Descriptive System Models Research Articles

Combining System Models and CAD for Change Scenario Management

AbstractIn the development of complex mechatronic systems, interdisciplinary cooperation requires an exchange between stakeholders in order to ensure fulfillment of requirements and related functions. The cooperation of the stakeholders from different disciplines must be well coordinated to address systems changes. The system model as an essential aspect of model‐based systems engineering, has to be linked to specific models of involved disciplines (mechanical, electrical/electronical and software) to enable a basis for automatic identification e.g., of affected subsystems and functions. This contribution discusses the potential of model linking from the perspective of change scenarios by considering published approaches to link system models and computer‐aided design (CAD). A methodical approach on how descriptive system models can be used as base for computer‐aided design is presented. This approach is analyzed by reviewing a use case for a design change scenario of an automotive eAxle.

Synchronous Integration Method of System and Simulation Models for Mechatronic Systems Based on SysML

System architecture could be described clearly and unambiguously by a descriptive system model during the system-level design of mechatronic products. However, for system verification, it is necessary to integrate the system modeling tools and simulation tools for analysis. Most of the existing integration methods focus on the integration in the physical architecture phase of the system model. A model integration framework that can integrate simulation into the logical architecture phase of the entire model for synchronous model verification is proposed in this paper. In the integration process, a Model-to-Text method is employed for information extraction and model transformation from the SysML model to the simulation model. The system model can be transformed by reusing and updating the generated simulation model through the framework for model verification. A framework with the Simulink model as the transformation target is constructed as an example in this paper, and the capability of this framework is verified by a design process of a quadruped robot. The result shows that this framework can realize the system design paradigm of integrating system verification for the entire logical architecture, which can improve the design efficiency and provide a reference for other system design and integration work.

Towards Holistic System Models Including Domain-Specific Simulation Models Based on SysML

In the face of the rapid growth in the scale and complexity of multidisciplinary systems, being able to develop reliable systems under ever-faster changing and more individual market requirements is becoming more and more challenging. The Model-Based Systems Engineering (MBSE) approach has already been researched heavily, and started to be introduced for the management of complexity, maintaining consistency, and reducing development costs and the time-to-market. However, a major drawback of the current MBSE methodologies is the lack of capability to integrate with domain-specific simulation models to investigate design concepts in the early phases of the development process. In order to address this issue, we propose a holistic system modeling approach that allows system engineers to link descriptive system models with domain-specific simulation models. In this paper, the Systems Modeling Language (SysML) is used as the standard architecture modeling language. A system modeling approach in SysML based on the system’s functional architecture for system design and validation is defined. The approach was developed to integrate domain-specific models into the system model using a SysML modeler with the capability of running and reusing simulation tasks via the coupling of external tools, which helps to bridge the existing gap between models on the system level and detail level. The feasibility of the proposed approach will be evaluated based on the case study of a wind turbine (WT) system. The study shows that our approach has the potential to enable the consistent, parameter-based interlinkage of domain-specific models based on always-up-to-date data, and to assist engineers in making design decisions to meet the system requirements accurately and rapidly in different engineering fields.

Model-Based Development and Simulation for Robotic Systems with SysML, Simulink and Simscape Profiles

In system-level design, it is difficult to achieve a system verification which fulfils the requirements of various stakeholders using only descriptive system models. Descriptive system models using...

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

The 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.

Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints

In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other "black box" model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO's overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.

System model of production inventory control

The primary consequence of just-in-time (JIT) production is reduction of inventory. The associated benefit is (a) improved cashflow due to less financial resource tied up in inventory, and (b) less storage space required hence more productive plant area. Coupled with lean production and total quality management (TQM), it may also be possible to (c) improve worker productivity, (d) improve product quality, and (e) reduce production waste. Thus there are strong financial incentives to move to JIT/lean production. However, existing methods cannot adequately explain the causality that leads to failed (or successful) JIT implementations. While it is generally acknowledged that qualitative variables exist and affect success, there are no structured system models that accommodate the qualitative variables. This paper describes a fresh theoretical approach to examining the larger organisational factors in which inventory control systems are embedded. A structured descriptive system model for production inventory control is described, including base stock, conwip, kanban, and hybrid systems. The model was then interrogated to produce lists of tentative critical success factors for implementation of these strategies in JIT and lean production, and propose explanations as to why it succeeds or fails. This demonstrates that it is indeed possible to use descriptive modelling methods to explore the effectiveness of JIT and lean production systems.