An Efficient Multi-Objective Optimization Method for Complicated Vehicle Random Vibration

2011 ◽  
Vol 94-96 ◽  
pp. 1694-1700
Author(s):  
Wen Tao Xu ◽  
Tong Chen Miao ◽  
Zheng Liu
Keyword(s):  
Random Vibration ◽  
Optimization Methods ◽  
Kriging Model ◽  
Optimization Method ◽  
Dynamic Responses ◽  
Gaussian Random Process ◽  
Multi Objective ◽  
The Road ◽  
Multi Objective Programming ◽  
Design Variables

In this paper, a new approach is proposed and addressed for designing vehicle suspension systems based on the scheme of multi-objective programming. For complicated vehicle random vibration, a linear model is used to describe the dynamic behavior of vehicles running on randomly profiled roads. The road irregularity is regarded as a Gaussian random process. Pesudo excitation method has been used to solve the dynamic responses. And a Kriging model is introduced to build the approximate mapping relationship between the design variables and the responses. Optimal solutions are derived by means of the method of centers for structural optimization with multiple objectives. Numerical examples are given, and compared with other optimization methods.

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

1999 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Structural Design ◽  
Hierarchical Structures ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Problems ◽  
Design Variables ◽  
The Hierarchical Structure ◽  
Machine Systems ◽  
System Designs ◽  
Crossover And Mutation

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

A Multi-Dwell Temperature Profile Design for the Cure of Thick CFRP Composite Laminates

2021 ◽  
Author(s):  
Wenchang Zhang ◽  
Yingjie Xu ◽  
Xinyu Hui ◽  
Weihong Zhang
Keyword(s):  
Temperature Profile ◽  
Composite Laminates ◽  
Optimization Method ◽  
Maximum Temperature ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimization Result ◽  
Design Variables ◽  
Cure Time ◽  
Thick Laminates

Abstract This paper develops a multi-objective optimization method for the cure of thick composite laminates. The purpose is to minimize the cure time and maximum temperature overshoot in the cure process by designing the cure temperature profile. This method combines the finite element based thermo-chemical coupled cure simulation with the non-dominated sorting genetic algorithm-II (NSGA-II). In order to investigate the influence of the number of dwells on the optimization result, four-dwell and two-dwell temperature profiles are selected for the design variables. The optimization method obtains successfully the Pareto optimal front of the multi-objective problem in thick and ultra-thick laminates. The result shows that the cure time and maximum temperature overshoot are both reduced significantly. The optimization result further illustrates that the four-dwell cure profile is more e ective than the two-dwell, especially for the ultra-thick laminates. Through the optimization of the four-dwell profile, the cure time is reduced by 51.0% (thick case) and 30.3% (ultra-thick case) and the maximum temperature overshoot is reduced by 66.9% (thick case) and 73.1% (ultra-thick case) compared with the recommended cure profile. In addition, Self-organizing map (SOM) is employed to visualize the relationships between the design variables with respect to the optimization result.

scholarly journals The Dynamic Characteristic of a Faulted Rotor System with Multi-Objective Optimization Designed SFD

2019 ◽  
Vol 9 (17) ◽  
pp. 3628 ◽  
Author(s):  
Liang Ma ◽  
Jun Wang ◽  
Guichang Zhang
Keyword(s):  
Fundamental Frequency ◽  
Working Conditions ◽  
Optimization Method ◽  
Rotor System ◽  
Dynamic Responses ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Coupling Fault ◽  
Misalignment Fault

As an important part of the turbomachinery, the rotor–bearing system has been upgraded to provide a high rotating speed in order to meet the demand of high power production. With increasing demand for stability, the squeeze film damper (SFD) has been widely used in industrial machinery because it can reduce the vibration amplitude and suppress the external force. Usually, it shows inadaptability under the different working conditions where the SFD parameters didn’t change appropriately. Therefore, the reasonable choice of operational parameters of SFD is the key solution that can provide viscous damping effectively and restrain the nonlinear vibration generated by faults. In this paper, the mathematical model of a rotor-ball bearing-SFD system considering the misalignment fault and misalignment-rubbing coupling fault is built first. Then the dynamic characteristics under typical working conditions (ω = 1000 rad/s) of the faulted rotor are discussed. The vibration attenuation effects of the SFD parameters selected by using the multi-objective optimization method on the dynamic responses are analyzed. The results show that when the rotor system operates under different states, the value and the sensitivity of optimization parameters are altered. With no fault, the amplitude of fundamental frequency decrease 23%. With the misalignment fault, the amplitude of the fundamental frequency decreases by 43.4%, the amplitude of 2× fundamental frequency decreases by 27.5%, and the amplitude of 3× fundamental frequency decreases by 66.7%. With the misalignment-rubbing coupling fault, the amplitude of fundamental frequency reduces by 7.4%, the amplitude of 2× fundamental frequency drops by 51.5%, and the amplitude of 3× fundamental frequency drops by 16.8%. Overall, the feasibility of the optimization method of the variable-structured SFD operational parameters for the faulted rotor system is verified. These parametric analyses are very helpful in the development of a high-speed rotor system and provide a theoretical reference for the vibration control and optimal design of rotating machinery.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

Design Optimization of Valveless DTH Pneumatic Hammers by a Weighted Pseudo-Gradient Search Method

1998 ◽  
Vol 120 (4) ◽  
pp. 687-694 ◽  
Author(s):  
L. E. Chiang ◽  
E. B. Stamm
Keyword(s):  
Dynamic Model ◽  
Linear Equations ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Variables ◽  
Standard Procedures ◽  
Non Linear ◽  
Predicted Values ◽  
Gradient Search Method ◽  
Non Linear Equations

A design methodology for Down-The-Hole (DTH) pneumatic hammers used for rock drilling is proposed which renders an optimal design for a given set of constraints. A generic non-linear dynamic model developed by the authors is used to compute the hammer performance. This model consists of a set of six differential equations plus a set of twenty non-linear polynomial equations. In addition there are parameter range restrictions given by fabrication and operational standard procedures. In any given application, magnitudes such as power, impact energy, frequency, efficiency and mass flow may be sought for optimality. However these magnitudes must be computed by integration after solving the dynamic model over an entire cycle, thus traditional optimization methods for non-linear equations that are based in gradient information are not suitable. Hence a method that uses secant information is used to approximate the gradient of the space of design variables. Several prototypes using this optimization method have been designed and field tested. The results are in agreement with predicted values.

scholarly journals A Smart-Distributed Pareto Front Using the ev-MOGA Evolutionary Algorithm

2014 ◽  
Vol 23 (02) ◽  
pp. 1450002 ◽  
Author(s):  
J. M. Herrero ◽  
G. Reynoso-Meza ◽  
M. Martínez ◽  
X. Blasco ◽  
J. Sanchis
Keyword(s):  
Pareto Front ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initial Condition ◽  
Multi Objective Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Normalized Normal Constraint ◽  
Normal Constraint

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

Multi-Objective Design Optimization of Cooling Turbine Blade Based on Kriging Model

2012 ◽  
Vol 184-185 ◽  
pp. 316-319
Author(s):  
Liang Bo Ao ◽  
Lei Li ◽  
Yuan Sheng Li ◽  
Zhi Xun Wen ◽  
Zhu Feng Yue
Keyword(s):  
Design Optimization ◽  
Turbine Blade ◽  
Optimization Model ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Optimization Method ◽  
Approximate Model ◽  
Multi Objective ◽  
Pin Fin

The multi-objective design optimization of cooling turbine blade is studied using Kriging model. The optimization model is created, with the diameter of pin fin at the trailing edge of cooling turbine blade and the location, width, height of rib as design variable, the blade body temperature, flow resistance loss and aerodynamic efficiency as optimization object. The sample points are selected using Latin hypercube sampling technique, and the approximate model is created using Kriging method, the set of Pareto-optimal solutions of optimization objects is obtained by the multi-object optimization model using elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) based on the approximate model. The result shows that the conflict among all optimization objects is solved effectively and the feasibility of the optimization method is improved.

DBD Plasma Actuator Multi-Objective Design Optimization at Reynolds Number 63,000: Baseline Case

2013 ◽  
Author(s):  
Taufik Sulaiman ◽  
Satoshi Sekimoto ◽  
Tomoaki Tatsukawa ◽  
Taku Nonomura ◽  
Akira Oyama ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Design Optimization ◽  
Linear Trend ◽  
Lift Coefficient ◽  
Optimization Method ◽  
Input Power ◽  
Plasma Actuator ◽  
Dbd Plasma ◽  
Multi Objective ◽  
Design Variables

The working parameters of the dielectric barrier discharge (DBD) plasma actuator were optimized to gain an understanding of the flow control mechanism. Experiments were conducted at a Reynolds number of 63,000 using a NACA 0015 airfoil which was fixed to the stall angle of 12 degrees. The two objective functions are: 1) power consumption (P) and 2) lift coefficient (Cl). The goal of the optimization is to decrease P while maximizing Cl. The design variables consist of input power parameters. The algorithm was run for 10 generations with a total population of 260 solutions. Although the number of generations and population size was limited due to experimental constraints, the algorithm was able to converge and the approximate Pareto-front was obtained. From the objective function space, we observe a relatively linear trend where Cl increases with P and after a certain threshold, the value of Cl seems to saturate. We discuss the results obtained in the objective space in addition to scatter plot matrix and color maps. This article, with its experiment-based approach, demonstrates the robustness of a Multi-Objective Design Optimization method and its feasibility for wind tunnel experiments.

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