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Rogers, James L.

Publications and source records attributed to Rogers, James L..

25 records · Page 2

Decomposing a complex design problem using CLIPS

Many engineering systems are large and multidisciplinary. Before the design of such complex systems can begin, much time and money are invested in determining the possible couplings among the participating subsystems and their parts. For designs based on existing concepts, like commercial aircraft design, the subsystems and their couplings are usually well-established. However, for designs based on novel concepts, like large space platforms, the determination of the subsystems, couplings, and participating disciplines is an important task. Moreover, this task must be repeated as new information becomes available or as the design specifications change. Determining the subsystems is not an easy, straightforward process and often important couplings are overlooked. The design manager must know how to divide the design work among the design teams so that changes in one subsystem will have predictable effects on other subsystems. The resulting subsystems must be ordered into a hierarchical structure before the planning documents and milestones of the design project are set. The success of a design project often depends on the wise choice of design variables, constraints, objective functions, and the partitioning of these among the design teams. Very few tools are available to aid the design manager in determining the hierarchical structure of a design problem and assist in making these decisions.

Rogers, James L.↗

The potential application of the blackboard model of problem solving to multidisciplinary design

The potential application of the blackboard model of problem solving to multidisciplinary design is discussed. Multidisciplinary design problems are complex, poorly structured, and lack a predetermined decision path from the initial starting point to the final solution. The final solution is achieved using data from different engineering disciplines. Ideally, for the final solution to be the optimum solution, there must be a significant amount of communication among the different disciplines plus intradisciplinary and interdisciplinary optimization. In reality, this is not what happens in today's sequential approach to multidisciplinary design. Therefore it is highly unlikely that the final solution is the true optimum solution from an interdisciplinary optimization standpoint. A multilevel decomposition approach is suggested as a technique to overcome the problems associated with the sequential approach, but no tool currently exists with which to fully implement this technique. A system based on the blackboard model of problem solving appears to be an ideal tool for implementing this technique because it offers an incremental problem solving approach that requires no a priori determined reasoning path. Thus it has the potential of finding a more optimum solution for the multidisciplinary design problems found in today's aerospace industries.

Rogers, James L.↗

A knowledge-based tool for multilevel decomposition of a complex design problem

Although much work has been done in applying artificial intelligence (AI) tools and techniques to problems in different engineering disciplines, only recently has the application of these tools begun to spread to the decomposition of complex design problems. A new tool based on AI techniques has been developed to implement a decomposition scheme suitable for multilevel optimization and display of data in an N x N matrix format.

Rogers, James L.↗

DeMAID: A Design Manager's Aide for Intelligent Decomposition user's guide

A design problem is viewed as a complex system divisible into modules. Before the design of a complex system can begin, the couplings among modules and the presence of iterative loops is determined. This is important because the design manager must know how to group the modules into subsystems and how to assign subsystems to design teams so that changes in one subsystem will have predictable effects on other subsystems. Determining these subsystems is not an easy, straightforward process and often important couplings are overlooked. Moreover, the planning task must be repeated as new information become available or as the design specifications change. The purpose of this research is to develop a knowledge-based tool called the Design Manager's Aide for Intelligent Decomposition (DeMAID) to act as an intelligent advisor for the design manager. DeMaid identifies the subsystems of a complex design problem, orders them into a well-structured format, and marks the couplings among the subsystems to facilitate the use of multilevel tools. DeMAID also provides the design manager with the capability of examining the trade-offs between sequential and parallel processing. This type of approach could lead to a substantial savings or organizing and displaying a complex problem as a sequence of subsystems easily divisible among design teams. This report serves as a User's Guide for the program.

Rogers, James L.↗

An intelligent advisor for the design manager

A design problem is viewed as a complex system divisible into modules. Before the design of a complex system can begin, much time and money are spent in determining the couplings among modules and the presence of iterative loops. This is important because the design manager must know how to group the modules into substems and how to assign subsystems to design teams so that changes in one subsystem will have predictable effects on other subsystems. Determining these subsystems is not an easy, straightforward process and often important couplings are overlooked. Moreover, the planning task must be repeated as new information becomes available or as the design specifications change. The purchase of this research effort is to develop a knowledge-based tool to act as an intelligent advisor for the design manager. This tool identifies the subsystems of a complex design problem, orders them into a well-structured format, and marks the couplings among the subsystems to facilitate the use of multilevel tools. The tool was tested in the decomposition of the COFS (Control of Flexible Structures) mast design which has about 50 modules. This test indicated that this type of approach could lead to a substantial savings by organizing and displaying a complex problem as a sequence of subsystems easily divisible among design teams.

Rogers, James L.↗

A strategy for reducing turnaround time in design optimization using a distributed computer system

There is a need to explore methods for reducing lengthly computer turnaround or clock time associated with engineering design problems. Different strategies can be employed to reduce this turnaround time. One strategy is to run validated analysis software on a network of existing smaller computers so that portions of the computation can be done in parallel. This paper focuses on the implementation of this method using two types of problems. The first type is a traditional structural design optimization problem, which is characterized by a simple data flow and a complicated analysis. The second type of problem uses an existing computer program designed to study multilevel optimization techniques. This problem is characterized by complicated data flow and a simple analysis. The paper shows that distributed computing can be a viable means for reducing computational turnaround time for engineering design problems that lend themselves to decomposition. Parallel computing can be accomplished with a minimal cost in terms of hardware and software.

Young, Katherine C.↗

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

The purpose of this research effort is to investigate the benefits that might be derived from applying artificial intelligence tools in the area of conceptual design. Therefore, the emphasis is on the artificial intelligence aspects of conceptual design rather than structural and optimization aspects. A prototype knowledge-based system, called STRUTEX, was developed to initially configure a structure to support point loads in two dimensions. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user by integrating a knowledge base interface and inference engine, a data base interface, and graphics while keeping the knowledge base and data base files separate. The system writes a file which can be input into a structural synthesis system, which combines structural analysis and optimization.

Rogers, James L.↗