Initiatives for acausal model connection using FMI in JSAE (Society of Automotive Engineers of Japan)

Co-simulation is a promising approach for the analysis of complex, multi-domain systems, that leverages mature simulation tools of the respective domains. It has been applied in many different disciplines in academia and industry, with limited sharing of findings. With the increasing adoption of the FMI standard, researchers have set to work on surveying the scattered knowledge on co-simulation in academia. This paper complements the existing surveys by taking on the social and empirical aspect, corroborating, and prioritizing, previous findings. We focus on understanding the perceived research challenges, and the current barriers, based on expert assessment. One of the main barriers pointed out is the limited support for discrete event and hybrid co-simulation.Co-simulation yields multiple advantages: Figure 1. Example master algorithm.

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“…(Sahlin and Lebedev, 2016)), industry (e.g. (Hirano et al, 2015) and academia (e.g. (Cremona et al, 2017b;Broman et al, 2013)).…”

Section: There Is Insufficient Documentationmentioning

confidence: 99%

Functional Mock-up Interface: An empirical survey identifies research challenges and current barriers

Schweiger¹,

Gomes²,

Engel³

et al. 2019

Linköping Electronic Conference Proceedings

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“…The most intuitive definition defines the angle θ ∈ [0, π] as the smaller positive angle between the two 8) where cos -1 denotes the inverse of the cosine with a domain of [0, π]. This definition is sufficient for most purposes especially if only the cosine of an angle is of interest.…”

Section: Angle Definitionsmentioning

confidence: 99%

“…using the definition of the orbit frame from Section 2.1 and Equation (8) . Another way to calculate the beta angle is to use the normal of the orbit plane and parameterise the vectors by the orbital elements describing the movement of the sun and the satellite.…”

Section: The Beta Anglementioning

confidence: 99%

“…The solar zenith angle is defined as in (Meseguer, Pérez-Grande, and Sanz-Andrés 2012) to describe the portion of the illuminated planet which is seen by the spacecraft. The solar zenith angle ξ is defined as the angle between the spacecraft vector r and the sun pointing vector s as ξ = ∠(r, s) (16) using (8). In order to enable a thermal analysis dependent of the orbit attitude, the influence of this angle can be described by the slowly time varying beta angle β and the periodic solar noon angle θ.…”

Section: The Solar Zenith Anglementioning

confidence: 99%

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Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

2019

Linköping Electronic Conference Proceedings

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Simulation Led Innovation at Procter & GambleThe required pace of innovation continues to increase in the consumer packaged goods industry. Increasingly historical approaches of development by physical prototypes lead to slow execution, high costs and incremental innovations.By leveraging simulations we can deliver better innovations, faster, at lower costs. Succeeding on this journey requires more than just technical mastery. Changing the way that innovation is done also requires changing how decisions are made and how resources are allocated. It is also necessary to think about the entire lifecycle of the digital assets such as simulation platforms in a manner similar to how we manage our physical assets (e.g. pilot plants). Digital assets required continued funding, maintenance and upgrades to remain viable over time.

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“…The causality needs to be specified on any acausal interface if a model containing such connectors is to be connected to an MFL model or any FMU. Hirano et al presents adaptors specifying the causality for signals in the electric and mechanics domains [13]. In Figure 2, a one dimensional thermodynamics domain adaptor for connections between Modelica.Fluid models and MFL volume type components is presented.…”

Section: Aircraft Environmental Control Systemmentioning

confidence: 99%

Early Insights on FMI-based Co-Simulation of Aircraft Vehicle Systems

Hällqvist¹,

Braun

Krus

2017

Linköping Electronic Conference Proceedings

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Modelling and Simulation is extensively used for aircraft vehicle system development at Saab Aeronautics in Linköping, Sweden. There is an increased desire to simulate interacting sub-systems together in order to reveal, and get an understanding of, the present cross-coupling effects early on in the development cycle of aircraft vehicle systems. The co-simulation methods implemented at Saab require a significant amount of manual effort, resulting in scarcely updated simulation models, and challenges associated with simulation model scalability, etc. The Functional Mock-up Interface (FMI) standard is identified as a possible enabler for efficient and standardized export and co-simulation of simulation models developed in a wide variety of tools. However, the ability to export industrially relevant models in a standardized way is merely the first step in simulating the targeted coupled sub-systems. Selecting a platform for efficient simulation of the system under investigation is the next step. Here, a strategy for adapting coupled Modelica models of aircraft vehicle systems to TLM-based simulation is presented. An industry-grade application example is developed, implementing this strategy, to be used for preliminary investigation and evaluation of a cosimulation framework supporting the Transmission Line element Method (TLM). This application example comprises a prototype of a small-scale aircraft vehicle systems simulator. Examples of aircraft vehicle systems are environmental control systems, fuel systems, and hydraulic systems. The tightly coupled models included in the application example are developed in Dymola, OpenModelica, and Matlab/Simulink. The application example is implemented in the commercial modelling tool Dymola to provide a reference for a TLM-based master simulation tool, supporting both FMI and TLM. The TLM-based master simulation tool TLMSimulator is investigated in terms of model import according to the FMI standard with respect to a specified set of industrial needs and requirements.

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Initiatives for acausal model connection using FMI in JSAE (Society of Automotive Engineers of Japan)

Cited by 4 publications

References 0 publications

Functional Mock-up Interface: An empirical survey identifies research challenges and current barriers

Functional Mock-up Interface: An empirical survey identifies research challenges and current barriers

Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

Early Insights on FMI-based Co-Simulation of Aircraft Vehicle Systems

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