ONSAS: an Open Nonlinear Structural Analysis Solver for GNU-Octave/Matlab

About ONSAS

ONSAS is a GNU-Octave/Matlab code for static/dynamic and linear/non-linear analysis of structures. The first version was developed for educational purposes and was published in a handbook of the course Análisis no lineal de Estructuras taught at Facultad de Ingeniería, Universidad de la República since 2017.

Some examples

A deployable ring

A propeller model

A truss tower model

Contact

You can send an e-mail to jorgepz [AT] fing.edu.uy .

License

The code is distributed under a GNU-GPL 3.0 license.

How to use ONSAS

The user should follow these steps to install and run onsas:

Download and install GNU-Octave and Paraview
Download the ONSAS zip source files from the realeases web https://github.com/ONSAS/ONSAS/releases
Open GNU-Octave, move to the examples directory and run one of the examples.

An introduction to using and contributing to ONSAS was presented in 2022. The recording is available at this youtube video.

Contributions

The complete list of authors of code, contributions, affiliations and acknowledgments is available in the documentation.

ONSAS's Projects

abaqusreader.jl

AbaqusReader.jl is a parse for ABAQUS FEM models. It's capable of parsing the geometry accurately, including surface sets, node sets, and other relevant geometrical data used in FEM calculations. Other option is to parse whole model, including boundary conditions, material data and load steps.

hasde

Interactive Learning Tool - Slope Deflection Method

libroanle

Códigos fuente de libro Análisis No Lineal de Estructuras

onsas

An Open Nonlinear Structural Analysis Solver for GNU-Octave or Matlab

onsas.docs

The documentation repo of the ONSAS solver

onsas.jl

An Open Nonlinear Structural Analysis Solver in Julia

packages

An index for GNU Octave packages.

plastaf

PLastic Analysis of Sections, Trusses and Frames

solid-mechanics-ml

This repository contains code for a project that trains a neural network to solve solid mechanics problems faster than the traditional finite element method. It includes a pipeline for generating a database of FEM solutions and experiments comparing the neural network model to the FEM.