DAE Tools is cross-platform equation-oriented process modelling, simulation, and optimization software. Various types of processes (lumped or distributed, steady-state or dynamic) can be modelled and optimized. They may range from very simple to those which require complex operating procedures. Equations can be ordinary or discontinuous, where discontinuities are automatically handled by the framework. Model reports containing all information about a model can be exported in XML MathML format, automatically creating high-quality documentation. The simulation results can be visualized, plotted, and/or exported into various formats.
|Tags||Simulation Optimization Visualization Scientific/Engineering computing modelling|
|Operating Systems||GNU/Linux Windows MacOS X|
Release Notes: This release adds integration speed improvements (no need for a memory copy from/to the DAE solver, better integration step control, and an option to avoid sparse matrix recreations after a discontinuity), adds support for units, variables, and parameters, adds animated 2D plots, lets equations have optional scaling, improves data reporting speed, adds a new distribution format (Python disutils), includes a Mac OS X port, supports information about the progress of simulation/optimization activity, and adds other small improvements and minor bugfixes.
Release Notes: This release has a new type of ports (Event ports) and a new function (ON_EVENT) in the daeModel class that specifies how the incoming events on a specific event port are handled. A new way of handling state transitions: the function ON_CONDITION in daeModel that specifies actions to be undertaken when the logical condition is satisfied. Non-linear least square minimization (the scipy wrapper of Minpack). Examples of DAE Tools and Scipy interoperability (scipy.optimize). Shell scripts to compile third party libraries and DAE Tools modules. Tutorials are available in C++ (cDAE). Several small bugfixes and enhancements.
Release Notes: New linear solvers were added: Standalone SuperLU_MT (multithreaded sparse direct), Trilinos AztecOO (iterative Krylov; Ifpack, ML, or built-in preconditioners), and NVidia CUDA enabled (experimental): CUSP (iterative Krylov), SuperLU_CUDA (sparse direct). New NLP solvers were added: NLOPT (from the Massachusetts Institute of Technology) and a Standalone IPOPT solver. Models can now be exported to pyDAE and cDAE. A new data reporter exports results in the Matlab MAT file format.
Release Notes: Optimization of steady-state and dynamic processes (IPOPT/Bonmin) was implemented. Several new features were added. Bugs were fixed.