OpenSeesRT

Nonlinear finite element analysis.

OpenSeesRT is a framework that provides an intuitive API for nonlinear finite element analysis, implemented in C++ through the OpenSees framework. OpenSees features state-of-the-art finite element formulations and solution algorithms, including mixed formulations for beams and solids, over 200 material models, and an extensive collection of continuation algorithms to solve highly nonlinear problems.

This package may be used as a drop-in replacement for both OpenSees.exe and OpenSeesPy (see Getting Started below), and generally provides a substantial performance boost.

This package is experimental and not yet intended for public use.

Note

This package is independent of the openseespy library, which is documented in the OpenSees documentation website.

Getting Started

The sees package can be installed into a Python environment in the standard manner. For example, using pip:

pip install sees

There are several ways to use the sees package:

• To execute Tcl procedures from a Python script, just create an instance of the sees.Model class and call its eval() method:

  model = sees.Model()
  model.eval("model Basic -ndm 2")
  model.eval("print -json")

• To start an interactive interpreter run the shell command:

  python -m opensees

  To quit the interpreter, just run exit:

  opensees > exit

• The sees package exposes a compatibility layer that exactly reproduces the OpenSeesPy functions, but does so without mandating a single global program state. To run OpenSeesPy scripts, just change the import:

  import openseespy.opensees

  to

  import opensees.openseespy

  For true stateless modeling, the Model class should be used instead of the legacy model function; see the documentation here.

Development

To compile the project see about/compiling

See also

• osmg OpenSees Model Generator
• mdof Optimized system identification library
• sdof Optimized integration for single degree of freedom systems

For more projects by the STAIRlab, visit https://github.com/STAIRlab .

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