scholarly journals Optimization of geometric parameters of arch bridges using visual programming FEM components and genetic algorithm

2021 ◽  
Vol 241 ◽  
pp. 112465
Author(s):  
Kamil Korus ◽  
Marek Salamak ◽  
Marcin Jasiński
Keyword(s):  
Genetic Algorithm ◽  
Visual Programming ◽  
Geometric Parameters ◽  
Arch Bridges

Genetic algorithm application for optimization of geometry of synchronous electric motor with permanent magnets by criterion of electromagnetic torque

2019 ◽  
pp. 56-62
Author(s):  
I. N. Belezyakov ◽  
K. G. Arakancev
Keyword(s):  
Genetic Algorithm ◽  
Electric Motor ◽  
Permanent Magnets ◽  
Evolutionary Optimization ◽  
Electric Machines ◽  
Geometric Parameters ◽  
Geometrical Parameters ◽  
Electromagnetic Torque ◽  
Automatic Control Systems ◽  
Optimization Of Geometry

At present time there is a need to develop a methodology for electric motors design which will ensure the optimality of their geometrical parameters according to one or a set of criterias. With the growth of computer calculating power it becomes possible to develop methods based on numerical methods for electric machines computing. The article describes method of a singlecriterion evolutionary optimization of synchronous electric machines with permanent magnets taking into account the given restrictions on the overall dimensions and characteristics of structural materials. The described approach is based on applying of a genetic algorithm for carrying out evolutionary optimization of geometric parameters of a given configuration of electric motor. Optimization criteria may be different, but in automatic control systems high requirements are imposed to electromagnetic torque electric machine produces. During genetic algorithm work it optimizes given geometric parameters of the electric motor according to the criterion of its torque value, which is being calculated using finite element method.

scholarly journals Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm

2012 ◽  
Vol 6 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Seiji Aoyagi ◽  
◽  
Masato Suzuki ◽  
Tomokazu Takahashi ◽  
Jun Fujioka ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Tracking System ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Geometric Parameters ◽  
Geometric Errors ◽  
Robot Arm ◽  
Positioning Accuracy ◽  
Laser Tracking

Offline teaching based on high positioning accuracy of a robot arm is desired to take the place of manual teaching. In offline teaching, joint angles are calculated using a kinematic model of the robot arm. However, a nominal kinematic model does not consider the errors arising in manufacturing or assembly, not to mention the non-geometric errors arising in gear transmission, arm compliance, etc. Therefore, a method of precisely calibrating the parameters in a kinematic model is required. For this purpose, it is necessary to measure the three-dimensional (3-D) absolute position of the tip of a robot arm. In this paper, a laser tracking system is employed as the measurement apparatus. The geometric parameters in the robot kinematic model are calibrated by minimizing errors between the measured positions and the predicted ones based on the model. The residual errors caused by non-geometric parameters are further reduced by using neural networks, realizing high positioning accuracy of sub-millimeter order. To speed up the calibration process, a smaller number of measuring points is preferable. Optimal measuring points, which realize high positioning accuracy while remaining small in number, are selected using Genetic Algorithm (GA).

Optimization of geometric parameters of latticed structures using genetic algorithm

2011 ◽  
Vol 83 (2) ◽  
pp. 59-68 ◽  
Author(s):  
Amir H. Hashemian ◽  
Mohammad H. Kargarnovin ◽  
Jafar E. Jam
Keyword(s):  
Genetic Algorithm ◽  
Geometric Parameters

Optimization of hanger arrangement in pedestrian tied arch bridge with sparse hanger system

2019 ◽  
Vol 22 (12) ◽  
pp. 2594-2604 ◽  
Author(s):  
Yonggang Tan ◽  
Yuanbin Yao
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Numerical Analysis ◽  
Mechanical Behavior ◽  
Live Load ◽  
Optimization Approach ◽  
Dense Network ◽  
Arch Bridge ◽  
Arch Bridges ◽  
Tied Arch Bridge

The hanger arrangement has a decisive influence on the mechanical behavior of the tied arch bridge with network hanger system. Many investigations on highway or railway tied arch bridges show that the arch bridges with dense network hangers are superior to those with vertical hangers under larger live load. However, numerous dense inclined hangers lower the esthetic effect of the bridge, especially for pedestrian tied arch bridges. Consequently, the sparse inclined hanger system is recommended in the design of pedestrian tied arch bridges. However, the amount of possible schemes of the hanger arrangement grows rapidly with the number of hangers increasing beyond 10, rendering great difficulties in searching for proper schemes. In this article, a dimensionless optimization approach based on genetic algorithm is proposed in searching for hanger arrangement schemes. Numerical analysis indicates that the proposed method is effective in the optimization of pedestrian tied arch bridge with sparse inclined hanger system, and some of the feasible hanger arrangement solutions show more excellent mechanical properties.

Conceptual Hull Design Using a Genetic Algorithm

2003 ◽  
Vol 47 (03) ◽  
pp. 222-236
Author(s):  
T. W. Lowe ◽  
J. Steel
Keyword(s):  
Genetic Algorithm ◽  
Design Tool ◽  
Geometric Parameters ◽  
Design Variables ◽  
Distinct Cluster ◽  
Wide Range ◽  
Hull Design ◽  
Variable Space ◽  
Hull Forms ◽  
Differential Equation Method

A genetic algorithm is used to search the design variable space of a model ship hull for forms having specified values of various primary and secondary geometric parameters. A representative of each distinct cluster of the forms found is identified and presented as a candidate hull design having the required geometric characteristics. This could form the basis of a prototype conceptual design tool enabling the generation of hull forms satisfying specified requirements. The geometric parameters considered include the displacement, waterline length, waterline beam, and waterplane coefficient together with the locations of the centers of flotation and buoyancy. The hull surface is represented using the partial differential equation method, which permits a wide range of fair hull forms to be accessible using a relatively small number of design variables.

Optimization of First Mode Sensitivity of V-Shaped AFM Cantilever Using Genetic Algorithm Method

2009 ◽  
Author(s):  
S. A. Moeini ◽  
M. H. Kahrobaiyan ◽  
M. Rahaeifard ◽  
M. T. Ahmadian
Keyword(s):  
Genetic Algorithm ◽  
Contact Stiffness ◽  
Sample Surface ◽  
Geometric Parameters ◽  
Design Parameters ◽  
Normal Contact ◽  
Genetic Algorithm Method ◽  
Normal Contact Stiffness ◽  
Afm Cantilever ◽  
Micro Cantilever

Atomic force microscopes (AFM) are widely used for feature detection and scanning surface topography of different materials. Contrast of topography images is significantly influenced by the sensitivity of AFM micro cantilever which means enhancement of sensitivity leads to increase of topography images resolution So, in the last years numerous scientists interested in studying the effects of different parameters such as geometric one on the sensitivity of AFM micro cantilevers. V-shape micro cantilever types of AFMs probe are widely used to scan various types of surfaces. In V-shape micro cantilevers, there are many geometric and design parameters which influence the flexural sensitivity of the micro beam, noticeably. In this paper evaluation of optimum geometric parameters and optimum cantilever slope is considered as a significant purpose in order to obtain maximum flexural sensitivity by using genetic algorithm optimization method. In the calculations, the normal and lateral interaction forces between AFM tip and sample surface is considered and modeled by linear springs which represent the contact stiffness of the sample surface. Also, a relation for flexural sensitivity of AFM cantilever as a function of geometric parameters and cantilever slope is derived which is used in optimization step by employing a genetic algorithm program. Using genetic algorithm method, the optimum geometric parameters and cantilever slope are calculated which maximize the flexural sensitivity of the first mode of a V-shape cantilever for various values of normal contact stiffness. These optimum parameters versus normal contact stiffness are presented in some result figures. The results show that for any contact stiffness, there are a cantilever slope and a set of geometrical parameters which provide the maximum sensitivity for AFM probe. Adopting these parameters for the design of V-shape micro cantilever according to the sample contact stiffness, maximum flexural sensitivity can be obtained, so that high contrast images are reachable.

scholarly journals Exploring Filling Law of Small Fillet of Dual Clutch Hub

2018 ◽  
Vol 36 (3) ◽  
pp. 464-470
Author(s):  
Yilei Zhang ◽  
Zhili Hu ◽  
Jin Li ◽  
Mingliang Dai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Frictional Resistance ◽  
Mechanical Analysis ◽  
Geometric Parameters ◽  
Experimental Result ◽  
Forming Quality ◽  
Concave Shape ◽  
Multi Objective Genetic Algorithm

In extruding the dual clutch hub with small fillet gear, the filling of small fillet gear is not complete due to the large forming resistance. A method of preforming the blank into a concave shape is proposed. The mechanical analysis and finite element simulation are used to analyze the principle that the blank of concave shape can promote the filling of small fillet gear, stamping and extruding are both used to form the dual clutch hub and the preform is used to simulate the fillet gear’s extruding to analyze the influence of the geometric parameters of the preform on the forming quality. The mapping relationship between the geometric parameters of the preform and the size of unfilled fillet gear is established by using the BP neural network. The multi-objective genetic algorithm is used to optimize the geometric parameters of the preform. From the results we can see that the frictional resistance decreases due to reduced contact area between the blank and the mold when the section shape of the blank is concave, and at the same time, the tangential thrust is generated on the blank, so the filling of small fillet gear is better; BP neural network combined with genetic algorithm can reliably optimize the geometric parameters of the preform. The filling performance of the fillet gear is better under the optimal preform, and the forming quality of the dual clutch hub is improved. The experimental result verified the feasibility of the method and the accuracy of the simulation.

Application of Radial Basis Function Networks Combined with Genetic Algorithm in Predicting the Geometric Parameters of Drawbead

2011 ◽  
Vol 101-102 ◽  
pp. 790-794
Author(s):  
Jie Lai Chen ◽  
Da Zhi Jiang ◽  
Ya Yan Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Geometric Parameters ◽  
The Neural Network ◽  
Fitting Method ◽  
Radial Basis ◽  
Curve Fitting Method ◽  
Restraining Force

The drawbead plays a very important role in automobile covering part forming processes. Traditional drawbead design mainly depends on designers’ experience. In order to obtain proper restraining force during die try-out, it is often necessary to adjust the drawbead through a very complicated procedure. It is thus meaningful to study the relationship between the parameters used to reflect metal forming effects and the geometric parameters of drawbead and then create a prediction model for them. This paper employs the radial basis function neural network technology to predict the geometric parameters of drawbead used in forming processes, where the genetic algorithm is used to optimize the neural network structure. Simulation results show that the proposed approach outperforms the curve fitting method.

scholarly journals Rotor Cascade Shape Optimization with Unsteady Passing Wakes Using Implicit Dual-Time Stepping and a Genetic Algorithm

2003 ◽  
Vol 9 (5) ◽  
pp. 353-361 ◽  
Author(s):  
Eun Seok Lee ◽  
George S. Dulikravich ◽  
Brian H. Dennis
Keyword(s):  
Genetic Algorithm ◽  
Unsteady Flow ◽  
Shape Optimization ◽  
Objective Function ◽  
Stokes Equations ◽  
Leading Edge ◽  
Geometric Parameters ◽  
Time Stepping ◽  
Acceleration Techniques ◽  
Dual Time Stepping

An axial turbine rotor cascade-shape optimization with unsteady passing wakes was performed to obtain an improved aerodynamic performance using an unsteady flow, Reynolds-averaged Navier-Stokes equations solver that was based on explicit, finite difference; Runge-Kutta multistage time marching; and the diagonalized alternating direction implicit scheme. The code utilized Baldwin-Lomax algebraic andk-εturbulence modeling. The full approximation storage multigrid method and preconditioning were implemented as iterative convergence-acceleration techniques. An implicit dual-time stepping method was incorporated in order to simulate the unsteady flow fields. The objective function was defined as minimization of total pressure loss and maximization of lift, while the mass flow rate was fixed during the optimization. The design variables were several geometric parameters characterizing airfoil leading edge, camber, stagger angle, and inter-row spacing. The genetic algorithm was used as an optimizer, and the penalty method was introduced for combining the constraints with the objective function. Each individual's objective function was computed simultaneously by using a 32-processor distributedmemory computer. The optimization results indicated that only minor improvements are possible in unsteady rotor/stator aerodynamics by varying these geometric parameters.

Sign in / Sign up

Export Citation Format

Share Document