Multidisciplinary design optimization under correlated uncertainties

2017 ◽  
Vol 25 (3) ◽  
pp. 262-275 ◽  
Author(s):  
Huanwei Xu ◽  
Wei Li ◽  
Liudong Xing ◽  
Shun-Peng Zhu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Quantitative Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Engineering System ◽  
Optimization Approach ◽  
Complex Mechanical Systems

Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimization approach based on the ellipsoidal set theory to investigate the characteristics of correlated uncertainties and incorporate their effects in the multidisciplinary design optimization through an advanced collaborative optimization method, where the quantitative model of correlated uncertainties is transformed into constrains of subsystems. Both a mathematical example and a case study of an engineering system are provided to illustrate feasibility and validity of the proposed method.

Multidisciplinary Design Optimization of a Lunar Lander’s Soft-Landing Gear

2010 ◽  
Vol 42 ◽  
pp. 118-121
Author(s):  
Yun Tong Lu ◽  
Chun Jie Wang ◽  
Ang Li ◽  
Han Wang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Rapid Development ◽  
Fem Analysis ◽  
Landing Gear ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Soft Landing ◽  
Multi Body

The rapid development of Multidisciplinary Design Optimization (MDO) approach can simultaneously guarantee the cut of cost on design and optimal performance of spacecraft. Based on the theory of Collaborative Optimization approach (CO) of MDO, present paper proposes the method of CO by integrating Pro/E(3D modeling), Patran/Nastran(FEM analysis) and ADAMS(multi-body dynamic analysis) with the Isight software. In the analysis of the soft-landing gear of Lunar Lander, this method can optimize the mass of the landing gear and meanwhile ensures the reliability of structure statics, structure dynamics and multi-body dynamics. Thus the feasibility, applied value and guideline significance of this method in spacecraft structural design are proven.

Multidisciplinary Design Optimization: A New Way to Bring Softer Bows

2008 ◽  
Author(s):  
Zhiqiang Hu ◽  
Weicheng Cui ◽  
Jianmin Yang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Force Density ◽  
Optimization Analysis ◽  
Resistance Reduction ◽  
Collision Force ◽  
Bulbous Bow

It is well known that sharp bulbous bow has a good performance on ship resistance reduction, but it is also threatens the struck ships and the environment greatly. For their own economy profit, ship owners would like the bulbous bow to be designed sharp and rigid. However, from the viewpoint of environmental protection, the bulbous bow should be designed blunt and soft. Multidisciplinary Design Optimization (MDO) is a prosperous design concept and technique, to reconcile this problem effectively. The basic concept and theories of MDO are introduced in this paper. An optimization analysis is accomplished on the bulbous bow design for a container ship, using Collaborative Optimization Method. The characters of the bulbous bow on resistance reduction, collision force density and structural strength requirement are all considered at the same time. A compatible bulbous bow can be obtained by this way.

Uncertainty-Based Multidisciplinary Design Optimization for Feedback-Coupled Systems Under Both Parametric and Metamodeling Uncertainties

2020 ◽  
Author(s):  
Zhao Liu ◽  
Zhouzhou Song ◽  
Ping Zhu ◽  
Can Xu
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Simulation Models ◽  
Coupled Systems ◽  
Multidisciplinary Design ◽  
Engineering System ◽  
Optimization Approach ◽  
Satellite Design ◽  
Consistency Constraints

Abstract Uncertainty-based multidisciplinary design optimization (UMDO) is an effective methodology to deal with uncertainties in the engineering system design. In order to shorten the design cycle and improve the design efficiency, the time-consuming computer simulation models are often replaced by metamodels, which consequently introduces metamodeling uncertainty into the UMDO procedure. The optimal solutions may deviate from the true results or even become infeasible if the metamodeling uncertainty is neglected. However, it is difficult to quantify and propagate the metamodeling uncertainty, especially in the UMDO process with feedback-coupled systems since the interdisciplinary consistency needs to be satisfied. In this paper, a new approach is proposed to solve the UMDO problem for the feedback-coupled systems under both parametric and metamodeling uncertainties. This approach adopts the decoupled formulation and it applies the Kriging technique to quantify the metamodeling uncertainty. The polynomial chaos expansion (PCE) technique is applied to propagate the two types of uncertainties and represent the interdisciplinary consistency constraints. In the optimization approach, the proposed method uses the iterative construction of PCE models for response means and variances to satisfy the multidisciplinary consistency at the optimal solution. The proposed approach is verified by a mathematical example and applied to the fire satellite design. The results demonstrate the proposed approach can solve the UMDO problem for coupled systems accurately and efficiently.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

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.

scholarly journals Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401875472 ◽  
Author(s):  
Wei Sun ◽  
Xiaobang Wang ◽  
Maolin Shi ◽  
Zhuqing Wang ◽  
Xueguan Song
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Hard Rock ◽  
Tunnel Boring Machine ◽  
Collaborative Optimization ◽  
Optimization Process ◽  
Multidisciplinary Design ◽  
Tunnel Boring ◽  
Geological Conditions ◽  
Rock Tunnel

A multidisciplinary design optimization model is developed in this article to optimize the performance of the hard rock tunnel boring machine using the collaborative optimization architecture. Tunnel boring machine is a complex engineering equipment with many subsystems coupled. In the established multidisciplinary design optimization process of this article, four subsystems are taken into account, which belong to different sub-disciplines/subsytems: the cutterhead system, the thrust system, the cutterhead driving system, and the economic model. The technology models of tunnel boring machine’s subsystems are build and the optimization objective of the multidisciplinary design optimization is to minimize the construction period from the system level of the hard rock tunnel boring machine. To further analyze the established multidisciplinary design optimization, the correlation between the design variables and the tunnel boring machine’s performance is also explored. Results indicate that the multidisciplinary design optimization process has significantly improved the performance of the tunnel boring machine. Based on the optimization results, another two excavating processes under different geological conditions are also optimized complementally using the collaborative optimization architecture, and the corresponding optimum performance of the hard rock tunnel boring machine, such as the cost and energy consumption, is compared and analysed. Results demonstrate that the proposed multidisciplinary design optimization method for tunnel boring machine is reliable and flexible while dealing with different geological conditions in practical engineering.

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