While a system-modeling language such as SysML is a formal syntactic language, it is still based on elements of human language. Its formality adds clarity and discipline that are critical for describing the design of a system.
Such a language is easy to read and understand. Terms of MBSE's language simply map to parts of speech :.
This view of the modeling language helps its users to mentally map real-life concepts to abstract ideas, and eases the formalization of the modeling process.
Now that I have described the basics of a model's language and domains, I will describe the modeling approach. A model must describe both a problem that the designed system solves, and the designed system itself the solution. The model must have these two sides, the problem side and the solution side. These are sometimes referred to as the operational and system points of view. The operational point of view is the perspective of users, operators, and business people.
It should represent business processes, objectives, organizational structure, use cases, and information flows. The operational side of the model can contain the description of "the world as-is" and the future state. The system point of view is the solution, the architecture of the system that solves the problem posed in the operational side of the model. It should describe the behavior of the system, its structure, dataflows between components, and allocation of functionality.
It should describe how the system will be deployed in the real world. It can contain solution alternatives and analyses of them. Each of these points of view has two parts, logical and physical. Separating logical and physical aspects of the model is a way to manage a system's complexity. Logical parts of the model usually change little over time, while physical changes are often initiated by technology advances.
If the model is built properly, all four quadrants should be tightly connected, as shown in Figure 1 below. Statements of the problem should be traced to elements of the solution, and logical elements allocated to physical structures.
The user of the model should be able to see clearly how the top-level concepts and components decompose to the lower level features. Users should be able to perform system analysis, create dependency matrices, run simulations, and produce a view of the system for every stakeholder.
If the physical part of the system must change, the logical side of the model identifies exactly what functionality will be affected. If a requirement or business process must be changed, the model will easily discover the impact on the solutions. In this post, I explained what MBSE is, showed how it relates to systems engineering, and discussed the fundamentals of model and modeling.
My next post will take a more practical approach and discuss requirements and requirements models. Get our RSS feed. The specific heat capacity of the mild steel plate and the asbestos board used for the construction of the experimental setup are The novelty of this work is the use of such a study to generate empirical equations for Ghana and to produce representative equations for determining the heat transfer coefficient for solar plate collectors in unsteady humid outdoor conditions in West Africa.
This work is expected to contribute data alongside similar works done for different areas to help propose empirical equations for estimating global and not site-specific heat transfer coefficients.
Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Journal metrics. Submission to final decision days. Acceptance to publication 34 days. CiteScore 2. Journal Citation Indicator 0. Impact Factor -. Journal profile Modelling and Simulation in Engineering aims to provide a forum for the discussion of formalisms, methodologies and simulation tools which relate to the modelling and simulation of human-centred engineering systems.
About this journal. Editor spotlight Modelling and Simulation in Engineering maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
Meet the editorial board. An executable system model executable system model that represents the interaction of the system components may be used to validate that the component requirements can satisfy the system behavioral requirements. The descriptive, analytical, and executable system models each represent different facets of the same system.
The component designs must satisfy the component requirements that are specified by the system models. As a result, the component design and analysis models must have some level of integration integration to ensure that the design model is traceable to the requirements model. The different design disciplines for electrical, mechanical, and software each create their own models representing different facets of the same system.
It is evident that the different models must be sufficiently integrated to ensure a cohesive system solution. To support the integration, the models must establish semantic interoperability semantic interoperability to ensure that a construct in one model has the same meaning as a corresponding construct in another model. This information must also be exchanged between modeling tools.
One approach to semantic interoperability is to use model transformations model transformations between different models.
Transformations are defined which establish correspondence between the concepts in one model and the concepts in another. In addition to establishing correspondence, the tools must have a means to exchange the model data and share the transformation information.
There are multiple means for exchanging data between tools, including file exchange, use of application program interfaces API , and a shared repository. The use of modeling standards for modeling languages, model transformations, and data exchange is an important enabler of integration across modeling domains. Barry, P. Koehler, and B. Agent-Directed Simulation for Systems Engineering. January Wymore, A. Law, A. Simulation Modeling and Analysis , 4th ed.
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