scholarly journals Design Optimization of a Concrete Face Rock-Fill Dam by Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yanlong Li ◽  
Jing Wang ◽  
Zengguang Xu
Keyword(s):  
Design Optimization ◽  
Optimization Design ◽  
Optimal Solution ◽  
Rock Fill ◽  
Design Variables ◽  
Complex Optimization ◽  
Global Optimal ◽  
Dam Stability ◽  
High Degree ◽  
Concrete Face

This paper combined with the adaptive principle to improve the genetic algorithms (GA) and applied it to optimal design of the shape of the concrete face rock-fill dam (CFRD). Based on the improved GA, a mathematical model was established for the design optimization of CFRD. CFRD utilizes dam cost as objective function and dam slope and geometries of the dam material partition as design variables. Dam stability, stress, displacement, and stress level are used as the main condition constraints. The calculation procedures were prepared, and the GA was used to optimize the design of Jishixia CFRD. Results show that the GA could solve the global optimal solution problem of complex optimization design, such as the high degree of nonlinearity and the recessiveness of constraint conditions, and using the GA to optimize the CFRD design can reduce the quantities of projects and engineering safety costs.

Structure Optimization Design for the Bed of Gear Hobbing Machine

2013 ◽  
Vol 765-767 ◽  
pp. 176-180
Author(s):  
Rong Chuang Zhang ◽  
Ao Xiang Liu ◽  
Jun Wang ◽  
Wan Shan Wang
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Element Model ◽  
Weighting Coefficient ◽  
Design Parameters ◽  
Analytic Hierarchy ◽  
Design Variables ◽  
Gear Hobbing ◽  
Vibration Modal ◽  
Hierarchy Process

In the optimization design of the gear hobbing machine bed, the finite element model is build and the static analysis and vibration modal analysis are performed. Then sensitivity analysis is used to gain the main design parameters which influence the bed property most. Furthermore, the multi-objective optimization design of the bed is performed in ANSYS Workbench with these design parameters as the design variables. At last, after all optimum proposals are showed up, Analytic Hierarchy Process is used to determine the weighting coefficient, and the most optimal solution is found out. As a result, the dynamic and static performances of the machine bed are improved under control of the machine bed mass.

Optimization Design and Buckling Analysis of ZLP500L Type Suspended Platform

2011 ◽  
Vol 211-212 ◽  
pp. 449-453
Author(s):  
Liang Zhao ◽  
Ke Zhang ◽  
Yu Hou Wu ◽  
Jia Sun ◽  
Hui Ye Yu
Keyword(s):  
Optimization Design ◽  
Element Model ◽  
Buckling Analysis ◽  
Structure Stability ◽  
State Variables ◽  
Total Quality ◽  
Design Variables ◽  
Static Performance ◽  
First Order Method ◽  
High Degree

To take ZLP500L type suspended platform for research object, establishes parametric finite element model with ANSYS, and analyze static performance of its structure. Define the platform section parameters as the design variables, and structure in dangerous working conditions of stress and displacement as state variables, makes optimization design of platform with first-order method, so obtain lightweight high degree of section parameters which reducing 26.16% total quality of platform. Meanwhile makes buckling analysis of optimized platform to obtain smallest the critical load coefficient is 171.56, and then verifies the platform structure stability, provides theory basis to ZLP500L type suspended platform design and alteration.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

Reliability-based design optimization of 3D orthogonal woven composite automobile shock tower

2021 ◽  
pp. 002199832110476
Author(s):  
Zhao Liu ◽  
Lei Zhang ◽  
Ping Zhu ◽  
Mushi Li
Keyword(s):  
Design Optimization ◽  
Composite Structures ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Woven Composite ◽  
Reliability Based Design Optimization ◽  
Multi Scale ◽  
Reliability Based Design ◽  
Design Variables ◽  
3D Orthogonal Woven

Three-dimensional orthogonal woven composites are noted for their excellent mechanical properties and delamination resistance, so they are expected to have promising prospects in lightweight applications in the automobile industry. The multi-scale characteristics and inherent uncertainty of design variables pose great challenges to the optimization procedure for 3D orthogonal woven composite structures. This paper aims to propose a reliability-based design optimization method for guidance on the lightweight design of 3D orthogonal woven composite automobile shock tower, which includes design variables from material and structure. An analytical model was firstly set up to accurately predict the elastic and strength properties of composites. After that, a novel optimization procedure was established for the multi-scale reliability optimization design of composite shock tower, based on the combination of Monte Carlo reliability analysis method, Kriging surrogate model, and particle swarm optimization algorithm. According to the results, the optimized shock tower meets the requirements of structural performance and reliability, with a weight reduction of 37.83%.

Multidisciplinary Robust Optimization Design of Multi-Functional Open-Air Hydraulic Drill

2010 ◽  
Vol 44-47 ◽  
pp. 1135-1140 ◽  
Author(s):  
You Xin Luo ◽  
Hui Jun Wen ◽  
Heng Shu Li
Keyword(s):  
Robust Optimization ◽  
Uncertainty Analysis ◽  
Design Optimization ◽  
Robust Design ◽  
Multidisciplinary Design Optimization ◽  
Design Theory ◽  
Optimization Design ◽  
Optimal Solution ◽  
Multidisciplinary Design ◽  
Robust Optimization Design

In this paper, the basic concepts and methods of multidisciplinary design optimization, uncertainty analysis and robust design have been introduced. According to the features of a multi-functional open-air hydraulic drill, a new design theory called multidisciplinary robust optimization design was discussed. This theory can undertake uncertainty analysis and robust design in multidisciplinary design optimization. It fully considers both the synergy among each disciplinary or subsystem in the multi-functional open-air hydraulic drill to get the optimal solution to the whole system and the effect of the uncertainty factors upon the drill quality, and adopts the parallel design to improve the quality, robustness and reliability of the drill, to shorten the market cycles of products, to reduce product cost. Finally, the design points were discussed in detail in the paper.

Fuzzy optimization design-based multi-level response surface of bogie frame

2019 ◽  
Vol 10 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Pengpeng Zhi ◽  
Yonghua Li ◽  
Bingzhi Chen ◽  
Meng Li ◽  
Guannan Liu
Keyword(s):  
Boundary Conditions ◽  
Response Surface ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Content Type ◽  
Design Variables ◽  
Multi Level ◽  
Response Surface Function

Purpose In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem. Design/methodology/approach The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination. Findings The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively. Originality/value Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

Aerodynamic Optimization and Inverse Design of 2D and 3D Turbine Cascades Using the Discrete Adjoint Method

2013 ◽  
Author(s):  
Juan Lu ◽  
Chaolei Zhang ◽  
Zhenping Feng
Keyword(s):  
Design Optimization ◽  
Optimization Design ◽  
Adjoint Method ◽  
Inverse Design ◽  
Aerodynamic Design ◽  
Discrete Adjoint ◽  
Design Variables ◽  
Discrete Adjoint Method ◽  
Turbine Cascades ◽  
2D And 3D

The adjoint method has significant advantage in sensitivity analysis because its computation cost is independent of the number of the design variables. In recent years it has been applied greatly in aerodynamic design optimization of turbomachinery. This paper developed the discrete adjoint method based on the authors’ previous work and demonstrated the applications of the method in the aerodynamic design optimization for turbine cascades. The Non-uniform Rational B-Spline (NURBS) technology was introduced in the current design optimization system and a flexible parameterization method for 3D cascade was proposed. Based on the parameterization method, the stack line and the blade profile are parameterized together by using NURBS curves. During the design process, the control points of the profile, the stack point and the stagger angle of the blade on each section can be taken as the design variables. Moreover, the flow solver and the discrete adjoint solver were extended towards the turbulent flow environment by adopting the k – ω turbulence model. Based on the optimization design system, several applications including two optimization design cases and two inverse design cases for 2D and 3D turbine cascades were implemented with the mass flow ratio constraint. The gradient verification and the numerical cases showed the correctness and accuracy of the discrete adjoint solver. The numerical results demonstrated the validity and efficiency of the design optimization system based on discrete adjoint method.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

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