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Über dieses Buch

Stockholm, home to the author and also known as "Beauty on the Sea", has much to offer, including a rare insight into ship construction and architecture in the early seventeenth century. Sweden was busy building an empire around the Baltic Sea in northern Europe and a strong navy was essential. During the I620s Sweden was at war with Poland, and in 1625 the Swedish king Gustavus Adolphus ordered new warships, among them the Vasa. The Vasa was built at the Stockholm shipyard by Henrik Hybertsson-an experi­ enced Dutch shipbuilder. In the seventeenth century, however, architectural draw­ ings and engineering specifications did not exist. Instead of using calculations, shipbuilders used so-called reckoning, which recorded certain ship measurements. The reckoning used in building the Vasa were intended for smaller ships with only one gun deck. The Vasa was built quite differently. When in 1628, in the presence of spectators among them foreign diplomats, the Vasa heeled over and sank on its maiden voyage, the experience of the master builder and the skills of the carpenters he employed were not much above what we today would call the component level of the time.The knowledge ofbuoyancy, bal­ last, centerofgravity and stability was basic at best.

Inhaltsverzeichnis

Frontmatter

1. Introduction to Systems Modelling

Abstract
Engineering is about designing system solutions that fulfil requirements for what we expect the system to do. Systems modelling is an essential part of the concep­tual tools we use for visualizing possible solutions, be it just in our minds, on a white board, on paper, or with the aid of a computerized tool. We need models to understand the complexity of systems, to validate that the system will satisfy requirements and to trust that the system will work, long before it is implemented. We might also use system models to support the design, the implementation and the testing of components of our systems. In that case, we need formal modelling languages to use the computer to help us translate the system model into the soft­ware and hardware components we need to build real systems.
Thomas Muth

2. Foundations of Sysnet Modelling

Abstract
Sysnet modelling (SM) is a specification and construction method for large and complex systems. SM is partly a domain-specific method. It comprises a set of concepts, architectural principles and generic patterns for modelling process control systems in general, and telecom systems in specific. The modelling language Abstract systems Modelling Language (AML) can be used for model creation. However, this book is however not primarily a description of AML. The importance of systems modelling lies in understanding the concepts, architectural principles and generic patterns (i.e. SM), which also is the primary theme of this book. The language (symbols, terms and syntactical constructs) acts more like a tool in this context. It is conceivable that languages other than AML could serve the same purpose. To support modelling in general, tools are also needed. Presently, however, no SM&AML tool exists. This book, therefore, also addresses tool makers, by providing the requirements for an SM&AML tool.
Thomas Muth

3. Logical Network Modelling

Abstract
The purpose of logical network modelling is to define the structure of logical nodes in functional space for a physical network. The model fragments that are created are logical networks, which belong to integration level 3 of the integration hierarchy.
Thomas Muth

4. Physical Network Modelling

Abstract
Physical network models are defined on the highest integration level (lowest abstraction level) in the integration hierarchy. Contrary to lower integration levels (higher abstraction levels), physical network models exhibit almost no addressing transparency. If two physical nodes need to communicate, the sending node must identify a physical media connection endpoint, which is a nearly static identification. Also contrary to higher abstraction levels, physical nodes exhibit nearly no temporary existence. They either exist in a particular physical network model, or they don’t. We say “nearly” because:
  • Models in functional space do not include spatial dimensions. If those dimension are included in a model, a spatial network model is created as well, showing sites, cables, etc. A physical network model can then be mapped on the spatial model, whereby the physical network becomes integrated in a concrete network, and physical nodes get physical addresses as well. This form of integration is, however, outside SM&AML.
  • A physical node can be removed from a site or added to a site (electrically and/or physically). In this context we can say that the physical node has a temporary existence in a concrete network. Changes of this kind are performed by network operator activities, partly through the portion of the management system that handles physical network aspects. This form of temporary existence of physical nodes is also outside SM&AML.
Thomas Muth

5. Protocol Network Modelling

Abstract
A protocol network is a functional part of a logical network. A protocol network is defined in the D-dimension as a collaboration of protocol elements. Protocol elements communicate according to the rules of a protocol (PR1, PR2 and PR3 in Fig. 5.1). Seen over a physical network, all protocol elements that can communicate by a set of mutual agreed protocols belong to the same protocol network. For a protocol element to have an existence in the DC-plane of functional space, it must be allocated in a logical node that comprises a permanent integration function, generally modelled as an integrator unit, see the example in Fig. 5.1.
Thomas Muth

6. Process Control System Modelling

Abstract
Our basic structural definition of any processing system is that it consists of two major subsystems: a resource system (RS) and a process control system (PCS), the latter being the “brain” of the processing system.
Thomas Muth

7. Concepts and Notations for Process Control Systems Modelling

Abstract
This chapter presents concepts, notations and suitable information elements for process control system modelling in a more formal way than has been done in previous chapters. The reader should already have a good understanding of the meaning of most concepts, primarily from Chap. 6.
Thomas Muth

Backmatter

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