Integrating Analytical Models with Descriptive System Models: Implementation of the OMG SyML Standard for the Tool-specific Case of MapleSim and MagicDraw

AbstractThe Jet Propulsion Laboratory (JPL) is investing heavily in the development of an infrastructure for building system models using the Systems Modeling Language (SysML). An essential component is a transformation apparatus that permits diverse models to be integrated independently of their nature (e.g. declarative, analytical and statistical). This paper presents one useful case: the integration of analytical models expressed using the Modelica language. Modelica is an open standard, declarative, multi-domain modeling language that allows for complex dynamic systems to be modeled. Maplesoft's MapleSim is one software tool that supports the Modelica language. The tool-neutral specification for the transformation between the languages Modelica and SysML is defined in the SysML-Modelica transformation specification (SyML) standard published by the Object Management Group (OMG). As part of the development efforts, said specification has been implemented using the Query-View- Transformation Operational (QVTO) language. During the process, several critical changes to the current SyML standard were proposed. Furthermore, a number of current limitations related to MapleSim were identified. Despite these issues, a proof-of- concept transformation was successfully implemented. In conclusion, the integration of complex simulation models conforming to the Modelica language with SysML-based system models has shown great promise and is a highly useful tool to support the decision making process in design

Model Continuity in Discrete Event Simulation: A Framework for Model-Driven Development of Simulation Models.

Most of the well known modeling and simulation methodologies state the importance of conceptual modeling in simulation studies and they suggest the use of conceptual models during the simulation model development process. However, only a limited number of methodologies refers to howto move from a conceptual model to an executable simulation model. Besides, existing modeling and simulation methodologies do not typically provide a formal method for model transformations between the models in different stages of the development process. Hence, in the current M&S practice, model continuity is usually not fulfilled. In this article, a model driven development framework for modeling and simulation is in order to bridge the gap between different stages of a simulation study and to obtain model continuity. The applicability of the framework is illustrated with a prototype modeling environment and a case study in the discrete event simulation domain

SysML Executable Model of an Energy Efficient House and Trade-Off Analysis

With the growing complexity of energy efficient buildings, the methods of modeling and simulating such structures must account for monitoring several thousand design parameters across multiple diverse domains. As a result, modeling tools are now very specific to their respective domains and are growing more and more incongruous with each other. This calls for a way to integrate multiple modeling tools in the effort to create a single, large model capable to encapsulate data from multiple, different models. Thus, in this thesis, different methods to perform an integration with Systems Modeling Language (SysML) and a simulation tool were identified, described and evaluated. Then, a new method was developed and discussed. Finally, the new method was demonstrated by developing a SysML executable model of a simple two-room house that utilizes solar power for space heating, with a heat pump used as a backup. Using the Functional Mock-up Interface (FMI) standard, the SysML model is integrated with a Modelica model, and a simulation is run in Simulink. Finally, a tradeoff analysis was performed for the purpose of design space exploration

Supporting Multi-Domain Model Management

Model-driven engineering has been used in different domains such as software engineering, robotics, and automotive. This approach has models as the primary artifacts, and it is expected to improve quality of system specification and design, as well as the communication among the development team. Managing models that belong to the same domain might not be a complex task because of the features provided by the available development tools. However, managing interrelated models of different domains is challenging. A robot is an example of such a multi-domain system. To develop it one might need to combine models created by experts from mechanics, electronics and software domains. These models might be created using domain specific tools of each domain, and a change in one model of one domain might impact a model from a different domain causing inconsistency in the entire system. This thesis therefore aims to facilitate the evolution of the models in this multi-domain setting. It starts with a systematic literature review in order to identify the open issues, and strategies used to manage models from different domains. We identified that making explicit the relationship between models from different domains can support the models maintenance, making it easy to recognize affected models because of a change. The following step was to investigate ways of extracting information from different engineering models that were created using different modeling notations. For this goal, we required a uniform approach that would be independent from the peculiarities of the notations. This uniform approach can only be based on elements typically present in various modeling notations, i.e., text, boxes, and lines. Thus, we investigated the suitability of optical character recognition (OCR) for extracting textual elements from models from different domains. We also identified the common errors made by the off-the-shelf OCR services, and we proposed two approaches to correct one of these errors. After that, we used name matching techniques on the textual elements extracted by OCR to identify relationships between models from different domains. To conclude, we created an infrastructure that combines all the previous elements into one single tool that can also store the relationships in a structured manner making it easier to maintain the consistency of an entire system. We evaluated it by means of an observational study with a multidisciplinary team that builds autonomous robots designed to play football