确定联合模拟用fmu的组成

David Broman, Christopher X. Brooks, L. Greenberg, Edward A. Lee, M. Masin, S. Tripakis, M. Wetter
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引用次数: 156

摘要

在本文中,我们解释了如何在FMI (Functional Mockup Interface)标准下实现fmu (Functional Mockup Units)的确定性执行。我们特别关注联合仿真,其中FMU要么包含自己的内部仿真算法,要么作为仿真工具的网关。我们给出了fmu和主算法(协调fmu的执行)的设计条件,以实现确定性的联合仿真。我们表明,在当前版本的标准中,这些条件需要FMU的功能,这些功能在标准中是可选的,并且在实践中很少由FMU提供。当fmu缺乏这些必要的功能来构建模型时,许多基本的建模功能变得无法实现,包括简单的离散事件模拟和变步长数值积分算法。我们提出了一个标准的小扩展和一个策略,用于设计fmu,使更广泛的模型类的确定性执行成为可能。扩展使主算法能够查询未来预期事件的时间。我们证明,如果模型中的所有fmu都是无内存的,或者实现回滚或步长预测之一,则可以确定性地执行模型。我们进一步表明,这样的模型最多可以包含一个“遗留”FMU,它不是无内存的,既不提供回滚也不提供步长预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determinate composition of FMUs for co-simulation
In this paper, we explain how to achieve deterministic execution of FMUs (Functional Mockup Units) under the FMI (Functional Mockup Interface) standard. In particular, we focus on co-simulation, where an FMU either contains its own internal simulation algorithm or serves as a gateway to a simulation tool. We give conditions on the design of FMUs and master algorithms (which orchestrate the execution of FMUs) to achieve deterministic co-simulation. We show that with the current version of the standard, these conditions demand capabilities from FMUs that are optional in the standard and rarely provided by an FMU in practice. When FMUs lacking these required capabilities are used to compose a model, many basic modeling capabilities become unachievable, including simple discrete-event simulation and variable-step-size numerical integration algorithms. We propose a small extension to the standard and a policy for designing FMUs that enables deterministic execution for a much broader class of models. The extension enables a master algorithm to query an FMU for the time of events that are expected in the future. We show that a model can be executed deterministically if all FMUs in the model are either memoryless or implement one of rollback or step-size prediction. We show further that such a model can contain at most one “legacy” FMU that is not memoryless and provides neither rollback nor step-size prediction.
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