scholarly journals Optimization for a Flexure Hinge Using an Effective Hybrid Approach of Fuzzy Logic and Moth-Flame Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Minh Phung Dang ◽  
Hieu Giang Le ◽  
Ngoc Le Chau ◽  
Thanh-Phong Dao
Keyword(s):  
Fuzzy Logic ◽  
Objective Function ◽  
Response Surface ◽  
Optimization Algorithm ◽  
Rotation Axis ◽  
Flexure Hinge ◽  
Objective Functions ◽  
Angular Deflection ◽  
Surface Method ◽  
Moth Flame Optimization Algorithm

Flexure hinge is a critical element in a positioner of a nanoindentation tester. To effectively work, a suitable flexure hinge should simultaneously meet multiple objectives, including rotation axis shift, safety factor, and angular deflection. The main aim of this article was to illustrate a hybrid method of the Taguchi method, fuzzy logic, response surface method, and Moth-flame optimization algorithm to solve the design optimization of a flexure hinge in order to enhance the three quality characteristics of the flexure hinge. Firstly, four common flexure hinges are compared together to seek the best suitable one. Secondly, numerical experiments are gathered via the Taguchi-based detasFlex software. Thirdly, three objective functions are transferred into signal to noises in order to eliminate the unit differences. Later on, fuzzy modeling is proposed to interpolate these three objective functions into one integrated objective function. An integrated regression equation is built using the response surface method. Finally, the flexure hinge is optimized by the Moth-flame optimization algorithm. The results found that the rotation axis shift is 10.944∗10−5 mm, the high safety factor is 2.993, and the angular deflection is 52.0058∗10−3 rad. The verifications are in a suitable agreement with the forecasted results. An analysis of variance and sensitivity analysis are also performed to identify the effects and meaningful contributions of input variables on the integrated objective function. In addition, employing the Wilcoxon signed rank test and the Friedman test, the results find that the proficiency of the proposed method has more benefits than the ASO algorithm and the GA. The results of this research provide a beneficial approach for conducting complicated multiobjective optimal problems.

ANN-Based Structure Optimization with Fatigue Reliability Constrains

2012 ◽  
Vol 204-208 ◽  
pp. 3128-3131
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Reliability Analysis ◽  
Response Surface ◽  
Minimum Weight ◽  
Structure Optimization ◽  
Fatigue Reliability ◽  
Ann Model ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization for considering fatigue reliability requirements in structural optimization was proposed. The ANN-based response surface method was employed for performing fatigue reliability analysis. The fatigue reliability requirements were considered as constraints while the weight as the objective function, the ANN model was adopted to establish the relationship between the fatigue reliability and geometry dimension of the structure, the optimal results of the structure with a minimum weight was reached.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

A Study on Improvement of Aerodynamic Performance for 100HP Axial Fan Blade and Guide Vane Using Response Surface Method

2019 ◽  
Author(s):  
Young-Seok Choi ◽  
Yong-In Kim ◽  
Sung Kim ◽  
Seul-Gi Lee ◽  
Hyeon-Mo Yang ◽  
...  
Keyword(s):  
Objective Function ◽  
Response Surface ◽  
Response Surface Method ◽  
Guide Vane ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Internal Flow ◽  
Aerodynamic Performance ◽  
Axial Fan ◽  
Surface Method

Abstract This paper describes the numerical optimization of an axial fan focused on the blade and guide vane (GV). For numerical analysis, three-dimensional (3D) steady-state Reynolds-averaged Navier-Stokes (RANS) equations with the shear stress transport (SST) turbulence model are discretized by the finite volume method (FVM). The objective function is enhancement of aerodynamic performance with specified total pressure. To select the design variables which have main effect to the objective function, 2k factorial design is employed as a method for design of experiment (DOE). In addition, response surface method (RSM) based on the central composite design applied to carry out the single-objective optimization. Effects on the components such as bell mouth and hub cap are considered with previous analysis. The internal flow characteristics between base and optimized model are analyzed and discussed.

Semiautomatic Multiple Resolution Design for History Matching

SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 408-419 ◽  
Author(s):  
Baoyan Li ◽  
Francois Friedmann
Keyword(s):  
Fluid Flow ◽  
Experimental Design ◽  
Objective Function ◽  
Response Surface ◽  
History Matching ◽  
Design Methodology ◽  
Production Data ◽  
Objective Functions ◽  
Flow Parameters ◽  
Production History

Summary History matching is an inverse problem in which an engineer calibrates key geological/fluid flow parameters by fitting a simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. History-match solutions are obtained by searching for minima of an objective function below a preselected threshold value. Experimental design and response surface methodologies provide an efficient approach to build proxies of objective functions (OF) for history matching. The search for minima can then be easily performed on the proxies of OF as long as its accuracy is acceptable. In this paper, we first introduce a novel experimental design methodology for semi-automatically selecting the sampling points, which are used to improve the accuracy of constructed proxies of the nonlinear OF. This method is based on derivatives of constructed proxies. We propose an iterative procedure for history matching, applying this new design methodology. To obtain the global optima, the proxies of an objective function are initially constructed on the global parameter space. They are iteratively improved until adequate accuracy is achieved. We locate subspaces in the vicinity of the optima regions using a clustering technique to improve the accuracy of the reconstructed OF in these subspaces. We test this novel methodology and history-matching procedure with two waterflooded reservoir models. One model is the Imperial College fault model (Tavassoli et al. 2004). It contains a large bank of simulation runs. The other is a modified version of SPE9 (Killough 1995) benchmark problem. We demonstrate the efficiency of this newly developed history-matching technique. Introduction History matching (Eide et al. 1994; Landa and Güyagüler 2003) is an inverse problem in which an engineer calibrates key geological/fluid flow parameters of reservoirs by fitting a reservoir simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. The traditional history matching is performed in a semi-empirical approach, which is based on the engineer's understanding of the field production behavior. Usually, the model parameters are adjusted using a one-factor-at-a-time approach. History matching can be very time consuming, because many simulation runs may be required for obtaining good fitting results. Attempts have been made to automate the history-matching process by using optimal control theory (Chen et al. 1974) and gradient techniques (Gomez et al. 2001). Also, design of experiment (DOE) and response surface methodologies (Eide et al. 1994; Box and Wilson 1987; Montgomery 2001; Box and Hunter 1957; Box and Wilson 1951; Damsleth et al. 1992; Egeland et al. 1992; Friedmann et al. 2003) (RSM) were introduced in the late 1990s to guide automatic history matching. The goal of these automatic methods is to achieve reasonably faster history-matching techniques than the traditional method. History matching is an optimization problem. The objective is to find the best of all possible sets of geological/fluid flow parameters to fit the production data of reservoirs. To assess the quality of the match, we define an OF (Atallah 1999). For history-matching problems, an objective function is usually defined as a distance (Landa and Güyagüler 2003) between a simulator's output and reservoir production data. History-matching solutions are obtained by searching for minima of the objective function. Experimental design and response surface methodologies provide an efficient approach to build up hypersurfaces (Kecman 2001) of objective functions (i.e., proxies of objective functions with a limited number of simulation runs for history matching). The search for minima can then be easily performed on these proxies as long as their accuracy is acceptable. The efficiency of this technique depends on constructing adequately accurate objective functions.

Multi-Functional Optimization of Sandwich Panel with Bio-Inspired Lightweight Structure Cores

2013 ◽  
Vol 461 ◽  
pp. 73-80
Author(s):  
Zhen Yu Lu ◽  
Ce Guo ◽  
Chun Sheng Zhu ◽  
Zhen Dong Dai
Keyword(s):  
Objective Function ◽  
Sampling Method ◽  
Sandwich Panel ◽  
Heat Dissipation ◽  
Latin Hypercube Sampling ◽  
Objective Functions ◽  
Functional Optimization ◽  
Effective Medium Model ◽  
Surface Method ◽  
Lightweight Structure

The overall heat dissipation coefficients of the sandwich panels with bio-inspired lightweight composite cores were analyzed by using the effective medium model, Then the effective heat dissipation objective function of the panel was constructed. The author took advantage of the Latin hypercube sampling method to sample the experimental data and then to calculate it. On this basis, the static and dynamic mechanical objective functions of the panel were obtained by using the response surface method. Finally, the overall objective function was constructed and solved to achieve the target of multi-functional optimization.

Sign in / Sign up

Export Citation Format

Share Document