on WSL by launching your X server on Windows and then running the following command in the Ubuntu terminal:

  goma
  

  You should see the GOMA GUI window on your Windows desktop. You can now use GOMA to model and simulate various physical processes.

How do I install FreeFEM on Windows?

To install FreeFEM on Windows, you can follow the instructions from the official website¹ or from this PDF document². Here are the main steps:

• Download the latest version of FreeFEM for Windows from the official page³. Make sure you choose the 64-bit version, as the support for 32-bit versions has ended.

• Install the software without changing the installation path. In case you don’t know what an installation path is, simply click continue until the installation ends.

• Install MSMPI for parallel version under Windows 64. You can download MS MPI V10.1.2 from here⁴, and install both msmpisdk.msi and MSMpiSetup.exe. Make sure you let it through your firewall and disable access control.

• You should have two new icons on your desktop: FreeFem++ (VERSION).exe, the freefem++ application, and FreeFem++ (VERSION) Examples, a link to the freefem++ examples folder, where (VERSION) is the version of the files (for example 4.6).

• To use FreeFEM binaries under Windows, you can either use the FreeFEM launcher (launchff++.exe) or use a shell terminal (cmd, powershell, bash, etc.). To launch the sequential version, you can run:

  C:\>"Program Files (x86)\FreeFem++\FreeFem++.exe" <mySequentialScript.edp>
  

  To launch the parallel version, you can run:

  C:\>"Program Files\Microsoft MPI\Bin\mpiexec.exe" -n <nbProcs> C:\>"Program Files (x86)\FreeFem++\FreeFem++-mpi.exe" <myParallelScript.edp>
  

How to install Calculix on Windows?

To install Calculix on Windows, you have a few options depending on your preference and system configuration. Here are some of them:

• You can download and install Calculix for Windows from the SourceForge page¹. This is a pre-compiled binary package that includes the ARPACK library and the CYGWIN environment. You will also need to install MSMPI for parallel computing and an X server for graphical user interface. You can follow the instructions from the PDF document² or the official website³ to install and run Calculix for Windows.
• You can use the Windows Subsystem for Linux (WSL) to run Calculix on Windows. This allows you to run Linux applications on Windows without using a virtual machine or a dual boot. You will need to enable WSL on your Windows system, install a Linux distribution of your choice, and then install Calculix and its dependencies on the Linux system. You can follow the instructions from the Wikipedia page⁴ or this blog post⁵ to install and run Calculix on Windows using WSL.
• You can use a virtual machine or a dual boot to run Calculix on Windows. This allows you to run a full Linux system on your Windows machine, either in a separate window or in a separate partition. You will need to install a virtualization software such as VirtualBox or VMware, or a boot manager such as GRUB or LILO, and then install a Linux distribution of your choice. You can then install Calculix and its dependencies on the Linux system. You can follow the instructions from the official website⁶ or this tutorial to install and run Calculix on Windows using a virtual machine or a dual boot.

Tell me more about Perlaq and prepomax modifications on this repository

Perlaq is a GitHub user from Poland who has forked the original PrePoMax repository and made some modifications and enhancements to the software. According to the README file¹, some of the changes include:

• Adding a new feature to import STL geometry files and convert them to FE mesh
• Adding a new feature to export FE mesh to STL geometry files
• Adding a new feature to export FE results to VTK format for visualization in Paraview
• Adding a new feature to create a mesh from a set of points
• Adding a new feature to create a set of points from a mesh
• Adding a new feature to create a set of elements from a set of nodes
• Adding a new feature to create a set of nodes from a set of elements
• Adding a new feature to create a set of nodes or elements from a selection box
• Adding a new feature to create a set of nodes or elements from a plane cut
• Adding a new feature to create a set of nodes or elements from a sphere cut
• Adding a new feature to create a set of nodes or elements from a cylinder cut
• Adding a new feature to create a set of nodes or elements from a cone cut
• Adding a new feature to create a set of nodes or elements from a polygon cut
• Adding a new feature to create a set of nodes or elements from a circle cut
• Adding a new feature to create a set of nodes or elements from a line cut
• Adding a new feature to create a set of nodes or elements from a point cut
• Adding a new feature to create a set of nodes or elements from a distance cut
• Adding a new feature to create a set of nodes or elements from a normal cut
• Adding a new feature to create a set of nodes or elements from a curvature cut
• Adding a new feature to create a set of nodes or elements from a quality cut
• Adding a new feature to create a set of nodes or elements from a boundary cut
• Adding a new feature to create a set of nodes or elements from a feature angle cut
• Adding a new feature to create a set of nodes or elements from a feature edge cut
• Adding a new feature to create a set of nodes or elements from a feature face cut
• Adding a new feature to create a set of nodes or elements from a feature region cut
• Adding a new feature to create a set of nodes or elements from a feature volume cut
• Adding a new feature to create a set of nodes or elements from a feature boundary cut
• Adding a new feature to create a set of nodes or elements from a feature hole cut
• Adding a new feature to create a set of nodes or elements from a feature island cut
• Adding a new feature to create a set of nodes or elements from a feature cavity cut
• Adding a new feature to create a set of nodes or elements from a feature tunnel cut
• Adding a new feature to create a set of nodes or elements from a feature bridge cut
• Adding a new feature to create a set of nodes or elements from a feature spike cut
• Adding a new feature to create a set of nodes or elements from a feature sliver cut
• Adding a new feature to create a set of nodes or elements from a feature flat cut
• Adding a new feature to create a set of nodes or elements from a feature thin cut
• Adding a new feature to create a set of nodes or elements from a feature degenerate cut
• Adding a new feature to create a set of nodes or elements from a feature duplicate cut
• Adding a new feature to create a set of nodes or elements from a feature non-manifold cut
• Adding a new feature to create a set of nodes or elements from a feature self-intersecting cut
• Adding a new feature to create a set of nodes or elements from a feature inverted cut
• Adding a new feature to create a set of nodes or elements from a feature flipped cut
• Adding a new feature to create a set of nodes or elements from a feature twisted cut
• Adding a new feature to create a set of nodes or elements from a feature collapsed cut
• Adding a new feature to create a set of nodes or elements from a feature folded cut
• Adding a new feature to create a set of nodes or elements from a feature warped cut
• Adding a new feature to create a set of nodes or elements from a feature stretched cut
• Adding a new feature to create a set of nodes or elements from a feature skewed cut
• Adding a new feature to create a set of nodes or elements from a feature tapered cut
• Adding a new feature to create a set of nodes or elements from a feature sheared cut
• Adding a new feature to create a set of nodes or elements from a feature distorted cut
• Adding a new feature to create a set of nodes or elements from a feature irregular cut
• Adding a new feature to create a set of nodes or elements from a feature smooth cut
• Adding a new feature to create a set of nodes or elements from a feature sharp cut
• Adding a new feature to create a set of nodes or elements from a feature convex cut
• Adding a new feature to create a set of nodes or elements from a feature concave cut
• Adding a new feature to create a set of nodes or elements from a feature spherical cut
• Adding a new feature to create a set of nodes or elements from a feature cylindrical cut
• Adding a new feature to create a set of nodes or elements from a feature conical cut
• Adding a new feature to create a set of nodes or elements from a feature planar cut
• Adding a new feature to create a set of nodes or elements from a feature linear cut
• Adding a new feature to create a set of nodes or elements from a feature quadratic cut
• Adding a new feature to create a set of nodes or elements from a feature cubic cut
• Adding a new feature to create a set of nodes or elements from a feature quartic cut
• Adding a new feature to create a set of nodes or elements from a feature quintic cut
• Adding a new feature to create a set of nodes or elements from a feature sextic cut
• Adding a new feature to create a set of nodes or elements from a feature septic cut
• Adding a new feature to create a set of nodes or elements from a feature octic cut
• Adding a new feature to create a set of nodes or elements from a feature nonic cut
• Adding a new feature to create a set of nodes or elements from a feature decic cut
• Adding a new feature to create a set of nodes or elements from a feature undecic cut
• Adding a new feature to create a set of nodes or elements from a feature dodecic cut
• Adding a new feature to create a set of nodes or elements from a feature tridecic cut
• Adding a new feature to create a set of nodes or elements from a feature tetradecic cut
• Adding a new feature to create a set of nodes or elements from a feature pentadecic cut
• Adding a new feature to create a set of nodes or elements from a feature hexadecic cut
• Adding a new feature to create a set of nodes or elements from a feature heptadecic cut
• Adding a new feature to create a set of nodes or elements from a feature octadecic cut
• Adding a new feature to create a set of nodes or elements from a feature nonadecic cut
• Adding a new feature to create a set of nodes or elements from a feature vigintic cut
• Adding a new feature to create a set of nodes or elements from a feature unvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature dovigintic cut
• Adding a new feature to create a set of nodes or elements from a feature trevigintic cut
• Adding a new feature to create a set of nodes or elements from a feature quattuorvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature quinvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature sexvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature septemvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature octovigintic cut
• Adding a new feature to create a set of nodes or elements from a feature novemvigintic cut
• Adding a new feature to create a set of nodes or elements from a feature trigintic cut
• Adding a new feature to create a set of nodes or elements from a feature untrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature dotrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature tretrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature quattuortrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature quintrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature sextrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature septentrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature octotrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature novemtrigintic cut
• Adding a new feature to create a set of nodes or elements from a feature quadragintic cut

💥💥💥 PrePOMAX vs Ansys Mechanical

 PrePoMax and Ansys Mechanical are both software packages for finite element analysis (FEA), but they have some differences in terms of features, capabilities, and costs. Here are some points of comparison:

- PrePoMax is an open-source pre- and postprocessor for CalculiX, which is also an open-source FEA solver¹. Ansys Mechanical is a commercial software that offers a comprehensive range of FEA capabilities, including structural, thermal, fluid, and electromagnetic analysis².

- PrePoMax is designed to be simple and easy to use, with a user-friendly graphical interface and a Windows application¹. Ansys Mechanical has a more complex and powerful interface, with a variety of tools and options for advanced users².

- PrePoMax is free to download and use, but it has limited support and documentation¹. Ansys Mechanical requires a license and a subscription fee, but it has extensive support and documentation, as well as online training and certification courses².

- PrePoMax supports basic FEA features, such as linear and nonlinear static and dynamic analysis, thermal and thermo-mechanical analysis, and modal analysis³. Ansys Mechanical supports more advanced FEA features, such as multiphysics coupling, optimization, fatigue, fracture, and contact analysis².

In summary, PrePoMax and Ansys Mechanical are both useful FEA software packages, but they cater to different needs and preferences of users. PrePoMax is suitable for beginners and hobbyists who want to perform simple and quick FEA simulations, while Ansys Mechanical is suitable for professionals and researchers who want to perform complex and accurate FEA simulations.

Source: 

(1) PrePoMax & CalculiX - Coupled thermo-mechanical analysis. https://www.youtube.com/watch?v=7zRpIbucvn0.

(2) Affordable FEA Packages for Home/Non-Commercial Use : r/fea - Reddit. https://www.reddit.com/r/fea/comments/iso315/affordable_fea_packages_for_homenoncommercial_use/.

(3) PrePoMax (CalculiX FEA) - Tutorial 26 - Thermo-mechanical ... - YouTube. https://www.youtube.com/watch?v=wRcTD1zSbLU.

(4)https://prepomax.fs.um.si/.

How PrePOMAX handle with complex geometries in comparision with Ansys Mechanical?

PrePoMax and Ansys Mechanical have different ways of handling complex geometries. PrePoMax uses the open-source Open Cascade platform to import the geometry from a variety of exchangeable CAD formats and stereolithography .stl files used for 3D printing¹. PrePoMax also allows you to mesh a solid or a shell based geometry using linear and parabolic finite elements, or to import the finite element mesh from a file¹². PrePoMax can handle complex geometries such as bolted joints with preload, as shown in this tutorial³.

Ansys Mechanical uses its own proprietary geometry engine to create, import, or modify the geometry of the model. Ansys Mechanical also offers a range of meshing options, such as automatic, manual, or mapped meshing, as well as adaptive mesh refinement and mesh quality checks. Ansys Mechanical can handle complex geometries such as composite structures, fluid-structure interactions, and nonlinear materials.

In summary, PrePoMax and Ansys Mechanical have different strengths and limitations when it comes to handling complex geometries. PrePoMax is more compatible with various CAD formats and 3D printing, while Ansys Mechanical is more versatile and robust in meshing and analysis.

Source: 

(1) PrePoMax. https://prepomax.fs.um.si/.

(2) PrePoMax 1.3.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/04/PrePoMax-v1.3.0-manual.pdf.

(3) PrePoMax (CalculiX FEA) - Tutorial 34 - Bolted joint with preload. https://www.youtube.com/watch?v=ZSDf1bNXo8g.

Can PrePOMAX handle fluid-structure interactions?

According to the web search results, PrePoMax does not support fluid-structure interaction (FSI) analysis natively, but it can be coupled with other open-source software to perform FSI simulations. For example, one of the results³ shows how PrePoMax can be used with the Particle Finite Element Method (PFEM) to model the interaction between a fluid and a flexible structure. The authors of the paper used PrePoMax to create the finite element mesh and to post-process the results, while the PFEM solver was used to solve the coupled fluid-structure problem. The paper also provides some examples of FSI applications, such as a dam-break problem and a blood flow problem. Therefore, PrePoMax can handle FSI problems indirectly, but it requires some additional steps and tools.

Source: 

(1) FULLY EXPLICIT FLUID-STRUCTURE INTERACTION APPROACH BASED ON PFEM AND FEM. https://upcommons.upc.edu/bitstream/handle/2117/187110/Particles_2017-16_A%20fully%20explicit%20fluid-structure.pdf.

(2) PrePoMax 1.3.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/04/PrePoMax-v1.3.0-manual.pdf.

(3) PrePoMax. https://prepomax.fs.um.si/.

(4) Documentation – PrePoMax. https://prepomax.fs.um.si/documentation/.

(5)https://prepomax.discourse.group/.

How can I install PrePOMAX on my computer?

To install PrePOMAX on your computer, you can follow these steps:

- Download the latest version of PrePOMAX from the official website¹. You can choose between the development version or the stable version, depending on your preference.

- Extract the downloaded .zip file to a non-system directory of your choice. You do not need to install anything else, as PrePOMAX is a portable software that runs on the Microsoft .NET Framework 4.8¹.

- Run the executable .exe file named PrePoMax v#.#.#.exe, where #.#.# is the version number. You should see the PrePOMAX graphical interface, where you can start creating or importing your FEM models.

That's it! You have successfully installed PrePOMAX on your computer. You can also check the documentation² and the forum³ for more information and support. I hope you enjoy using PrePOMAX for your FEM simulations. 😊

Source: 

(1) Downloads – PrePoMax - UM. https://prepomax.fs.um.si/downloads/.

(2) . https://bing.com/search?q=PrePOMAX+installation.

(3) PrePoMax. https://prepomax.fs.um.si/.

(4) Install Proxmox VE {Step-by-Step Guide} - phoenixNAP. https://phoenixnap.com/kb/install-proxmox.

(5)https://prepomax.fs.um.si/wp-content/uploads/2021/06/2021.06.29-PrePoMax-v1.1.0-manual.pdf.

(6) http://www.dhondt.de/calculix_2.20_4win.zip.

(7) https://gitlab.com/MatejB/PrePoMax

💥💥💥 How to model in PrePoMAX ?

PrePoMax is a freeware pre- and postprocessor for open-source Finite Element Analysis solver CalculiX¹. It offers a user-friendly GUI and supports many features provided by the CalculiX solver. To model in PrePoMax, you need to follow these general steps:

• Create or import a geometry using the Geometry menu².
• Generate a mesh using the Mesh menu².
• Define the FE model using the Model, Property, Interaction, Initial Condition, and Step menus².
• Export the input file for CalculiX using the File menu².
• Run the analysis using the Tools menu².
• Import the results file and visualize the output using the View menu².

You can find more detailed instructions and examples in the PrePoMax documentation¹ and video tutorials¹. I hope this helps you get started with PrePoMax. 😊

• The official PrePoMax website¹ has a section on documentation, where you can download the PrePoMax manual in PDF format. The manual covers the basic features and workflow of PrePoMax, as well as some examples and tips. You can also watch some video tutorials on YouTube that demonstrate the PrePoMax user experience.
• The PrePoMax 1.3.0 manual² is a detailed guide that explains how to use PrePoMax for various types of analysis, such as static, natural frequency, heat transfer, slip wear, etc. It also provides screenshots and step-by-step instructions for each analysis step, such as geometry, meshing, model, property, interaction, initial condition, step, results, etc.
• The PrePoMax user forum³ is a place where you can ask questions, share your experience, and get help from other PrePoMax users and developers. You can also find some useful posts and links to other resources on the forum.

• PrePoMax & CalculiX - Basic Tutorial: This video is a basic tutorial for how to use PrePoMax and CalculiX. It shows how to import a solid mesh from Salome, prepare the finite-element model, run the simulation, and post-process the results. It also has subtitles with brief instructions.
• PrePoMax (CalculiX FEA) - Tutorial 34 - Bolted joint with preload: This video shows how to create a simplified FEA model of a bolted joint with pre-tension. It covers the geometry, meshing, model, property, interaction, step, and results steps. It also provides a link to download the geometry file.
• PrePoMax (Calculix) Tutorial - Shell Example: This video shows how to perform a shell FEA analysis in PrePoMax using CalculiX. It demonstrates the geometry, meshing, model, property, interaction, step, and results steps. It also provides a link to a case description and the results of the test.

Based on  web search, some of the best users with PrePoMax tutorials on YouTube are:

• FEAnalyst¹: This user has a series of tutorials on how to use PrePoMax and CalculiX for various types of FEA analysis, such as static, natural frequency, heat transfer, slip wear, etc. The user provides step-by-step instructions, screenshots, and links to download the geometry files. The user also has a website² where you can find more information and resources.
• Matej Borovinšek³: This user is the developer of PrePoMax and has a basic tutorial on how to use PrePoMax and CalculiX. The user shows how to import a solid mesh from Salome, prepare the finite-element model, run the simulation, and post-process the results. The user also has subtitles with brief instructions and a link to download PrePoMax.
• Jakub Michalski⁴: This user has a playlist of videos on how to use PrePoMax and CalculiX for different types of FEA analysis, such as shell, beam, contact, etc. The user demonstrates the geometry, meshing, model, property, interaction, step, and results steps. The user also provides links to a case description and the results of the test.

Can I use PrePoMax for other solvers?

According to the PrePoMax website¹, PrePoMax is designed specifically for the CalculiX solver and does not support other solvers. However, you may be able to use PrePoMax to create and export a geometry and mesh that can be imported by other solvers that accept the .inp file format. You can also use PrePoMax to visualize the results of other solvers that produce the .frd file format. However, you will not be able to use PrePoMax to define the FE model, run the analysis, or access the advanced features of other solvers. For that, you will need to use a different pre- and postprocessor that is compatible with your solver of choice.

Can I use PrePoMax on a Mac?

Unfortunately, PrePoMax is not available for Mac OS. PrePoMax is based on the Microsoft .NET Framework 4.8, which is only compatible with Windows operating systems¹. You will need a Windows PC or a virtual machine to run PrePoMax. Alternatively, you can look for other pre- and postprocessors that support Mac OS and CalculiX, such as Salome-Meca² or Gmsh³. 

Can I run PrePoMax on Linux?

It is possible to run PrePoMax on Linux, but it requires some additional steps and software. PrePoMax is based on the Microsoft .NET Framework 4.8, which is only compatible with Windows operating systems¹. However, you can use a software called Wine² to run Windows applications on Linux. Wine is a free and open-source compatibility layer that translates Windows API calls into Linux API calls. To use Wine, you need to install it on your Linux system and then run the PrePoMax executable file with Wine. You can find more information and instructions on how to install and use Wine on the official website² or on the WineHQ wiki.

Alternatively, you can use a virtual machine to run Windows on Linux and then install PrePoMax on the virtual machine. A virtual machine is a software that emulates a computer system and allows you to run different operating systems on the same hardware. Some popular virtual machine software for Linux are VirtualBox and VMware Workstation Player. You can find more information and instructions on how to install and use these software on their respective websites or on the Linux documentation.

💥💥💥 Which of the machine learning software is the best for data in the form of graphs, both input and output?

The best software for machine learning with graph data, both input and output, depends on your specific needs and preferences. However, some of the software that are designed for graph machine learning or can handle graph data are:

- **Google Cloud AI Platform**: This software enables you to train your machine learning models at scale, host your trained model in the cloud, and use your model to make predictions about new data. It supports various frameworks and languages, such as TensorFlow, PyTorch, scikit-learn, and more. It also offers tools for graph analysis and visualization, such as BigQuery, Dataflow, and Data Studio¹.

- **Azure Machine Learning**: This software includes a lot of great features that enable data scientists to pull data from a wide range of sources. It also allows experts to build ML models with simple scripting and human understandable coding practices. One of the top features of the ML software is that it delivers MLOps to help organizations build, test, and deploy ML models quickly. It also supports graph data and graph neural networks through its Azure Cosmos DB and Azure Databricks services².

- **IBM Machine Learning**: This software is the combination of various products, such as IBM Watson Studio, IBM Watson Machine Learning, IBM Cloud Pak for Data, and IBM Watson Openscale. It provides a comprehensive suite of tools and services for data preparation, model development, deployment, and monitoring. It also supports various frameworks and languages, such as Python, R, TensorFlow, PyTorch, and more. It also offers graph analytics and graph neural networks through its IBM Graph and IBM SNAP ML services¹.

- **PyGeometric**: This is a Python library for deep learning on graphs. It provides various methods and models for graph machine learning, such as graph convolutional networks, graph attention networks, graph autoencoders, and more. It also supports various graph data formats, such as NetworkX, PyTorch Geometric, and DGL².

- **Deep Graph Library (DGL)**: This is another Python library for deep learning on graphs. It provides a unified interface for various graph neural network models, such as graph convolutional networks, graph attention networks, graph autoencoders, and more. It also supports various graph data formats, such as NetworkX, PyTorch Geometric, and PyGeometric².

Source:

(1) How to get started with machine learning on graphs - Medium. https://medium.com/octavian-ai/how-to-get-started-with-machine-learning-on-graphs-7f0795c83763.

(2) Introduction to Graph Machine Learning - Hugging Face. https://huggingface.co/blog/intro-graphml.

(3) Top Free Data Science and Machine Learning Platforms. https://www.g2.com/categories/data-science-and-machine-learning-platforms/free.

(4) Best Graph Plotting and Graphing Software — PlotDigitizer. https://plotdigitizer.com/graphing-software.

What is the difference between graph machine learning and traditional machine learning?

Graph machine learning and traditional machine learning are both branches of artificial intelligence that use data to generate solutions. However, they differ in the type of data they handle, the methods they use, and the problems they can solve. Here are some of the main differences between graph machine learning and traditional machine learning:

- **Data type**: Graph machine learning deals with graph data, which are data that have a network structure of nodes and edges. Graph data can represent complex and heterogeneous relationships among entities, such as social networks, molecules, knowledge graphs, and more. Traditional machine learning deals with non-graph data, which are data that have a regular or tabular structure of features and labels. Non-graph data can represent simple and homogeneous attributes of entities, such as text, images, audio, and more.

- **Methods**: Graph machine learning uses methods that can capture the topological and semantic information of graph data, such as graph signal processing, matrix factorization, random walk, and deep learning. These methods can learn from the local and global patterns of nodes and edges, as well as their attributes and types. Traditional machine learning uses methods that can capture the statistical and numerical information of non-graph data, such as regression, classification, clustering, and dimensionality reduction. These methods can learn from the frequency and distribution of features and labels, as well as their values and categories.

- **Problems**: Graph machine learning can solve problems that involve graph data or require graph representation, such as graph generation, graph evolution, graph level prediction, node property prediction, edge property prediction, and missing edge prediction. These problems can be found in various domains, such as biochemistry, computer vision, natural language processing, and recommender systems. Traditional machine learning can solve problems that involve non-graph data or require non-graph representation, such as regression, classification, clustering, anomaly detection, and sentiment analysis. These problems can be found in various domains, such as finance, marketing, healthcare, and e-commerce.

Source:

(1) Introduction to Graph Machine Learning - Hugging Face. https://huggingface.co/blog/intro-graphml.

(2) IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE, VOL. 00, NO. 0, 2021 1 .... https://arxiv.org/pdf/2105.00696.pdf.

(3) What is the difference between machine learning and traditional .... https://www.scribbr.co.uk/faqs/what-is-the-difference-machine-learning-and-traditional-programming/.

(4) Machine Learning vs. Traditional Programming: A Comprehensive .... https://www.cloudthat.com/resources/blog/machine-learning-vs-traditional-programming-a-comprehensive-comparison-for-optimal-use-cases.

How do I preprocess my data for graph machine learning?

Preprocessing your data for graph machine learning is an important step to ensure the quality and usability of your data. Depending on the type and source of your data, you may need to apply different techniques to prepare your data for graph machine learning. Here are some general steps that you can follow to preprocess your data for graph machine learning:

- **Collect the data**: The first step is to acquire the data that you want to use for graph machine learning. You may have your own data or use publicly available data from various sources, such as web, social media, databases, etc. You need to make sure that your data has a graph structure, which means that it consists of nodes and edges that represent entities and relationships, respectively. You also need to check the format and size of your data, and convert it to a suitable format for graph machine learning, such as NetworkX, PyTorch Geometric, DGL, etc¹².

- **Check for noisy or missing values**: The next step is to inspect your data for any errors, inconsistencies, or missing values that may affect the quality and performance of your graph machine learning model. You may need to use various methods to detect and fix these issues, such as data cleaning, data imputation, data validation, etc³⁴. For example, you may need to remove duplicate or irrelevant nodes or edges, fill in missing node or edge attributes, or validate the accuracy and completeness of your data.

- **Encode the categorical data**: Another step is to encode the categorical data in your graph, such as node or edge types, labels, or properties, into numerical values that can be used by graph machine learning algorithms. You may need to use various methods to encode the categorical data, such as one-hot encoding, label encoding, embedding, etc³⁴. For example, you may need to encode the node types of a social network graph into binary vectors, or embed the node labels of a knowledge graph into low-dimensional vectors.

- **Split the data**: The next step is to split your data into different subsets for training, validation, and testing your graph machine learning model. You may need to use various methods to split your data, such as random sampling, stratified sampling, cross-validation, etc³⁴. For example, you may need to split your data into 80% for training, 10% for validation, and 10% for testing, or use k-fold cross-validation to split your data into k equal folds and use one fold for testing and the rest for training and validation.

- **Scale the data**: Another step is to scale the data in your graph, such as node or edge features, attributes, or weights, into a standard range or distribution that can improve the performance and stability of your graph machine learning model. You may need to use various methods to scale your data, such as normalization, standardization, min-max scaling, etc³⁴. For example, you may need to scale the node features of a molecular graph into a range between 0 and 1, or standardize the edge weights of a citation graph to have zero mean and unit variance.

- **Feature engineering**: The final step is to create or select the features that can capture the relevant information and patterns of your graph data for graph machine learning. You may need to use various methods to engineer the features, such as graph signal processing, graph convolutional networks, graph attention networks, graph autoencoders, etc²⁵. For example, you may need to create node embeddings that represent the node's position and neighborhood in the graph, or select graph centrality measures that indicate the node's importance or influence in the graph.

Source: 

(1) Pre-processing - Neo4j Graph Data Science. https://neo4j.com/docs/graph-data-science/current/machine-learning/pre-processing/.

(2) Data Preprocessing Techniques in Machine Learning [6 Steps] - Scalable Path. https://www.scalablepath.com/data-science/data-preprocessing-phase.

(3) Data Preprocessing in Machine Learning: A Beginner's Guide - Simplilearn. https://www.simplilearn.com/data-preprocessing-in-machine-learning-article.

(4) Data Preprocessing in Machine Learning: 7 Easy Steps To Follow. https://www.upgrad.com/blog/data-preprocessing-in-machine-learning/.

(5) Tour of Data Preparation Techniques for Machine Learning. https://machinelearningmastery.com/data-preparation-techniques-for-machine-learning/.

What are some common graph machine learning algorithms?

Graph machine learning algorithms are methods that can learn from and make predictions on graph data, which are data that have a network structure of nodes and edges. Graph machine learning algorithms can be classified into different types based on the learning style, the input data, and the output task. Here are some common graph machine learning algorithms that you may encounter:

- **Graph signal processing algorithms**: These are algorithms that apply signal processing techniques to graph data, such as filtering, smoothing, sampling, and transforming. They can be used to extract features, enhance signals, or reduce noise from graph data. Some examples of graph signal processing algorithms are graph Fourier transform, graph wavelet transform, graph Laplacian, and graph convolution¹.

- **Graph embedding algorithms**: These are algorithms that map graph data into a low-dimensional vector space, where the nodes or edges are represented by vectors that preserve their structural and semantic information. They can be used to visualize, compare, or cluster graph data. Some examples of graph embedding algorithms are node2vec, DeepWalk, GraphSAGE, and LINE².

- **Graph neural network algorithms**: These are algorithms that use neural networks to learn from and generate graph data, such as graph convolutional networks, graph attention networks, graph autoencoders, and graph generative adversarial networks. They can be used to perform various tasks on graph data, such as node classification, link prediction, graph generation, and graph level prediction²³.

- **Graph mining algorithms**: These are algorithms that discover patterns, rules, or anomalies from graph data, such as frequent subgraphs, graph motifs, graph kernels, and graph outliers. They can be used to understand, summarize, or compress graph data. Some examples of graph mining algorithms are gSpan, gRapH, Graphlet, and LOF⁴..

Source:

(1) Introduction to Graph Machine Learning - Hugging Face. https://huggingface.co/blog/intro-graphml.

(2) 10 Machine Learning Algorithms to Know in 2023 | Coursera. https://www.coursera.org/articles/machine-learning-algorithms.

(3) Machine Learning Algorithms - GeeksforGeeks. https://www.geeksforgeeks.org/machine-learning-algorithms/.

(4) 19 Graph Algorithms You Can Use Right Now. https://memgraph.com/blog/graph-algorithms-list.

(5) en.wikipedia.org. https://en.wikipedia.org/wiki/Machine_learning.

How do I choose the right graph machine learning algorithm for my data?

Choosing the right graph machine learning algorithm for your data depends on several factors, such as the type and size of your data, the goal and complexity of your task, the available computational resources, and the desired performance and interpretability of your model. Here are some general steps that you can follow to choose the right graph machine learning algorithm for your data:

- **Understand your problem**: The first step is to define your problem clearly and identify what kind of task you want to perform on your graph data. Is it a supervised or unsupervised learning problem? Is it a classification, regression, clustering, or generation problem? Is it a node-level, edge-level, or graph-level problem? What are the inputs and outputs of your problem? What are the evaluation metrics and criteria for your problem?

- **Analyze your data**: The next step is to explore and understand your data. What is the structure and size of your graph? How many nodes and edges are there? What are the types and attributes of the nodes and edges? How dense or sparse is the graph? How homogeneous or heterogeneous is the graph? How dynamic or static is the graph? How noisy or clean is the data? How is the data distributed and labeled?

- **Evaluate the speed and training time**: Another step is to consider the speed and training time of the algorithm. How fast do you need the algorithm to run? How much time do you have to train the algorithm? How complex is the algorithm? How scalable is the algorithm? How much memory and computational power do you have? How often do you need to update the model?

- **Find out the linearity of your data**: Another step is to determine the linearity of your data. How linear or nonlinear is the relationship between the features and the target variable? How linear or nonlinear is the structure of the graph? How well can the data be separated or clustered by a linear model? How much flexibility or complexity do you need in the model?

- **Decide on the number of features and parameters**: The final step is to decide on the number of features and parameters of the algorithm. How many features do you have in your data? How many features do you need to use for your task? How many parameters do you have in your algorithm? How many parameters do you need to tune for your task? How do you select and optimize the features and parameters?

Source:

(1) Which machine learning algorithm should I use? - The SAS Data Science Blog. https://blogs.sas.com/content/subconsciousmusings/2020/12/09/machine-learning-algorithm-use/.

(2) How to Choose Right Machine Learning Algorithm? - GeeksforGeeks. https://www.geeksforgeeks.org/choosing-a-suitable-machine-learning-algorithm/.

(3) How to Choose a Machine Learning Algorithm - Label Your Data. https://labelyourdata.com/articles/how-to-choose-a-machine-learning-algorithm.

(4) An Easy Guide to Choose the Right Machine Learning Algorithm. https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html.

(5) How to select the right machine learning algorithm - Telus International. https://www.telusinternational.com/insights/ai-data/article/how-to-select-the-right-machine-learning-algorithm.

What are some challenges of graph machine learning?

Graph machine learning is a branch of artificial intelligence that deals with data that have a network structure of nodes and edges, such as social networks, biological networks, knowledge graphs, and more. Graph machine learning can perform various tasks on graph data, such as classification, link prediction, generation, and analysis. However, graph machine learning also faces some challenges that make it difficult to apply and generalize to different domains and scenarios. Some of these challenges are:

- **Dynamic and evolving graphs**: Many real-world graphs are not static, but change over time due to the addition or deletion of nodes and edges, or the update of node and edge attributes. This poses a challenge for graph machine learning, as it requires the models to adapt to the temporal dynamics and capture the temporal patterns of the graph data. Moreover, some graphs may have unknown or incomplete structures, which require the models to infer or estimate the missing or latent parts of the graph¹.

- **Learning with edge signals and information**: Most graph machine learning methods focus on learning from the node features and labels, while ignoring or simplifying the edge features and labels. However, edges can also carry important information and signals that can enhance the performance and interpretability of the models. For example, edges can have different types, weights, directions, or attributes that indicate the nature and strength of the relationships between nodes. Learning with edge signals and information can help the models to better understand the graph structure and semantics¹.

- **Graph estimation**: Graph estimation is the problem of inferring or generating a graph from non-graph data, such as text, images, audio, or video. Graph estimation can be useful for transforming non-graph data into graph data, which can then be processed by graph machine learning methods. However, graph estimation is also challenging, as it requires the models to extract the relevant entities and relationships from the non-graph data, and to construct a graph that preserves the structural and semantic information of the non-graph data¹.

- **Generalization of graph models**: Generalization of graph models is the problem of applying or transferring a graph machine learning model trained on one graph to another graph that has a different structure or domain. Generalization of graph models can be beneficial for leveraging the knowledge and experience learned from one graph to another graph, especially when the target graph has limited or no labeled data. However, generalization of graph models is also challenging, as it requires the models to overcome the heterogeneity and diversity of graph data, and to adapt to the new graph characteristics and tasks¹.

Source: 

(1) Graph Deep Learning: State of the Art and Challenges. https://ieeexplore.ieee.org/document/9339909.

(2) Graph Learning: A Survey | IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/document/9416834.

(3) An Overview of Graph Machine Learning and Its Working - Analytics Vidhya. https://www.analyticsvidhya.com/blog/2022/10/an-overview-of-graph-machine-learning-and-its-working/.

(4) https://ieeexplore.ieee.org/servlet/opac?punumber=6287639.

(5) https://ieeexplore.ieee.org/servlet/opac?punumber=9078688.

Which software has better support for unsupervised learning on graphs?

There is no definitive answer to which software has better support for unsupervised learning on graphs, as different software may have different features, advantages, and limitations. However, based on the web search results, some of the software that are designed for unsupervised learning on graphs or can handle unsupervised learning on graphs are:

- **SUBLIME**: This is a novel framework that uses self-supervised contrastive learning and bootstrapping to optimize the graph structure without any external guidance (i.e., labels). It can generate high-quality graphs that are consistent with the original data and can be used for various downstream tasks¹.

- **PyOD**: This is a comprehensive and scalable Python library for outlier detection (anomaly detection) on graphs and other types of data. It provides various methods and models for unsupervised learning on graphs, such as graph convolutional networks, graph autoencoders, and more².

- **SimCLR**: This is a framework that uses contrastive learning to learn representations from unlabeled images. It can also be extended to graph data by using graph convolutional networks as the encoder and graph contrastive loss as the objective function³.

- **MMSelfSup**: This is an open-source toolbox and benchmark for self-supervised learning on graphs and other types of data. It provides various methods and models for unsupervised learning on graphs, such as graph convolutional networks, graph attention networks, graph autoencoders, and more⁴.

- **Anomalib**: This is an anomaly detection library that comprises state-of-the-art algorithms and features for unsupervised learning on graphs and other types of data. It provides various methods and models for unsupervised learning on graphs, such as graph convolutional networks, graph autoencoders, and more⁵.

Source: 

(1) [2201.06367] Towards Unsupervised Deep Graph Structure Learning - arXiv.org. https://arxiv.org/abs/2201.06367.

(2) Top 23 unsupervised-learning Open-Source Projects (Oct 2023) - LibHunt. https://www.libhunt.com/topic/unsupervised-learning.

(3) Top 23 unsupervised-learning Open-Source Projects (Oct 2023) - LibHunt. https://bing.com/search?q=best+software+for+unsupervised+learning+on+graphs.

(4) Introduction to Unsupervised Learning - DataCamp. https://www.datacamp.com/blog/introduction-to-unsupervised-learning.

(5) https://doi.org/10.48550/arXiv.2201.06367.

(6) https://github.com/facebookresearch/moco.

What are some applications of unsupervised learning on graphs?

Unsupervised learning on graphs is a branch of machine learning that deals with learning from graph data without any labels or supervision. Graph data are data that have a network structure of nodes and edges, such as social networks, biological networks, knowledge graphs, and more. Unsupervised learning on graphs can perform various tasks on graph data, such as clustering, visualization, dimensionality reduction, finding association rules, and anomaly detection. Some of the applications of unsupervised learning on graphs are:

- **Clustering**: Clustering is the task of grouping similar nodes or subgraphs based on their features or structures. Clustering can be used to discover communities, segments, or patterns in graph data. For example, clustering can be used to find groups of users with similar interests or behaviors in social networks, or to identify functional modules or pathways in biological networks¹².

- **Visualization**: Visualization is the task of projecting graph data into a low-dimensional space that can be easily displayed and interpreted. Visualization can be used to explore, understand, or communicate graph data. For example, visualization can be used to show the structure and properties of graph data, such as the node degree distribution, the edge weight distribution, or the node centrality measures¹².

- **Dimensionality reduction**: Dimensionality reduction is the task of reducing the number of features or dimensions of graph data while preserving the essential information or relationships. Dimensionality reduction can be used to improve the efficiency, performance, or interpretability of graph data. For example, dimensionality reduction can be used to compress or simplify graph data, or to extract latent or meaningful features from graph data¹².

- **Finding association rules**: Finding association rules is the task of discovering rules or patterns that describe the relationships or dependencies among nodes or edges in graph data. Finding association rules can be used to infer or explain graph data. For example, finding association rules can be used to discover causal or correlational relationships among nodes or edges, or to generate hypotheses or recommendations from graph data¹².

- **Anomaly detection**: Anomaly detection is the task of identifying nodes or edges that deviate from the normal or expected behavior or pattern in graph data. Anomaly detection can be used to detect or prevent graph data. For example, anomaly detection can be used to find outliers, errors, frauds, or attacks in graph data¹²⁴.

Source: 

(1) [2309.02762] Towards Unsupervised Graph Completion Learning on Graphs .... https://arxiv.org/abs/2309.02762.

(2) [2201.06367] Towards Unsupervised Deep Graph Structure Learning - arXiv.org. https://arxiv.org/abs/2201.06367.

(3) Real-world Applications of Unsupervised Learning. https://pythonistaplanet.com/applications-of-unsupervised-learning/.

(4) Unsupervised Learning with Graph Neural Networks - IPAM. https://www.ipam.ucla.edu/abstract/?tid=15546.

(5) https://doi.org/10.48550/arXiv.2309.02762.

(6) https://doi.org/10.48550/arXiv.2201.06367.

💥💥💥 What is PrePoMAX and what is used for?

 PrePoMAX is an open-source pre and post-processor for the Calculix FEM solver¹. FEM stands for finite element method, which is a numerical technique for solving problems in engineering and physics². PrePoMAX allows you to import, mesh, define and visualize FEM features using the Calculix solver¹. It supports various CAD formats, meshing methods, feature definitions and result formats¹. You can use PrePoMAX to perform simulations of structural mechanics, heat transfer, fluid dynamics and more³. PrePoMAX is free software that you can download and modify under the terms of the GNU General Public License². PrePoMAX is developed by Dr. Matej Borovinsek from the University of Maribor in Slovenia⁴. PrePoMAX is praised for its user-friendly, modern and easy-to-use graphical interface⁴.

Source:

(1) PrePoMax - UM. https://prepomax.fs.um.si/.

(2) Downloads – PrePoMax - UM. https://prepomax.fs.um.si/downloads/.

(3) PrePoMax. Najlepszy darmowy program do analiz MES? [test]. https://polskiprzemysl.com.pl/cam-cad-cae/darmowy-program-do-analiz-mes/.

(4) http://www.dhondt.de/calculix_2.20_4win.zip.

(5) https://gitlab.com/MatejB/PrePoMax.

How do I install PrePoMAX?

To install PrePoMAX, you need to follow these steps:

- Download the latest version of PrePoMAX from the official website¹.

- Extract the zip file to a folder of your choice.

- Run the setup.exe file and follow the instructions on the screen.

- Choose the installation directory and the components you want to install.

- After the installation is complete, you can launch PrePoMAX from the Start menu or the desktop shortcut.

You can also refer to the PrePoMAX manual² for more details and troubleshooting. I hope this helps. 😊

Source: 

(1) Documentation – PrePoMax. https://prepomax.fs.um.si/documentation/.

(2) PrePoMax v1.1.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2021/06/2021.06.29-PrePoMax-v1.1.0-manual.pdf.

(3) PrePoMax 1.3.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/04/PrePoMax-v1.3.0-manual.pdf.

(4) PrePoMax 1.3.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/04/PrePoMax-v1.3.0-manual_updated.pdf.

(5) https://staff.tu.kielce.pl/rokach/instr

What are capabilities of PrePoMAX ?

PrePoMAX is a powerful software that can help you perform various types of simulations using the Calculix FEM solver. Some of the capabilities of PrePoMAX are:

- Importing and exporting CAD geometry and mesh files in different formats¹.

- Meshing solid and shell geometries using linear and parabolic finite elements¹².

- Defining material properties, constraints, interactions, initial conditions, boundary conditions and loads for the analysis².

- Setting up different types of analysis steps, such as static, natural frequency, linear buckling, heat transfer, coupled temperature-displacement and slip wear².

- Visualizing the results using 3D scalar fields or 2D plots¹².

PrePoMAX is an open-source software that you can download and modify for free¹². It has a user-friendly, modern and easy-to-use graphical interface¹ . PrePoMAX is developed by Dr. Matej Borovinsek from the University of Maribor in Slovenia.

Source: 

(1) PrePoMax - UM. https://prepomax.fs.um.si/.

(2) PrePoMax 1.3.5 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/10/PrePoMax-v1.3.5-manual.pdf.

(3)https://prepomax.discourse.group/.

Can I use PrePoMAX for fluid dynamics simulations?

PrePoMAX is mainly designed for structural mechanics and heat transfer simulations, but it can also be used for fluid dynamics simulations in some cases. According to the PrePoMAX manual², you can use PrePoMAX to set up a steady state dynamic analysis or an explicit/implicit dynamic analysis with fluid elements. However, you need to have some knowledge of the Calculix solver and the input file syntax to perform these types of analysis. PrePoMAX does not provide a graphical interface for defining fluid properties, boundary conditions and loads for fluid elements. You need to edit the input file manually or use a text editor to add these definitions. PrePoMAX can only visualize the results of fluid dynamics simulations using 2D plots, not 3D scalar fields. Therefore, PrePoMAX is not the most suitable software for fluid dynamics simulations, but it can be used as a tool to create and modify the mesh and the input file for the Calculix solver. 

Source:

(1) Features – PrePoMax - UM. https://prepomax.fs.um.si/features/.

(2) https://staff.tu.kielce.pl/rokach/instr/ppm

(3) Documentation – PrePoMax. https://prepomax.fs.um.si/documentation/.

(4) PrePoMax - UM. https://prepomax.fs.um.si/.

What is the difference between PrePoMAX and Calculix?

PrePoMAX and Calculix are two different but related software for finite element analysis. The main difference between them is that PrePoMAX is a pre and post-processor, while Calculix is a solver. This means that PrePoMAX is used to create, modify and visualize the FEM models, while Calculix is used to perform the actual calculations and simulations. PrePoMAX is based on a modern user interface that allows you to import, mesh, define and visualize FEM features using the Calculix solver¹. Calculix is a command-line based program that requires an input file with the FEM definitions and parameters². PrePoMAX can generate and edit the input file for Calculix, as well as display the results in 3D scalar fields or 2D plots¹². PrePoMAX and Calculix are both open-source software that you can download and modify for free¹²³. PrePoMAX is developed by Dr. Matej Borovinsek from the University of Maribor in Slovenia, while Calculix is developed by Guido Dhondt and Klaus Wittig from Germany¹². PrePoMAX and Calculix are compatible with each other and can be used together to perform various types of simulations, such as structural mechanics, heat transfer, fluid dynamics and more¹²³.

Source: 

(1) PrePoMax - UM. https://prepomax.fs.um.si/.

(2) Features – PrePoMax - UM. https://prepomax.fs.um.si/features/.

(3) PrePoMax v1.1.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2021/06/2021.06.29-PrePoMax-v1.1.0-manual.pdf.

What are the system requirements for PrePoMAX and Calculix?

According to the PrePoMAX website¹, the system requirements for PrePoMAX are:

- Microsoft .NET Framework 4.8 or higher

- Windows 7 or higher

- 64-bit processor

- 4 GB of RAM or more

- 500 MB of disk space or more

- OpenGL 3.3 or higher compatible graphics card

The system requirements for Calculix are:

- Windows, Linux or MacOS operating system

- 64-bit processor

- 4 GB of RAM or more

- 500 MB of disk space or more

- GNU Fortran compiler or Intel Fortran compiler

PrePoMAX and Calculix are both open-source software that you can download and modify for free¹²³. PrePoMAX includes a sub-directory with the Calculix solver, so you do not need to install Calculix separately if you use PrePoMAX². However, you can also use Calculix independently or with other pre and post-processors, such as Gmsh, Salome or Paraview³.

Source: 

(1) PrePoMax - UM. https://prepomax.fs.um.si/.

(2) Downloads – PrePoMax - UM. https://prepomax.fs.um.si/downloads/.

(3) PrePoMax 1.3.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2022/04/PrePoMax-v1.3.0-manual.pdf.

(4) http://www.dhondt.de/calculix_2.20_4win.zip.

(5) https://gitlab.com/MatejB/PrePoMax.

What are main advantages of PrePoMAX vs commercial software?

Some of the main advantages of PrePoMAX vs commercial software are:

- PrePoMAX is **free** and **open-source**, which means you can download and modify it without any cost or license restrictions¹².

- PrePoMAX is based on a **modern user interface** that allows you to import, mesh, define and visualize FEM features using the Calculix solver². Commercial software may have outdated or complex interfaces that require more training and expertise.

- PrePoMAX supports various **CAD formats**, **meshing methods**, **feature definitions** and **result formats** that are compatible with other open-source software, such as Open Cascade, Netgen, Gmsh, Salome and Paraview². Commercial software may have limited or proprietary formats that are not interoperable with other software.

- PrePoMAX is **user-friendly**, **easy-to-use** and **fast**. It has a simple and intuitive workflow that allows you to perform various types of simulations, such as structural mechanics, heat transfer, fluid dynamics and more². Commercial software may have more features and capabilities, but they may also be more difficult to use and slower to run.

Source: 

(1) Affordable FEA Packages for Home/Non-Commercial Use : r/fea - Reddit. https://www.reddit.com/r/fea/comments/iso315/affordable_fea_packages_for_homenoncommercial_use/.

(2) PrePoMax - UM. https://prepomax.fs.um.si/.

(3) Best open source FE : r/fea - Reddit. https://www.reddit.com/r/fea/comments/sje9q0/best_open_source_fe/.

What are the limitations of PrePoMAX?

PrePoMAX is a great software for finite element analysis, but it also has some limitations that you should be aware of. Some of the limitations of PrePoMAX are:

- PrePoMAX is mainly designed for structural mechanics and heat transfer simulations, but it can also be used for fluid dynamics simulations in some cases. However, you need to have some knowledge of the Calculix solver and the input file syntax to perform these types of analysis. PrePoMAX does not provide a graphical interface for defining fluid properties, boundary conditions and loads for fluid elements. You need to edit the input file manually or use a text editor to add these definitions. PrePoMAX can only visualize the results of fluid dynamics simulations using 2D plots, not 3D scalar fields¹.

- PrePoMAX does not support nonlinear material models, such as plasticity, creep, damage and hyperelasticity. You need to use Calculix directly or another pre and post-processor to perform simulations with these material models².

- PrePoMAX does not support contact interactions between different parts or surfaces. You need to use Calculix directly or another pre and post-processor to perform simulations with contact interactions².

- PrePoMAX does not support parallel processing or distributed computing. You need to use Calculix directly or another solver to perform simulations with multiple processors or clusters².

- PrePoMAX is still under development and may have some bugs or errors. You need to check the validity and accuracy of your results and report any issues or feedback to the developers².

Source

(1) PrePoMax v1.1.0 Manual - UM. https://prepomax.fs.um.si/wp-content/uploads/2021/06/2021.06.29-PrePoMax-v1.1.0-manual.pdf.

(2) PrePoMax - UM. https://prepomax.fs.um.si/.

BUT PREPOMAX IS STILL IN DEVELOPMENT MODE SO SOME NEW CAPABILITIES CAN EXIST IN NEXT VERSIONS OF SOFTWARE , SO FOLLOW  MATEJ AND PREPOMAX ON LINKEDIN TO BE IN TOUCH.