Reliability design optimization for a pressure shell based on a response surface model

2015 ◽  
Author(s):  
J. Wang ◽  
Y. J. Pang ◽  
Z. Y. Yang ◽  
L. H. Zhang
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Response Surface Model ◽  
Surface Model ◽  
Reliability Design

scholarly journals XU-RSM: Diffuse response surface model for reliability-based design optimization

2010 ◽  
Vol 10 ◽  
pp. 012209
Author(s):  
P P Zhang ◽  
P Breitkopf ◽  
C Vayssade ◽  
W H Zhang ◽  
H Tao
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Response Surface Model ◽  
Surface Model ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

Multi-Objective Aerodynamic Design Optimization Based on Camber Line and Thickness Distribution for a Transonic Compressor Rotor

2008 ◽  
Author(s):  
Xiangfeng Wang ◽  
Songtao Wang ◽  
Wanjin Han
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Response Surface ◽  
Total Pressure ◽  
Optimization Problems ◽  
Thickness Distribution ◽  
Response Surface Model ◽  
Navier Stokes ◽  
Surface Model ◽  
Multi Objective

The paper describes a new optimization system for computationally expensive design optimization problems of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), multi-objective genetic algorithm (MOGA) and a 3-D Navier-Stokes solver. A flow field solver code was developed based on three dimensional Navier-Stokes equations and validated by comparing computation results with experimental data. The improved non-dominated sorting genetic algorithm (NSGA-II) was used to solve the multi-objective problems. A constraint handling method without penalty function used to treat constrained optimization problems was improved and applied to constrained multi-objective problems. Data points for response evaluations were selected by the improved-hypercube sampling (IHS) algorithm and 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. The genetic algorithm was applied to the response surface model to perform global optimization and obtain the optimum design. The above optimization method was applied to aerodynamic redesign of NASA Rotor37 with camber line and thickness distribution, the objects were to maximize the total pressure ratio and the adiabatic efficiency. Results showed the adiabatic efficiency improved by 0.7% and the total pressure by 0.66%. The multi-objective optimization design method is feasible.

Approximated Unimodal Region Elimination Based Global Optimization Method for Engineering Design

2007 ◽  
Author(s):  
Adel Younis ◽  
Ruoning Xu ◽  
Zuomin Dong
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Response Surface ◽  
Computer Analysis ◽  
Optimization Problems ◽  
Response Surface Model ◽  
Design Experiment ◽  
Surface Model ◽  
Experiment Data ◽  
Computation Efficiency

Computer analysis and simulation based design optimization requires more computationally efficient global optimization tools. In this work, a new global optimization algorithm based on design experiments, region elimination and response surface model, namely Approximated Unimodal Region Elimination Method (AUREM), is introduced. The approach divides the field of interest into several unimodal regions using design experiment data; identify and rank the regions that most likely contain the global minimum; form a response surface model with additional design experiment data over the most promising region; identify its minimum, remove this processed region, and move to the next most promising region. By avoiding redundant searches, the approach identifies the global optimum with reduced number of objective function evaluations and computation effort. The new algorithm was tested using a variety of benchmark global optimization problems and compared with several widely used global optimization algorithms. The experiments results present comparable search accuracy and superior computation efficiency, making the new algorithm an ideal tool for computer analysis and simulation black-box based global design optimization.

Design Optimization of Transonic Compressor Rotor Using CFD and Genetic Algorithm

2006 ◽  
Author(s):  
Weilin Yi ◽  
Hongyan Huang ◽  
Wanjin Han
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Response Surface ◽  
Optimization Design ◽  
Optimization Problems ◽  
Response Surface Model ◽  
Navier Stokes ◽  
Surface Model ◽  
3 Dimensional ◽  
Transonic Compressor

The paper describes a new optimization strategy for computationally expensive design optimization problems of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver. Data points for response evaluations were selected by Latin hypercube design (LHD) and 3-dimensional Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. The genetic algorithm was applied to the response surface model to perform global optimization to obtain the optimum design. The above method was applied to the optimization design of NASA rotor37. The object was to maximize the adiabatic efficiency. An optimum leading edge line was found which produced a new 3-dimensional blade combined with sweep and composite bowing. As a result of this optimization, the adiabatic efficiency was successfully increased by 1.58%. It was found that the strategy of this paper provides a reliable design optimization method for turbomachinery blades at reasonable computing cost.

Fuzzy multi-objective optimization of a train suspension using response surface model

2000 ◽  
Author(s):  
Tael Lee ◽  
Kwangki Lee ◽  
Chankyoung Park
Keyword(s):  
Response Surface ◽  
Response Surface Model ◽  
Surface Model ◽  
Multi Objective Optimization ◽  
Multi Objective

Secondary Nozzle of HYLTE Nozzle Optimization Based on Response Surface Model

2008 ◽  
Author(s):  
Jing Lei ◽  
Lin Lai ◽  
Zhen-guo Wang
Keyword(s):  
Response Surface ◽  
Response Surface Model ◽  
Surface Model
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