scholarly journals Multi-language Co-design Environment for Controller System Design

2005 ◽  
Vol 1 (3) ◽  
pp. 337-340
Author(s):  
M. Benmohamme ◽  
S. Merniz.
Keyword(s):  
System Design ◽  
Design Environment

A OneNASA Framework: The Virtual System Design Environment

2004 ◽  
Author(s):  
Johnny Medina ◽  
Dale Spalding ◽  
Chris Stone ◽  
Chris Holtery
Keyword(s):  
System Design ◽  
Design Environment ◽  
Virtual System

Multiobjective Collaborative Optimization

1997 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
System Design ◽  
Systems Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Accurate Estimation ◽  
Successful Implementation ◽  
Design Environment ◽  
Comprehensive Overview ◽  
Design Problems ◽  
Multidisciplinary Systems

Abstract This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this research is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for MultiObjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily been applied to system design problems which have a single objective function. Recent investigations involving multidisciplinary design simulators have reported success in applying CO to multiobjective system design problems. In this research three MultiObjective Collaborative Optimization (MOCO) strategies are developed, reviewed and implemented in a comparative study. The goal of this effort is to provide an in depth comparison of different MOCO strategies available to system designers. Each of the three strategies makes use of parameter sensitivities within multilevel solution strategies. In implementation studies, each of the three MOCO strategies is effective in solving two multiobjective multidisciplinary systems design problems. Results indicate that these MOCO strategies require an accurate estimation of parameter sensitivities for successful implementation. In each of the three MOCO strategies these parameter sensitivities are obtained using post-optimality analysis techniques.

Multiobjective Collaborative Optimization

1997 ◽  
Vol 119 (3) ◽  
pp. 403-411 ◽  
Author(s):  
R. V. Tappeta ◽  
J. E. Renaud
Keyword(s):  
System Design ◽  
Systems Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Accurate Estimation ◽  
Successful Implementation ◽  
Design Environment ◽  
Comprehensive Overview ◽  
Design Problems ◽  
Multidisciplinary Systems

This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this paper is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for Multiobjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily been applied to system design problems which have a single objective function. Recent investigations involving multidisciplinary design simulators have reported success in applying CO to multiobjective system design problems. In this research three Multiobjective Collaborative Optimization (MOCO) strategies are developed, reviewed and implemented in a comparative study. The goal of this effort is to provide an in depth comparison of different MOCO strategies available to system designers. Each of the three strategies makes use of parameter sensitivities within multilevel solution strategies. In implementation studies, each of the three MOCO strategies is effective in solving a multiobjective multidisciplinary systems design problem. Results indicate that these MOCO strategies require an accurate estimation of parameter sensitivities for successful implementation. In each of the three MOCO strategies these parameter sensitivities are obtained using post-optimality analysis techniques.

An integrated system design environment: Concepts and a status report

1992 ◽  
Vol 2 (4) ◽  
pp. 319-347 ◽  
Author(s):  
A. H. Dogru ◽  
S. N. Delcambre ◽  
C. Bayrak ◽  
Y. T. Chen ◽  
E. S. Chan ◽  
...  
Keyword(s):  
System Design ◽  
Integrated System ◽  
Status Report ◽  
Design Environment ◽  
Integrated System Design

Virtual Prototyping Simulation for Design of Mechanical Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 63-70 ◽  
Author(s):  
E. J. Haug ◽  
K. K. Choi ◽  
J. G. Kuhl ◽  
J. D. Wargo
Keyword(s):  
System Design ◽  
Mechanical System ◽  
Driving Simulator ◽  
Dynamic Performance ◽  
Virtual Prototyping ◽  
Mechanical Systems ◽  
Design Sensitivity ◽  
Vehicle Design ◽  
Design Environment ◽  
Analysis And Design

Developments in simulation technology that enable a qualitatively new virtual prototyping approach to design of mechanical systems are summarized and their integration into an engineering design environment is illustrated. Simulation tools and their enabling technologies are presented in the context of vehicle design, with references to the literature provided. Their implementation for design representation, real-time driver-in-the-loop simulation, dynamic performance simulation, dynamic stress and life prediction, maintainability analysis, design sensitivity analysis, and design optimization is outlined. A testbed comprised of computer aided engineering tools and a design level of fidelity driving simulator that has been developed to demonstrate the feasibility of virtual prototyping simulation for mechanical system design is presented. Two 1994 demonstrations of this capability for vehicle design are presented, to illustrate the state of the technology and to identify challenges that remain in making virtual prototyping simulation an integral part of mechanical system design in US industry.

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