Multidisciplinary design optimization of stamped and welded structures

Material Selection ◽

Real Life ◽

Element Model ◽

Welded Structures ◽

Loading Case ◽

Wheel Chair ◽

The Cost

Multidisciplinary design optimization (MDO) is very useful in present day engineering. The aim of this thesis is to develop and utilize an MDO procedure that can be applied to stamped and welded structures. This procedure involves new techniques such as material selection, weld constraint, and cost optimizations. The MDO is developed through five design iterations starting with a simple finite element model. As more techniques are added, the procedure progresses towards using a real life radiator support structure in a static loading case. Three trials were completed to optimize the cost of the structure; the final result is that the total cost was minimized by 20%. The MDO procedure was also applied to a real life wheel chair ramp model from a modified minivan. This structure was subject to a rear crash situation and the total mass, after the procedure was applied, was reduced by 19%.

Multidisciplinary Design Optimization of Driving Axle Housing Using Sparse Grid Approach

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.224.82 ◽

2012 ◽

Vol 224 ◽

pp. 82-86 ◽

Cited By ~ 2

Author(s):

Shi Lei Ma ◽

Fang Yi Li ◽

Yang He ◽

Qing Zhong Xu

Keyword(s):

Design Optimization ◽

Lightweight Design ◽

Dynamic Performance ◽

Optimal Solution ◽

Element Model ◽

Sparse Grid ◽

Housing System ◽

Grid Approach

In order to improve the engineering performance of lightweight design on the driving axle housing, lightweight, structural mechanics, fatigue strength and dynamics are applied in the multidisciplinary design optimization. Firstly, finite element model of driving axle housing was established and its accuracy was verified through bench tests. Secondly, driving axle housing system was divided into multiple sub-discipline systems and design variables of multidisciplinary lightweight design were determined, in order to solve the problems of large amount of data transmission and complex calculation, sparse grid approach was used to establish high accuracy approximate model of each discipline. Lastly, mass of driving axle housing and difference values of first six order modal frequencies before and after lightweight design were optimized through Non-dominated Sorted Genetic Algorithm-Ⅱ, the Pareto optimal solution set was obtained. In optimization results, masses of driving axle housing are all decreased compared to the initial design, meanwhile, the dynamic performance, structural static intensity and fatigue life are all ensured.

Adding Control in Multidisciplinary Design Optimization of a Wing for Active Flutter Suppression

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-0892 ◽

2021 ◽

Author(s):

Emmeline Faïsse ◽

Robin Vernay ◽

Fabio Vetrano ◽

Daniel Alazard ◽

Joseph Morlier

Keyword(s):

Design Optimization ◽

Flutter Suppression ◽

Active Flutter Suppression

Approaches to Multidisciplinary Design Optimization

10.21236/ada385813 ◽

2001 ◽

Cited By ~ 3

Author(s):

Timothy W. Simpson

Keyword(s):

Design Optimization ◽

Multidisciplinary Design

Multidisciplinary Design Optimization for High Reliability and Robustness

10.21236/ada442871 ◽

2005 ◽

Author(s):

Ramana Grandhi

Keyword(s):

Design Optimization ◽

High Reliability ◽

Reliability And Robustness

Multidisciplinary design optimization of underwater glider for improving endurance

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-021-02844-z ◽

2021 ◽

Author(s):

Shuxin Wang ◽

Ming Yang ◽

Wendong Niu ◽

Yanhui Wang ◽

Shaoqiong Yang ◽

...

Keyword(s):

Design Optimization ◽

Underwater Glider

Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

Journal of Marine Science and Engineering ◽

10.3390/jmse9050478 ◽

2021 ◽

Vol 9 (5) ◽

pp. 478

Author(s):

Hao Chen ◽

Weikun Li ◽

Weicheng Cui ◽

Ping Yang ◽

Linke Chen

Keyword(s):

Design Optimization ◽

Optimization Algorithm ◽

Robotic Fish ◽

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

Volume 1: Offshore Technology ◽

10.1115/omae2012-83086 ◽

2012 ◽

Author(s):

Dongqin Li ◽

Yifeng Guan ◽

Qingfeng Wang ◽

Zhitong Chen

Keyword(s):

Design Optimization ◽

Support Vector Regression ◽

Design Process ◽

Initial Point ◽

Ship Design ◽

Collaborative Optimization ◽

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.