Distributed Computer Control Systems Research Articles

Economic Considerations for Real-Time Aircraft/Avionic Distributed Computer Control Systems

Using aircraft/avionic systems as examples, economic considerations for Distributed Computer Control Systems (DCCS) are discussed. Centralized, distributed, and federated processing architectures are used as the primary set of systems alternatives from which the economic factors are developed. Technical, schedule, and financial risks for the system architectures are presented. Standardization of computer hardware and software is examined from the economic viewpoint and other related risk factors. The economic impact of subsequent logistic support for standardized computer hardware and software versus non-standard products is identified. System considerations such as reliability, maintainability, availability, build-in-test, fault tolerance, and redundancy are examined from the standpoint of resources available to design and develop the DCCS, and also from the viewpoint of economic impact of failure of the DCCS to perform as expected. The economic impact of external factors such as the rate of technology advancement, technology independence, limited production runs, and the general lack of economic leverage upon the market are examined and related to the life-cycle support requirements of the DCCS. The economic considerations of a DCCS for an aircraft/avionic system application will be used to define similar considerations for an idealized automated process plant.

A View of Business Requirements on Distributed Data Processing Systems

In introducing this author’s view to the discussion of similarities and differences between distributed data processing systems and distributed computer control systems, this paper discusses some of the key requirements in the business environment highlighting the need for some degree of central control over the DDP environment.

Welcome to the First IFAC Workshop on Distributed Computer Control Systems

Welcome to the First IFAC Workshop on Distributed Computer Control Systems

Transaction Processing - An Effective Conceptual Framework for Comparing and Understanding Distributed Systems

This paper describes the introductory statements of the author presented in a panel discussion that compares the essential characteristics and requirements of distributed data processing (DDP) and distributed computer control systems (DCCS). The similarity hypothesis and definition of a distributed system set forth in the introductory paper by T.J. Harrison are supported. The parametric measures of control mode, interprocess communication and system integrity are discussed. Finally, the opinion that transaction processing is an excellent frame of reference for comparing and understanding distributed systems is postulated.

Introductory Remarks: DDP vs. DCC

Publisher Summary This chapter describes the comparison of distributed computer control (DDC) and distributed data processing (DDP) systems. Great similarities can be identified in the DDC and DDP systems. In a hierarchical architecture, there is a large central processor that handles all communication and coordination among a set of local processors.In a decentralized architecture, there is a communications network interconnecting a number of local processors, and the management and coordination functions are also decentralized. The timescale of the local process determines the required bandwidth of the controller, and the ways that the local process interacts with the rest of the plant is well-defined. The type of information required from other parts of the plant and the frequency with which it must be updated, are also reasonably well defined. If the interconnecting structure of the overall physical plant can be modified at all, the number of possible modifications is usually finite and they are not frequently made.

Structure of an Ideal Distributed Computer Control System

The problems related to distributed systems belong in general to the crucial ones in the design of computer systems. A general model which simplifies an efficient design of such systems has therefore to include appropriate means to deal with distributed systems.Such a general model is presented here and it is shown, that a high level system, which provides virtually unlimited parallel processing capability independent from geographical distribution can in two steps be mapped to a low level system which only knows sequential processors.

Parallel Processing Concepts for Distributed Computer Control Systems

It is proposed to utilize the spare processing power embedded in a real-time computer control network for the computation of a common distributed task in a co-ordinated manner. The algorithm of Hierarchical System Theory is used as a medium to explore the principles involved in time-sharing this distributed background job with the computation of the real-time control algorithm. An interconnection structure is put forward to satisfy the resulting data-processing requirements.

The Design of Distributed Computer Control Systems

The complexity of decentralised control, large scale systems and distributed processing are combined in the design of distributed computer control systems. A general view of the problems that arise in the design of distributed computer control systems and some of the present approaches to solve them are presented. Particular emphasis in the cross fertilization of computer science and control theory is made.

Conference report. Distributed computer control systems

Conference report. Distributed computer control systems

Distributed computer control systems

Energy cost comparison between MSF, MED and SWRO has been conducted. In order to investigate energy consumption differences when combined with a simple cycle Oil-Fired Power Plant (OFPP) or a Combined Cycle Power Plant (CCPP), Yanbu Ph.3 and Ras Al-Khair Ph.1 power and water cogeneration projects are considered as practical heat and mass balance references. With the net power production of 2708.5 MW and the total water production of 124.54 MIGD, the fuel energy differences due to desalination are compared. By calculating the fuel energy requirement for desalination from a cogeneration cycle itself, some controversial issues including evaluating electricity and steam could be avoided. Results show that the required fuel energies for desalination are less by 11–49% when combined with CCPP than with OFPP, owing to the higher efficiency of the combined cycle. Thermal desalination benefits more greatly from the combined cycle's higher efficiency due to a lowered steam value. While SWRO shows a better fuel energy efficiency for most of the studied regime, high Performance Ratio (PR) MEDs combined with CCPP could be a similar energy efficient option if PR is 16.5–19.3, compared to SWRO with CCPP.

The Computer in BWR Operations - Present and Future

The current General Electric Boiling Water Reactor process computer is a conventionally architectured, medium sized, monolithic system based upon the GE-PAC* 4010. The system performs scan, log, and alarm functions, as well as some rather complicated nuclear steam supply and turbine generator performance calculations. However, over the years, the G.E. system designers in San Jose have become aware of both the limitations of the monolithic architecture as applied to the rapidly growing nuclear applications as well as the recent tremendous advances in computer hardware technology. What has emerged is the concept of a distributed computer control system primarily designed for the G.E. BWR, but also ideally suited for other applications. Built around a high speed common data base memory, the distributed system yields a giant improvement in terms of overall computer system modularity, software simplicity, versatility, price/performance ratio, availability, and functional capability. The G.E. design is built around two major changes in philosophy in terms of applying process computers to an application: (1) Force the computer architecture to fit the man and the application, and (2) Increase the hardware content and complexity whenever it is consistent with reducing the software complexity and cost.