How to leverage the Functional Mock-up Interface (FMI) for Model Based System...Siemens PLM Software
This presentation focusses on the use cases and motivations behind FMI and provide some tips on when to use Model Exchange or Co-Simulation. It illustrates how FMI helps covering all the phases of product design in a scalable way by connecting LMS Imagine.Lab Amesim™ models with models of various levels of detail such as 3D/MBS tools or advanced FEA or CFD codes. Parallelized heterogeneous co-simulation of Functional Mock-up Units (FMUs) is described from MiL, through SiL, towards HiL applications, for instance on recent FMI compliant multiprocessor real-time targets. The use of “surrogate FMUs” for controls validation, or for evaluating global product performance attributes such as Vehicle Fuel Economy is discussed. Then it is explained why FMI enables the management of complex product architectures and their associated scenarios at high level, and how this can be achieved thanks to Siemens PLM Software's LMS Imagine.Lab product family. Lastly, Siemens PLM Software provides its view and perspectives on promising evolutions of the FMI standard.
The document provides an overview of the OPTIMICA Compiler Toolkit. It discusses the toolkit's capabilities for transient and steady-state time domain simulation and optimization. It also outlines Modelon's model-based development workflow using model libraries, authoring, compilers, solvers, and other technologies. Key features of the toolkit include Modelica and FMI-based computation, dynamic and steady-state simulation, optimization capabilities, and scripting APIs.
A framework for nonlinear model predictive controlModelon
This document presents a new framework for nonlinear model predictive control (MPC) in JModelica.org. MPC is an optimal control strategy that solves an optimal control problem repeatedly online to determine the optimal inputs. The new framework aims to improve the computational efficiency of MPC in JModelica.org by reusing information between optimizations. It discretizes the optimal control problem once, rather than at each iteration. In a benchmark of controlling a combined cycle power plant, the new MPC framework achieved similar control performance as the existing open-loop framework but was significantly faster, taking around 70% less total time. The MPC framework also makes the JModelica.org modeling environment easier to use for MPC applications.
Using FMI (Functional Mock-up Interface) for MBSE at all steps of System DesignSiemens PLM Software
This presentation describes several FMI use-cases addressed by LMS Imagine.Lab Amesim covering all the phases of MBSE.
For more information, please visit our website: www.siemens.com/plm/simcenter-amesim
How to leverage the Functional Mock-up Interface (FMI) for Model Based System...Siemens PLM Software
This presentation focusses on the use cases and motivations behind FMI and provide some tips on when to use Model Exchange or Co-Simulation. It illustrates how FMI helps covering all the phases of product design in a scalable way by connecting LMS Imagine.Lab Amesim™ models with models of various levels of detail such as 3D/MBS tools or advanced FEA or CFD codes. Parallelized heterogeneous co-simulation of Functional Mock-up Units (FMUs) is described from MiL, through SiL, towards HiL applications, for instance on recent FMI compliant multiprocessor real-time targets. The use of “surrogate FMUs” for controls validation, or for evaluating global product performance attributes such as Vehicle Fuel Economy is discussed. Then it is explained why FMI enables the management of complex product architectures and their associated scenarios at high level, and how this can be achieved thanks to Siemens PLM Software's LMS Imagine.Lab product family. Lastly, Siemens PLM Software provides its view and perspectives on promising evolutions of the FMI standard.
The document provides an overview of the OPTIMICA Compiler Toolkit. It discusses the toolkit's capabilities for transient and steady-state time domain simulation and optimization. It also outlines Modelon's model-based development workflow using model libraries, authoring, compilers, solvers, and other technologies. Key features of the toolkit include Modelica and FMI-based computation, dynamic and steady-state simulation, optimization capabilities, and scripting APIs.
A framework for nonlinear model predictive controlModelon
This document presents a new framework for nonlinear model predictive control (MPC) in JModelica.org. MPC is an optimal control strategy that solves an optimal control problem repeatedly online to determine the optimal inputs. The new framework aims to improve the computational efficiency of MPC in JModelica.org by reusing information between optimizations. It discretizes the optimal control problem once, rather than at each iteration. In a benchmark of controlling a combined cycle power plant, the new MPC framework achieved similar control performance as the existing open-loop framework but was significantly faster, taking around 70% less total time. The MPC framework also makes the JModelica.org modeling environment easier to use for MPC applications.
Using FMI (Functional Mock-up Interface) for MBSE at all steps of System DesignSiemens PLM Software
This presentation describes several FMI use-cases addressed by LMS Imagine.Lab Amesim covering all the phases of MBSE.
For more information, please visit our website: www.siemens.com/plm/simcenter-amesim