Combining Marketing and Engineering Tools for Multi-Attribute Optimization

Consumer Preferences ◽

Computational Cost ◽

The Past ◽

Single Objective

Multidisciplinary design optimization has been an active topic of research in the past two decades in developing algorithms for reducing computational cost of re-analysis and also in developing efficient ways of calculating sensitivities. Most of the efforts were aimed at single objective function (attribute). Also very little work is done to include designer’s preferences inside the optimization. In this paper, conjoint analysis, a popular marketing technique to assess consumer preferences is used to involve the preferences of the designer. The optimization is driven by the designer’s preferences and a preferred design is obtained. Here, a novel way of combining tools from marketing and engineering is shown. A cantilever beam, and a composite lightweight torpedo are used as examples to demonstrate the method.

Multidisciplinary Design Optimization of Modular Industrial Robots

Volume 5: 37th Design Automation Conference, Parts A and B ◽

10.1115/detc2011-48196 ◽

2011 ◽

Cited By ~ 3

Author(s):

Mehdi Tarkian ◽

Johan Persson ◽

Johan O¨lvander ◽

Xiaolong Feng

Keyword(s):

Design Optimization ◽

Structural Strength ◽

Computational Cost ◽

Industrial Robots ◽

Geometry Model ◽

Optimization Framework ◽

Speed Up ◽

Multi Level

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model. The proposed framework utilizes well-established methods such as metamodeling and multi-level optimization in order to speed up the design optimization process. The contribution of the paper is to show that by applying a merger of well-established methods, the computational cost can be cut significantly, enabling search for truly novel concepts.

An Approach Based on Enhanced Collaborative Optimization and Kriging Approximation in Multidisciplinary Design Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.118-120.399 ◽

2010 ◽

Vol 118-120 ◽

pp. 399-403 ◽

Cited By ~ 2

Author(s):

M. Xiao ◽

Liang Gao ◽

Hao Bo Qiu ◽

Xin Yu Shao ◽

Xue Zheng Chu

Keyword(s):

Design Optimization ◽

Computational Cost ◽

Simulation Models ◽

Collaborative Optimization ◽

Comprehensive Strategy ◽

Speed Reducer ◽

Kriging Approximation ◽

Approximation Models

This paper concentrates on the computational challenge in multidisciplinary design optimization (MDO) and a comprehensive strategy combining enhanced collaborative optimization (ECO) and kriging approximation models is introduced. In this strategy, the computational and organizational advantages of original collaborative optimization (CO) are inherited by ECO, which can satisfy the strengthened consistency requirements. Kriging approximation models are constructed to replace high-fidelity simulation models in individual disciplines and reduce the expensive computational cost in practical MDO problems. The proposed methodology is demonstrated by solving the classical speed reducer design problem. The better results indicate that ECO using kriging approximation models can achieve a considerable reduction of computational expense while guaranteeing the accuracy of optimal solutions with efficient convergence.

Improved Multidisciplinary Design Optimization Based on Genetic Algorithm and Artificial Neural Networks and Its Application

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5593 ◽

2016 ◽

Vol 13 (10) ◽

pp. 6501-6508

Author(s):

Yi Su ◽

Fa-Yin Wang ◽

Jian-Yu Zhao

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Networks ◽

Design Optimization ◽

Computational Cost ◽

System Level ◽

Local Optimum ◽

Artificial Neural

Multidisciplinary Design Optimization (MDO) is an algorithm widely used in the engineering field currently. However, traditional MDO often leads to the failure of convergence or local optimum problems caused by convergence. In such cases, a multidisciplinary design optimization based on genetic algorithm (GA) and artificial neural networks (ANN) (GA-ANN-MDO) is presented in the paper. Under the thought of parallel distribution of traditional MDO, the real sub-disciplinary model is replaced by a highly precise ANN model dependent on the Latin Hypercube experimental design method in the GA-ANN-MDO, so as to reduce the computational cost and smooth the value noise. The GA optimization system level is applied to decline the possibility of partial solution involved in the optimization. As shown from the optimization results of two classic mathematical examples, GA-ANN-MDO is presented good robustness, which could quickly and effectively converge to the global optimal solution. In addition, a project example was employed finally to verify the feasibility of GA-ANN-MDO in the engineering.

Application of Multidisciplinary Design Optimization Based on Approximation Model to Marine Supercharger Turbo

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.139-141.1396 ◽

2010 ◽

Vol 139-141 ◽

pp. 1396-1399

Author(s):

Lei Li ◽

Zhen Zhou Lv ◽

Liang Bo Ao ◽

Ming Yu ◽

Zhu Feng Yue

Keyword(s):

Design Optimization ◽

Optimization Problems ◽

Computational Cost ◽

Computational Time ◽

Approximation Model ◽

Aerodynamic Model ◽

Multidisciplinary Analysis ◽

Distance Weighted

In this paper, the multidisciplinary design optimization based on Approximation Model for supercharge turbo is studied. Temperature and pressure loads are transferred to the solid model by distance-weighted function, and structure deformation is transferred to aerodynamic model by mesh regenerated method in order to avoid mesh aberration. The Multidisciplinary analysis (MDA) model of supercharge turbo considering aerodynamic, heat transfer, strength and vibration is obtained on the basis of information transferring, which is solved by iterated three times. The Kriging Approximation Model which fits the sample space accurately is employed in the MDO process to reduce computational cost. Results show that performance of supercharge turbo is improvement on the MDO system based on Approximation Model, meanwhile the computational time of the optimization system is saved. Also, this method is suitable for other Multidisciplinary Design Optimization problems.

Metamodel-based multidisciplinary design optimization methods for aerospace system

Astrodynamics ◽

10.1007/s42064-021-0109-x ◽

2021 ◽

Vol 5 (3) ◽

pp. 185-215

Author(s):

Renhe Shi ◽

Teng Long ◽

Nianhui Ye ◽

Yufei Wu ◽

Zhao Wei ◽

...

Keyword(s):

Design Optimization ◽

Computational Cost ◽

Optimization Methods ◽

Problem Formulation ◽

Black Box ◽

Optimization Techniques ◽

Multidisciplinary Optimization ◽

Aerospace Systems

AbstractThe design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

High-Fidelity Multidisciplinary Design Optimization Methodology with Application to Rotor Blades

Journal of the American Helicopter Society ◽

10.4050/jahs.64.032002 ◽

2019 ◽

Vol 64 (3) ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Li Wang ◽

Boris Diskin ◽

Robert T. Biedron ◽

Eric J. Nielsen ◽

Valentin Sonneville ◽

...

Keyword(s):

Fluid Dynamics ◽

Sensitivity Analysis ◽

Design Optimization ◽

Turbulent Flows ◽

Computational Cost ◽

Coupled System ◽

High Fidelity ◽

Variable Approach

A multidisciplinary design optimization procedure has been developed and applied to rotorcraft simulations involving tightly coupled, high-fidelity computational fluid dynamics and comprehensive analysis. A discretely consistent, adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows on unstructured, dynamic, overset meshes, whereas a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system for high-fidelity rotorcraft analysis is verified; simulation results exhibit good agreement with established solutions. A constrained gradient-based design optimization for a HART-II rotorcraft configuration is demonstrated. The computational cost for individual components of the multidisciplinary sensitivity analysis is assessed and improved.