# Simulation Accelerator Toolbox

**Source**: https://www.fsd.ed.tum.de/software/simaccel/
**Parent**: https://www.fsd.ed.tum.de/staff-members/johann-dambeck/

[Download (GitLab)](https://gitlab.com/tum-fsd/simaccel)

[Changelog](https://gitlab.com/tum-fsd/simaccel/-/blob/main/CHANGELOG)

[Manual](https://gitlab.com/tum-fsd/simaccel/-/blob/main/MANUAL.pdf)

[License](https://gitlab.com/tum-fsd/simaccel/-/blob/main/LICENSE.md)

[Publication](https://doi.org/10.2514/6.2021-1980)

This toolbox presents an abstraction layer that can be put in front of our [Subset Simulation Toolbox](https://www.fsd.ed.tum.de/software-new/subsetsim/) to simplify and speed up subset simulations with *Simulink* models. In short, it contains a **custom code generation and compilation process** that produces a binary simulation target that is optimised for speed in the context of subset simulation.

The details of the inner workings and architecture are captured in the user manual and publication linked above. In short summary, it might give you **5–15⨉ faster simulation results** when you iterate Monte Carlo samples than what you are able to achieve using the standard simulation tools.

## Features

### Simulation target optimised for speed

Contains our `CompiledScenario` class that produces a binary simulation target that is optimised for speed when used together with our [Subset Simulation toolbox](https://www.fsd.ed.tum.de/software-new/subsetsim/).

### Easy setup of subset simulation studies

Define a simulation `Scenario`, a list of `Parameter` objects and a failure `Metric`, then join them in a subset simulation `Study` that is ready to start.

### Iterative build artifact generation

Our custom code generation procedure uses an iterative Makefile-like approach. To regenerate certain artifacts, just delete them and start the process again.

### Support for many official toolbox features

Use embedded or external test harnesses, load scenario configuration from *Test Manager* files, automatically discretise models, and automatically select the best call signature of `sim()` and `parsim()`. If you are familiar with the official APIs, you should be able to get started quickly.

## Case Studies

### Speeding up simulation of an eVTOL

In this study (DOI link below), the authors compared simulation times for an eVTOL simulation model of comparative modelling complexity as would be needed for requirement verification. The code generation toolchain available in this toolbox (in the table named “proposed”) had a comparable setup time as the other simulation modes, but execution time was only a fraction, leading to a simulation speedup of 73⨉ in total, or 5⨉ above the fastest built-in simulation mode (rapid accelerator).

[doi:10.2514/6.2021-1980](https://doi.org/10.2514/6.2021-1980)

## Contact

If you have questions, feedback, bug reports or issues regarding licensing, please contact us at: