Multidisciplinary design environment development for air vehicle engineering

1992 ◽  
Author(s):  
J. VOLK
Keyword(s):  
Multidisciplinary Design ◽  
Vehicle Engineering ◽  
Design Environment ◽  
Air Vehicle

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.

Multidisciplinary Design and Prototype Development of a Micro Air Vehicle

1999 ◽  
Vol 36 (1) ◽  
pp. 227-234 ◽  
Author(s):  
Masoud Rais-Rohani ◽  
George R. Hicks
Keyword(s):  
Micro Air Vehicle ◽  
Multidisciplinary Design ◽  
Prototype Development ◽  
Air Vehicle

Development of a multidisciplinary design optimization framework for an efficient supersonic air vehicle

2015 ◽  
Vol 2 (1) ◽  
pp. 17-44 ◽  
Author(s):  
Darcy L. Allison ◽  
Craig C. Morris ◽  
Joseph A. Schetz ◽  
Rakesh K. Kapania ◽  
Layne T. Watson ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Air Vehicle

AN EXPERIMENTAL MULTI-AGENT ENVIRONMENT FOR ENGINEERING DESIGN

1996 ◽  
Vol 05 (02n03) ◽  
pp. 131-151 ◽  
Author(s):  
WEIMING SHEN ◽  
JEAN-PAUL A. BARTHES
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Multidisciplinary Design ◽  
Design Teams ◽  
Efficient Design ◽  
Design Environment ◽  
Open Design ◽  
Long Time ◽  
Design Activities ◽  
The Individual

Real world engineering design projects require the cooperation of multidisciplinary design teams using sophisticated and powerful engineering tools. The individuals or the individual groups of the multidisciplinary design teams work in parallel and independently often for quite a long time with different tools located on various sites. In order to ensure the coordination of design activities in the different groups or the cooperation among the different tools, it is necessary to develop an efficient design environment. This paper discusses a distributed architecture for integrating such engineering tools in an open design environment, organized as a population of asynchronous cognitive agents. Before introducing the general architecture and the communication protocol, issues about an agent architecture and inter-agent communications are discussed. A prototype of such an environment with seven independent agents located in several workstations and microcomputers is then presented and demonstrated on an example of a small mechanical design.

scholarly journals Multidisciplinary design optimization of the flapping wing system for forward flight

2017 ◽  
Vol 9 (2) ◽  
pp. 93-110
Author(s):  
Jung-Sun Choi ◽  
Gyung-Jin Park
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Flapping Wing ◽  
Multidisciplinary Design ◽  
Vehicle Model ◽  
Thrust Coefficient ◽  
Analysis And Design ◽  
Aeroelastic Analysis ◽  
Air Vehicle

The success of a flapping wing air vehicle flight is strongly related to the flapping motion and wing structure. Various disciplines should be considered for analysis and design of the flapping wing system. A design process for a flapping wing system is defined by using multidisciplinary design optimization. Unsteady aeroelastic analysis is employed as the system analysis. From the results of the aeroelastic analysis, the deformation of the wing is transmitted to the fluid discipline and the dynamic pressure is conveyed to the structural discipline. In the fluid discipline, a kinematic optimization problem is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency simultaneously. In the structural discipline, nonlinear dynamic topology optimization is performed to find the distribution of reinforcement by using the equivalent static loads method for nonlinear static response structural optimization. The defined design process is applied to a flapping wing air vehicle model and the flapping wing air vehicle model is fabricated based on the optimization results.

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.

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