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Simulation model design process with reuse tasks 

Simulation model design process with reuse tasks 

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Conference Paper
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The Simulation Model Reference Library (SMRL or SiMoReLi) activity is an initiative in the General Support Technology Programme (GSTP) in order to promote and increase the reuse and exchange of simulation models between different stakeholders in the space domain. In this paper, we discuss several aspects related to the construction of this library...

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... requirements engineering process encompasses the set of tasks undertaken by the engineering team to define the simulation model software requirements. This process happens in the early stages of the software development before the architecture definition and detailed design. The main output of this task is a Software Requirement Specification (SRS), the software engineers ’ understanding of the high level system requirements and dependencies. An Interface Control Document (ICD) could also support the interface requirement definition. The requirement engineering flow is presented in Fig. 4 and follows the “Software requirements analysis” described in [12]. The SMRL will be supporting this process as follows: When the search engine returns model candidates, the user can review and evaluate their specification, interface definition and logical model and then, subjectively, decide which parts can be reused. Finally, the SMRL will be providing templates for the standard ECSS documentation, tailored to the needs of simulation models. Those include the SRS, ICD documents and the Software Verification Report (SVR) that will be used when verifying the requirements and the logical model. These documents will be part of the artefacts stored and managed by the library. The design process consists on the set of activities undertaken by the engineering team in order to derive the model design from available requirements. We considered aspects of the “software architectural design” and the “software design” processes describe in [12], and adapted them when defining the architecture and design of a simulation model. The SMRL will be supporting this process as follows: it will provide design artefacts of the model candidates to support the reuse of the architecture, interfaces and detailed designs. Also, it will provide templates and guidelines for the realization of the standard design documentation, i.e. Software Design Description (SDD), Software User Manual (SUM) and Software Reuse File (SRF). This process comprises the definition of the model architecture, interfaces and the detailed design; the development of the architecture and detailed design can take advantage of reuse by evaluation and selection of the applicable parts of the reused models. The reusable parts of the design will be added when creating the detailed design of the new simulation model. From this activity the new SDD, SUM, SRF, and optionally the ICD design, of the new simulation model are relevant artefacts that will be stored and managed by the library (See Fig. 5). Under the term “ construction process ” we include the implementation and testing processes. In the coding process, the implementation of the simulation model and the build procedures to compile and link the simulation model shall be created. The SMRL helps to provide model source codes, build scripts and documentation (e.g. SDD, SUM) of the reused models. The engineers shall evaluate and select which parts could be reused by the model being developed. In the testing process, the SMRL provides test documentation such as a test plan, including test cases, e.g. inputs, outputs and test criteria, as well as test procedures, test results and the supporting artefacts such as test codes, test data and test scripts. Reusing these artefacts could be a starting point for the testing activities of the new model. The engineers considering reuse shall evaluate and select which of those artefacts are applicable and can be reused. The availability of the test documentation and artefacts is important to generate confidence and credibility in the behaviour of the models managed by the library. As defined in [15], the simulation model validation is the process of determining the degree to which a simulation model and its associated data are an accurate representation of the real object or idea, from the perspective of the intended uses of the model. Part of the validation will be covered by the tests applied to the simulation model during the construction process. When testing is not possible, the validation will be carried out by analysis, inspection or review of design. The verification is the process of determining that a model implementation and its associated data accurately represent the engin eer’ s conceptual description and specifications [15]. The verification tasks that occur during the engineering processes are described in detail in [12]. Based on the above and taking into account the original objectives of the activity, the following high level requirements were identified for the library and its supporting infrastructure. The SMRL ...

Citations

... However, there is a lack of tools to address a number of advanced modelmanagement use cases, such as semantic search, analysis, cross-referencing, checking, component selection automation, for a large body of models (Johansson, Pop, and Fritzson 2005). Despite recent developments (Sirin et al. 2015;Isasi, Noguerón, and Wijnands 2015;Hussain et al. 2022), tool support for the integration of Modelica models into advanced Model-Based Systems Engineering (MBSE) practices remains limited (Larsen et al. 2016). This hinders the reuse of models within the Modelica community, and particularly in an industrial context, can greatly limit the potential for adoption of Modelica tools within integrated Model-Based Design (MBD) and product development processes. ...
Conference Paper
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This paper reports on the development of GitWorks, an open platform for democratizated Model-Based Design of cyber-physical systems (CPS). The GitWorks platform is currently under development by Perpetual Labs Ltd in collaboration with the Open Source Modelica Consortium (OSMC)1 and the OpenCAESAR project2. In this paper, we present our vision for the platform, its system architecture and a prototype implementation. We also present a case study that demonstrates the use of the proposed platform for enabling the seamless integration of Modelica models into a broader range of systems engineering processes for complex product development. In the longterm, the platform also aims to enable the integration of Modelica tools with advanced systems engineering processes that rely on other domain specific languages (e.g. SysML v2, BPMN, etc.).
... Several ontologies that extend the CCO have been developed that capture several military domains but do not capture simulation models. There have been several researchers that have developed physics-based simulation models including Cheong and Butscher [14] and Isasi and colleagues [15]. ...
... Model characterization involves finding model [4] characteristics that are structured knowledge about models necessary for their interpretation and reuse. These characteristics are used for model documentation, which involves collecting model characteristics in an easily understandable format [12] so users can determine if the models can be reused for some intended application. Additionally, these characteristics are also used for model organization so users can quickly locate models using ontologies and hierarchies. ...
... Model creators characterize models differently across the engineering domain [12]. For example, in the space domain [12], models are characterized using several attributes, including model knowledge, metadata, execution details, and metaknowledge. ...
... Model creators characterize models differently across the engineering domain [12]. For example, in the space domain [12], models are characterized using several attributes, including model knowledge, metadata, execution details, and metaknowledge. In the mechatronics domain, knowledge is captured about a sheet metal feeder's physical decomposition, assumptions, limitations, and constraints [6]. ...
Conference Paper
Full-text available
div class="section abstract"> In this paper, we review the literature related to the reuse of computer-based simulation models in the context of systems design. Models are used to capture aspects of existing or envisioned systems and are simulated to predict the behavior of these systems. However, developing such models from scratch requires significant time and effort. Researchers have recognized that the time and effort can be reduced if existing models or model components are reused, leading to the study of model reusability. In this paper, we review the tasks necessary to retrieve and reuse model components from repositories, and to prepare new models and model components such that they are more amenable for future reuse. Model reuse can be significantly enhanced by carefully characterizing the model, and capturing its meaning and intent so that potential users can determine whether the model meets their needs. Traditionally, the meaning and intent of models has been captured in textual documentation, but more recently, semantically rich, axiomatized approaches using model characteristics based on ontologies have been introduced that enable algorithmic support for the identification, discovery, and reuse of models. Researchers have also recognized that the opportunity for reuse significantly increases when models are modularized into composable model components. While many repositories and frameworks for modular modeling have been recently developed, it is important to recognize that without considerable forethought in terms of model architecture, model composability often remains elusive. Even when models are well-characterized and organized in a repository, model users need to perform several tasks before they can successfully reuse these models and components for a specific analysis scenario. These tasks include searching, optionally adapting, composing, integrating and validating models and model components. We will review the literature on each of these tasks and focus on identifying opportunities for further improvement of model reuse support frameworks. </div