China
Africa
Explore chapters and articles related to this topic
Heterogeneous Model Composability
Published in Gabriel A. Wainer, Pieter J. Mosterman, Discrete-Event Modeling and Simulation, 2018
Hessam S. Sarjoughian, Gary R. Mayer
Many contemporary systems are integrated from subsystems that have simple to complex structures and behaviors. Given the diversity of parts and resulting complexity of the systems, criticality of operation, and enormous financial consequences, simulation is being used more and more as the primary science and technology to aid analysis, design, implementation, and testing. Indeed, simulation can be used across each phase of system development as proposed in the Simulation-Based Acquisition [1]. For example, a suite of system-level simulation models may be developed to help analyze requirements and evaluate potential architectural solutions far in advance of defining detailed design specifications. Yet, another suite of models may be used to help develop detailed design specifications that can be implemented. The purposes for these systems vary significantly, as each is aimed at satisfying particular goals.
Modeling, Design, and Simulation of N/MEMS by Integrating Finite Element, Lumped Element, and System Level Analyses
Published in Sarhan M. Musa, Computational Finite Element Methods in Nanotechnology, 2013
Jason Vaughn Clark, Prabhakar Marepalli, Richa Bansal
Simulink is a system level simulation tool that is based in MATLAB. It uses graphical building blocks to configure systems. Simulink has a large library of building blocks that span a wide variety of modules including control theory, digital signal processing, COMSOL, Sugar, etc. For instance, Simulink can be used to impart feedback and control signals, or environmental disturbances such as noninertial forces, temperature fluctuations, or noise, etc. Like COMSOL, Simulink operations can also be carried out in the MATLAB workspace, which we exploit with iSugar. The seamless integration of iSugar with SIMLINK allows for parametric optimization of the MEMS component as its performance is explored in a more complete system.
Contrasting digital twin vision of manufacturing with the industrial reality
Published in International Journal of Computer Integrated Manufacturing, 2022
Jyrki Savolainen, Mikkel Stein Knudsen
Following the diagram in Figure 4, the data analytics layer is the place where the magic of data analytics happens that is the optimal state of the system components at any given moment is calculated (proposition 6). One important notation, and an essential requirement for system-level simulation front-run, is the ability to feedback the results gained from the data analytics layer back to the data layer at the bottom as shown in Figure 4. That is, the feedback loop contains the arrays of optimized system parameters that are calculated or forecasted in each iteration of the system-level DT. As trivial as it may seem on paper, creating a feedback loop from the system-level DT back to the process control software through firewalls can be a technically daunting task which, again, is one of the key managerial reasons for starting small in the system-level DT-projects (see, also proposition 10). Secondly, it is much easier to keep a system model verified and validated when components and/or their functionalities are added gradually (proposition 7).