Intelligent Advisory Tool for Design Decomposition

1998 ◽  
pp. 293-319
Author(s):  
Dan Braha ◽  
Oded Maimon
Keyword(s):  
Design Decomposition

Neurocomputing strategies in structural design — decomposition based optimization

1994 ◽  
Vol 8 (4) ◽  
pp. 242-250 ◽  
Author(s):  
Z. P. Szewczyk ◽  
P. Hajela
Keyword(s):  
Structural Design ◽  
Design Decomposition

scholarly journals Development of a Design Procedure for Class E Amplifiers

2018 ◽  
Vol 223 ◽  
pp. 01016
Author(s):  
Mattison S. Siri ◽  
David S. Cochran
Keyword(s):  
Design Procedure ◽  
Design Parameters ◽  
Specific Load ◽  
Systems Architecture ◽  
Design Decomposition ◽  
Path Dependent ◽  
Capacitance Coupling ◽  
Class E

Here, the step-by-step design procedure for a Class E amplifier is presented. An existing Class E amplifier system is described using a systems architecture approach. The design decomposition for the case study is written so that Physical Solutions (PSs; equivalent to Design Parameters) are in terms of component parameters (such as frequency or capacitance). Coupling issues are found to arise given constraints on transistor use. The design decomposition is altered to reflect the case where an amplifier is required to power a specific load. A discussion of transistor failure enables a design procedure to be developed by observing path-dependent coupling. The design procedure is tested through the design of a real amplifier. The designed amplifier is built and its performance measured.

Active occurrence-matrix-based approach to design decomposition

1993 ◽  
Vol 25 (8) ◽  
pp. 500-512 ◽  
Author(s):  
Natarajan Sridhar ◽  
Rajiv Agrawal ◽  
Gary L Kinzel
Keyword(s):  
Design Decomposition ◽  
Occurrence Matrix

Managing Dependencies for Collaborative Design

2000 ◽  
Author(s):  
Kai-Lu Wang ◽  
Yan Jin
Keyword(s):  
Collaborative Design ◽  
Formal Model ◽  
Design Stage ◽  
Modeling Framework ◽  
Design Decomposition ◽  
Engineering Problems ◽  
Coordination Framework ◽  
Dependency Management ◽  
Coordination Methods

Abstract In collaborative engineering, the dependencies of engineering problems determine with whom and how designers should coordinate. Based on the directed dependency framework introduced in this research, we argue that the reciprocal and cyclic dependencies cause design iterations and inefficiencies, and should be avoided if possible. By studying the patterns of engineering dependencies and design decomposition, this paper provides an approach to manage the dependencies in order to avoid reciprocal and cyclic dependencies both in the early conceptual design stage and in design task arrangement stage, to make collaborative design more efficient. The long-term goal of this research is to develop a dependency-based coordination framework that consists of a formal model of engineering dependencies, and coordination methods, and guidelines for dependency-based engineering design and management. This paper describes our current dependency modeling framework and dependency management methods for collaborative design.

When Decomposition Increases Complexity: How Decomposing Introduces New Information Into the Problem Space

2021 ◽  
Author(s):  
Suparna Mukherjee ◽  
Anthony Hennig ◽  
Taylan G. Topcu ◽  
Zoe Szajnfarber
Keyword(s):  
Design Space ◽  
Decomposition Process ◽  
Space Robotics ◽  
Problem Space ◽  
Design Work ◽  
Descriptive Complexity ◽  
Parallel Design ◽  
Design Decomposition ◽  
New Information ◽  
Dominant Design

Abstract Decomposition is a dominant design strategy because it enables complex problems to be broken up into more manageable modules. However, although it is well known that complex systems are rarely fully decomposable, much of the decomposition literature is framed around reordering or clustering processes that optimize an objective function to yield a module assignment. As illustrated in this study, these approaches overlook the fact that decoupling partially decomposeable modules can require significant additional design work, with associated consequences that introduce considerable information to the design space. This paper draws on detailed empirical evidence from a NASA space robotics field experiment to elaborate mechanisms through which the processes of decomposing can add information and associated descriptive complexity to the problem space. Contrary to widely held expectations, we show that complexity can increase substantially when natural system modules are fully decoupled from one another to support parallel design. We explain this phenomenon through two mechanisms: interface creation and functional allocation. These findings have implications for the ongoing discussion of optimal module identification as part of the decomposition process. We contend that the sometimes-significant costs of later stages of design decomposition are not adequately considered in existing methods. With this work we lay a foundation for valuing these performance, schedule and complexity costs earlier in the decomposition process.

Improving design decomposition (extended version)

2017 ◽  
Vol 29 (4) ◽  
pp. 601-627 ◽  
Author(s):  
David Faitelson ◽  
Shmuel Tyszberowicz
Keyword(s):  
Extended Version ◽  
Design Decomposition
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