The smartest and fastest post-processing environment with which simulation engineers can concentrate on their most valuable tasks, not just on moving data around or repeating time-consuming jobs.
It is compatible with NASTRAN, ABAQUS, ANSYS and OPTISTRUCT.
Thanks to NaxTo things have changed in post-processing and can be done better and faster, taking it to the next level with advanced tools, intelligent reporting, and automation of tedious tasks.
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It is the 3D Viewer to graphically visualize your FEA models. It allows model tree selection, filtering, multi-window, scripting, advanced label creation, Free Body Forces and Moments, Derived Results, Results Animation, Attributes, 3D PDF generation with 3D views of your FEM models inside and much more.
It is connected seamlessly with the rest of the NaxTo modules.
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Intelligent image extraction to MS Word, PowerPoint and Excel. NaxToDoc saves information of any image captured and uses it to automate generation and update all images in batch.
Never lose again the time of taking a screenshot if anything changes!
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Link directly your results files with Excel cells, extracting your results for fast reporting with direct and easy functions with one click from inside Excel. Automate extraction of results and updates of all your data if any changes in your results files.
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It is NaxTo's powerful Python library for reading data from the result files.
Keep the same code syntax for all solvers, and store data in a simple Python framework. It can be easily combined with other Python packages such as Matplotlib or Pandas for Visualization or Exporting to Excel format.
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Extract element flows in the material axis for different load cases using a graphical user interface with NaxToPy
Scripting IDE: a powerful Python scripting tool to simplify the automation of repetitive tasks
Fasteners Evaluation and Reporting with NaxToPy and Jupyter Notebook
Free Body With NaxTo
ESA (European Space Agency) is backing Idaero solutions through ESA BIC Madrid Region.
The contract of one of the developers is subject to funding from EMPLEA 2016, from the National Programme for Talent Promotion and Employability, included in National Plan for Scientific Investigation, Technological and Innovation 2013-2016, from the Ministry of Economy and Competitiveness.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 728569.