Continuous Development and Management of Credible Modelica Models

Abstract

Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increases the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. In this article, we try to summarize the state-of-the-art and best-practice from the viewpoint of a Modelica language user, based on the experience gained in projects in which Modelica models have been utilized in the design process. Furthermore, missing features and gaps in the used processes are identified.

Towards Modelica Models with Credibility Information

“…Note, this section is a slightly improved version of Section 3.4 of [9]. Once a model is implemented, the modeling process goes on with the calibration, validation and verification, to ensure that the model appropriately fulfills the requirements it was created for.…”

“…It shall be based on the recently proposed "Credible Simulation Process" [7,8] of the SetLevel project (https://setlevel.de/en (accessed on 30 July 2022)). In [9], the state of the art in the development and management of credible Modelica models was summarized and missing features in the used processes were identified to guide further research in the UPSIM project. Based on this analysis, new proposals are presented below to improve the quality of Modelica models, in particular by adding traceability, uncertainty and calibration information of the parameters in a standardized way to models.…”

Signal Tables: An Extensible Exchange Format for Simulation Data

“…In [1] the authors analyzed how to improve the development of credible models based on Modelica Association standards such as the Modelica language [2] (https://github.com /modelica/ModelicaSpecification, accessed on 30 July 2022) or FMI (Functional Mock-up Interface) [3][4][5] (https://fmi-standard.org/downloads/, accessed on 30 July 2022). Their analysis showed that there are various areas where improvements are needed.…”

“…Such data should be useable both by post-processing tools and for archiving it, e.g., for traceable quality measures as needed for a credible simulation process. Currently, no satisfactory standardized format of this kind is known; see the analysis in Section 2 (a slightly improved version of Section 4.4 of [1]). There are further requirements that current formats do not support:…”

“…For example, a Boolean signal with values=[true, missing, missing, false] receives a new key flattenedValues=[1.0, NaN, NaN, 0.0]. An array signal "myarray" with a size of [100, 5,3] receives a new key flattenedValues with a matrix of size [100,15] and a new key legend = ["myarray [1,1]", "myarray[2,1]", ..., "myarray [5,3]"] that provides a meaningful legend for every column of this matrix. When a Signal Table is flattened with getFlattenedSignal, it can be imported in standard table formats such as Excel (https: //en.wikipedia.org/wiki/Microsoft_Excel, accessed on 30 July 2022), CSV, pandas (https: //pandas.pydata.org/, accessed on 30 July 2022), or DataFrames.jl (https://github.com/J uliaData/DataFrames.jl, accessed on 30 July 2022).…”