System Simulation (or Systems Modeling and Simulation) is the ultimate arbiter of whether a product is meeting its given performance objectives. As such, System Simulation incorporates the end effects of all sub-systems and component in determining overall product performance. Traditionally, in Engineering Design and Simulation circles, System Simulation has been synonymous to Model Based Systems Engineering (MBSE), which INCOSE defines as a “formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.” However, in recent years with the popularizing of the Digital Twin and the Internet of Things (IoT), MBSE is now viewed as a component of System Simulation and not its totality. In addition to MBSE, System Simulation now includes items such the methods for combining different data sources (physics based predictions, test data, field data, etc,) that are statistically analyzed (Bayesian Information Fusion) to make a business decision (as opposed to simply an engineering design decision). It has also expanded its reach to include not only engineering design decision, but the entire product experience lifecycle including, product conception, manufacturing, field usage, etc.
In the end, it all comes down to the system performance of a given system or systems of systems. The components may be optimized, but if the system architecture is wrong, or your control software is buggy, the product will flop. Simulation is the perfect tool for system design and system verification and validation.Here, you’ll access the latest news, articles, presentations, videos, webinars, and success stories related to system simulation
Open standards enable innovation. Systems need standards in order to connect models from different domains into the cyber-physical system. Check out the Modelica language and the FMI Standard.
An ever larger value in a product comes from software. It can be for controls, autonomy, or in the cloud as part of the Internet of Things. For mobility, the leading software stack is AUTOSAR.
Modelon’s newest library, the Jet Propulsion Library, provides the foundation for modeling and simulating jet engines along with the integrated model-based systems engineering of aircraft systems.View Summary
The prime mover of an aircraft, the jet engine, is one of the most important subsystems of an aircraft. Jet engines provide primary power (thrust) and secondary power (to drive flight control, air conditioning, cabin lighting, etc.) to the aircraft. Performance and efficiency improvements are becoming increasingly difficult to achieve when focusing on the engine in isolation. In the past, engine improvements have largely been developed separately in incremental design cycles – these cycles have not considered the need to develop the total aircraft package as an entire unit. Today, aeronautical systems and their subsystems are becoming more and more integrated and therefore simulation models need to become further aligned to meet performance and efficiency needs.View Blog Post
Unified model representation enhances knowledge-capture efficiency across multiple disciplinesView SummaryGet Content
Using simulation in product development is nothing new. There are many simulation tools that help users solve practical issues.
Beyond achieving success with important singular issues, engineers and their companies have been trying to achieve a global solution at a system level. This type of solution encompasses subsystems or components from domains such as mechanical, electrical, thermal and controls, and includes embedded software.View Blog Post
The arrival of the Functional-Mockup-Interface (FMI) has been a game changer by simplifying the combination of models from different tools.View SummaryGet Content
Most of us know by now that there is not one single software tool that will solve all our simulation needs. Even if we narrow it down to system simulation, we will not find that One tool.
Not so long ago, handling different aspects of product design with separate tools was not a major problem. But as competitive pressure builds to get products to market faster, the game is changing.
A major key to success is being able to integrate subsystems into system simulations early in the design process. This enables organizations to understand and assess system behavior, as opposed to component or subsystem behavior, to avoid flaws that become costly to correct in later stages.View Article
Executable requirements speed up the system level verification and validation processView Summary
This blog promotes the idea of connecting MBSE and MBD more seamlessly than was previously possible. Using recent results from the ITEA3 EU project MODRIO, a Modelica Library prototype to represent executable requirements is now available, and we have used this library to demonstrate the workflow to develop a simplified version of a vehicle thermal management control system to reduce fuel consumption.View Blog Post
Rimac, a Croatia-based automotive technology powerhouse, needed a more efficient and flexible way to evaluate multi-physics powertrain systems.Solution Providers:
This case study details how Rimac used Modelon’s simulation and modeling solutions, such as the Modelon Electrification Library, to meet their electric modeling, battery modeling, and full vehicle system simulation needs.View Story
The Modelica language offers first class support for representing system architecture in models. This enables both architecture exploration and the development of product families.View SummaryGet Content
All Modelon libraries are developed to adhere to the open-standard, Modelica language. We, at Modelon, thrive in this language standard for many reasons and fully leverage it in the use of templates and model architectures to rapidly create models and model variants. Modelon’s Vehicle Dynamics Library utilizes this template-based approach in full vehicle modeling. There is even the VehicleInterfaces library that attempts to provide common interfaces for use in vehicle simulationView Blog Post
FMI stands for Functional Mock-up Interface. It is an open standard for exchanging dynamical simulation models between different tools in a standardized format.View Summary
FMI stands for Functional Mock-up Interface. It is an open standard for exchanging dynamical simulation models between different tools in a standardized format.
The value of model-based development and investment in a simulation model portfolio increases tremendously when the models can be reused in different contexts.
The FMI standard specifies an open format for exporting and importing simulation models. This means that you can select the tool best suited for each type of analysis while keeping the same model. You can also share your model with colleagues who can reuse it for other purposes, using tools that match their needs, skills, and preferences.View Blog Post
The Functional Mockup INterface comes in two flavors: model exchange and co-simulation. This blog explains the differencesView SummaryGet Content
The Functional Mock-up Interface (FMI) standard supports two flavors of Functional Mock-up Units (FMUs), including: Co-Simulation (CS) and Model Exchange (ME). But, do you know the difference?
As an expert in the standard it’s my goal to help users understand the commonalities and differences – getting you to a point in which you’re choosing the best possible technique to achieve your goal! Let’s first look at what the two kinds of FMUs have in common.View Blog Post
This NASA Langley case study demonstrates how and why a simulation template was used to aid in the analysis and design of a complex space-borne electro-optical sensor.View SummaryGet Content
Structural-Thermal-Optical-Performance (STOP) Model Development and Analysis of a Field-widened Michelson Interferometer
An integrated Structural-Thermal-Optical-Performance (STOP) model was developed for a field-widened Michelson interferometer which is being built and tested for the High Spectral Resolution Lidar (HSRL) project at NASA Langley Research Center (LaRC). The performance of the interferometer is highly sensitive to thermal expansion, changes in refractive index with temperature, temperature gradients, and deformation due to mounting stresses. Hand calculations can only predict system performance for uniform temperature changes. An integrated STOP Simulation Template was developed to automate and investigate the effects of design modifications on the performance of the interferometer in detail, including CTE mismatch, and other three- dimensional effects.
The STOP Template was developed using the Comet SimApp Authoring Workspace which performs automated integration between Pro-Engineer®, Thermal Desktop®, MSC Nastran™, SigFit™, Code V™, and MATLAB®. This is the first flight project for which LaRC has utilized Comet, and it allows a larger trade space to be studied in a shorter time than would be possible in a traditional STOP analysis. This paper describes the development of the STOP Template, presents a comparison of STOP results for simple cases with hand calculations, and presents results of the correlation effort to bench-top testing of the interferometer. A trade study conducted with the STOP model which demonstrates a few simple design changes that can improve the performance seen in the lab is also presented.
This paper was published in the 2014 Conference Proceedings of the SPIE.View ArticleDownload
Engineering.com's Shawn Wasserman writes about Conceptual and Systems Design Fuels Open-Architecture Integrated CAE.View Summary
There is a need to bring simulation into conceptual design. And not just any simulation, but multiphysics and systems level simulations. By conducting these assessments earlier, teams are able to catch errors early in the design cycle. This will help to reduce costly changes to the design if prototypes were to fail.View Blog Post
Achieving safe, robust, and feasible Democratization at NASA Langley Research Center.Solution Providers:
From the CAASE 18 conference, this presentation outlines NASA Langley’s Democratized Simulation implementation and how challenges were overcome with intelligent templates.View Story