Consistent Simulation Environment with FMI based Tool Chain

Drenth, Edo; Törmänen, Mikael; Johansson, Krister; Andersson, Bengt-Arne; Andersson, Daniel; Torstensson, Ivar; Åkesson, Johan

doi:10.3384/ecp140961277

Cited by 4 publications

(3 citation statements)

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“…Our novel 3D-ICE 3.0 co-simulation interface follows the Functional Mock-up Interface (FMI) [16], [42], [43] standard, an industry standard for providing interoperability among models based on differential equations. In particular, FMI models are organized using a common structure, including an XML file describing the variables and parameters that are exposed across the interface.…”

Section: A the Co-simulation Interfacementioning

confidence: 99%

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Terraneo

Leva

Fornaciari

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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The increasing power density in modern highperformance multi-processor system-on-chip (MPSoC) is fueling a revolution in thermal management. On the one hand, thermal phenomena are becoming a critical concern, making accurate and efficient simulation a necessity. On the other hand, a variety of physically heterogeneous solutions are coming into play: liquid, evaporative, thermoelectric cooling, and more. A new generation of simulators, with unprecedented flexibility, is thus required. In this paper, we present 3D-ICE 3.0, the first thermal simulator to allow for accurate nonlinear descriptions of complex and physically heterogeneous heat dissipation systems, while preserving the efficiency of latest compact modeling frameworks at the silicon die level. 3D-ICE 3.0 allows designers to extend the thermal simulator with new heat sink models while simplifying the time-consuming step of model validation. Support for nonlinear dynamic models is included, for instance to accurately represent variable coolant flows. Our results present validated models of a commercial water heat sink and an air heat sink plus fan that achieve an average error below 1 • C and simulate, respectively, up to 3x and 12x faster than the real physical phenomena.

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Section: A the Co-simulation Interfacementioning

confidence: 99%

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Terraneo

Leva

Fornaciari

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Furthermore, co-simulation is used at every stage of the development process, from early system validation, to X-in-the-Loop co-simulation, bringing hard real-time constraints to the set of challenges [81].…”

Section: A4 Black-box Co-simulationmentioning

confidence: 99%

Co-Simulation

et al. 2018

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Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, co-simulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.

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“…The behavioral model-based design of a system can assist several tasks in the entire MBSE workflow. These are typically: system architecture definition, system presizing, component pre-sizing, detailed behavioral modeling and simulation, unitary and integration verification, real-time simulation for Hardware-in-the-Loop (HiL) simulation [DRE14], etc.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of Asian Modelica Conference 2020, Tokyo, Japan, October 08-09, 2020

2020

Linköping Electronic Conference Proceedings

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The automobile industry is currently undergoing a dramatic transformation. Highperformance networking, deep learning technology, and cloud computing have radically transformed how individuals and businesses manage data, and is poised to disrupt the state-ofthe-art in the development of intelligent machines. This talk explores how automated driving and artificial intelligence based on connected, distributed computing enables future mobility systems that will impact the design and evolution of future cities.

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Consistent Simulation Environment with FMI based Tool Chain

Cited by 4 publications

References 1 publication

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Co-Simulation

Proceedings of Asian Modelica Conference 2020, Tokyo, Japan, October 08-09, 2020

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