Multidisciplinary Design Optimization for Complex Engineered Systems Design: State of the Research and State of the Practice—Report From a National Science Foundation Workshop

2011 ◽  
Author(s):  
Timothy W. Simpson ◽  
Joaquim R. R. A. Martins
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Systems Design ◽  
The State ◽  
Multidisciplinary Design ◽  
Research Center ◽  
Future Research ◽  
Government Agencies ◽  
Complex Engineered Systems ◽  
Engineered Systems

Multidisciplinary design optimization (MDO) has evolved remarkably since its inception 25 years ago. Despite these advances, the design of complex engineered systems remains a challenge, and many large-scale engineering projects are routinely plagued by exorbitant cost overruns and delays. To gain insight into these challenges, 48 people gathered from industry, academia, and government agencies to examine MDO’s current and future role in designing complex engineered systems. This paper summarizes the views of five distinguished speakers on the “state of the research” along with the discussions from an industry panel of representatives from Boeing, Caterpillar, Ford, NASA Glenn Research Center, and United Technologies Research Center on the “state of the practice”. This paper also summarizes the future research topics identified by breakout groups in five key areas: (1) modeling and the design space; (2) metrics, objectives, and requirements; (3) coupling in complex engineered systems; (4) dealing with uncertainty; and (5) people and workflow. Finally, five over-arching themes are offered to advance MDO. First, we need to engage more disciplines outside of engineering and look for opportunities to use MDO outside of its traditional areas. Second, MDO problem formulations must evolve to encompass a wider range of design criteria. Third, we need effective strategies for putting designers “back in the loop” during MDO. Fourth, we need to do a better job of publicizing the successful examples of MDO so that we can improve the “buy in” that is needed to advance MDO in academia, industry, and government agencies. Fifth, we need to better educate our students and practitioners on systems design, optimization, and MDO along with their benefits and drawbacks.

Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Timothy W. Simpson ◽  
Joaquim R. R. A. Martins
Keyword(s):  
Design Optimization ◽  
Systems Engineering ◽  
Multidisciplinary Design Optimization ◽  
Systems Design ◽  
The State ◽  
Multidisciplinary Design ◽  
Research Center ◽  
Government Agencies ◽  
Complex Engineered Systems ◽  
Engineered Systems

Complex engineered systems are typically designed using a systems engineering framework that is showing its limitations. Multidisciplinary design optimization (MDO), which has evolved remarkably since its inception 25 years ago, offers alternatives to complement and enhance the systems engineering approach to help address the challenges inherent in the design of complex engineered systems. To gain insight into these challenges, a one-day workshop was organized that gathered 48 people from industry, academia, and government agencies. The goal was to examine MDO’s current and future role in designing complex engineered systems. This paper summarizes the views of five distinguished speakers on the “state of the research” and discussions from an industry panel comprised of representatives from Boeing, Caterpillar, Ford, NASA Glenn Research Center, and United Technologies Research Center on the “state of the practice.” Future research topics to advance MDO are also identified in five key areas: (1) modeling and the design space, (2) metrics, objectives, and requirements, (3) coupling in complex engineered systems, (4) dealing with uncertainty, and (5) people and workflow. Finally, five overarching themes are offered to advance MDO practice. First, MDO researchers need to engage disciplines outside of engineering and target opportunities outside of their traditional application areas. Second, MDO problem formulations must evolve to encompass a wider range of design criteria. Third, effective strategies are needed to put designers “back in the loop” during MDO. Fourth, the MDO community needs to do a better job of publicizing its successes to improve the “buy in” that is needed to advance MDO in academia, industry, and government agencies. Fifth, students and practitioners need to be better educated on systems design, optimization, and MDO methods and tools along with their benefits and drawbacks.

scholarly journals Preliminary Sub-Systems Design Integrated in a Multidisciplinary Design Optimization Framework

2017 ◽  
Vol 2017 (4) ◽  
pp. 9-23
Author(s):  
Marco Fioriti ◽  
Luca Boggero ◽  
Sabrina Corpino
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Systems Design ◽  
Aircraft Design ◽  
Multidisciplinary Design ◽  
Design Parameters ◽  
Preliminary Design ◽  
Horizon 2020 ◽  
Optimization Framework ◽  
Complex Subject

Abstract The aircraft design is a complex subject since several and completely different design disciplines are involved in the project. Many efforts are made to harmonize and optimize the design trying to combine all disciplines together at the same level of detail. Within the ongoing AGILE (Horizon 2020) research, an aircraft MDO (Multidisciplinary Design Optimization) process is setting up connecting several design tools and competences together. Each tool covers a different design discipline such as aerodynamics, structure, propulsion and systems. This paper focuses on the integration of the sub-system design discipline with the others in order to obtain a complete and optimized aircraft preliminary design. All design parameters used to integrate the sub-system branch with the others are discussed as for their redefinition within the different detail level of the design.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

2012 ◽  
Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen
Keyword(s):  
Design Optimization ◽  
Support Vector Regression ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Initial Point ◽  
Ship Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Support Vector ◽  
Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

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