Design Optimization of Magnetic Coupling Using Genetic Algorithm and 2D FEM Model

2004 ◽  
Author(s):  
Petr Krejci ◽  
Cestmir Ondrusek
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Permanent Magnets ◽  
Magnetic Coupling ◽  
Optimization Procedure ◽  
Magnetic Characteristics ◽  
Fe Model ◽  
Fem Model ◽  
Additional Advantage ◽  
Torque Transmission

Magnetic couplings (Figure 1) are widely used to torque transmission between two shafts without any mechanical contact. They are especially well suited for used in hazardous environments, to transmit torque through a separation wall. An additional advantage of a magnetic coupling is that slipping occurs when excessive torque is applied, this can be used to prevent mechanical failure due to torque overloads. This paper deals with influence of temperature on behavior of magnetic coupling and magnetic coupling design optimization. The permanent magnets that are used for torque transmission cannot be used close to Currie point, which is a point of loss of magnetic characteristics. We intend to use the magnetic coupling for pump of radioactive liquid materials for transmutation devices, where the temperature is close to four hundred centigrade. Because of we suggest the design changes for elimination of temperature influence. This paper presents the finite element (FE) parametric model of magnetic coupling, experimental verification of FE model and optimization of the inner part of magnetic coupling in order to increase the maximal torque. The genetic algorithm method in connection with FEM model of magnetic coupling was used for the design optimization procedure.

scholarly journals Design optimization of an axial-field eddy-current magnetic coupling based on magneto-thermal analytical model

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Julien Fontchastagner ◽  
Thierry Lubin ◽  
Smaïl Mezani ◽  
Noureddine Takorabet
Keyword(s):  
Design Optimization ◽  
Analytical Model ◽  
Eddy Current ◽  
Convection Heat Transfer ◽  
Design Procedure ◽  
Magnetic Coupling ◽  
Optimization Procedure ◽  
Computationally Efficient ◽  
Transfer Coefficients ◽  
Good Efficiency

Abstract This paper presents a design optimization of an axial-flux eddy-current magnetic coupling. The design procedure is based on a torque formula derived from a 3D analytical model and a population algorithm method. The main objective of this paper is to determine the best design in terms of magnets volume in order to transmit a torque between two movers, while ensuring a low slip speed and a good efficiency. The torque formula is very accurate and computationally efficient, and is valid for any slip speed values. Nevertheless, in order to solve more realistic problems, and then, take into account the thermal effects on the torque value, a thermal model based on convection heat transfer coefficients is also established and used in the design optimization procedure. Results show the effectiveness of the proposed methodology.

scholarly journals Fast and Accurate 2D Analytical Subdomain Method for Coaxial Magnetic Coupling Analysis

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4656
Author(s):  
Yusuf Akcay ◽  
Paolo Giangrande ◽  
Oliver Tweedy ◽  
Michael Galea
Keyword(s):  
Permanent Magnets ◽  
Magnetic Coupling ◽  
Analytical Tool ◽  
Sensitivity Analyses ◽  
Design Parameters ◽  
The Finite Element Method ◽  
Fast Analysis ◽  
Torque Transmission ◽  
Magnetic Couplings ◽  
The Impact

Magnetic couplings (MCs) enable contactless speed/torque transmission via interactions between the magnetic fields of permanent magnets (PMs) rather than a physical mechanical connection. The contactless transmission of mechanical power leads to improvements in terms of efficiency and reliability due to the absence of wear between moving parts. One of the most common MC topologies is the coaxial type, also known as the radial configuration. This paper presents an analytical tool for the accurate and fast analysis of coaxial magnetic couplings (CMCs) using a two-dimensional subdomain approach. In particular, the proposed analytical tool resolves Laplace’s and Poisson’s equations for both air-gap and PM regions. The tool can be used to evaluate the impact of several design parameters on the performance of the CMC, enabling quick and accurate sensitivity analyses, which in turn guide the choice of design parameters. After discussing the building procedure of the analytical tool, its applicability and suitability for sensitivity analyses are assessed and proven with the analysis of a fully parameterized CMC geometry. The accuracy and the computational burden of the proposed analytical tool are compared against those of the finite element method (FEM), revealing faster solving times and acceptable levels of precision.

Shape Optimization of Multi-Element Airfoils Using Evolutionary Algorithms and Hybrid Unstructured Framework

2004 ◽  
Author(s):  
V. Ahuja ◽  
A. Hosangadi ◽  
Y. T. Lee
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Shape Optimization ◽  
Design Optimization ◽  
Optimization Procedure ◽  
Complex Design ◽  
Local Extrema ◽  
Present Design ◽  
Set Up ◽  
Comprehensive Framework

In this paper we present design optimization studies of multi-element airfoils utilizing evolutionary algorithms. The shape optimization process is carried out by utilization of high fidelity CFD based comprehensive framework. The framework comprises of a genetic algorithm based design optimization procedure coupled to the hybrid unstructured CRUNCH CFD® code and a grid generator. The genetic algorithm based optimization procedure is very robust, and searches the complex design landscape in an efficient and parallel manner. Furthermore, it can easily handle complexities in constraints and objectives and is disinclined to get trapped in local extrema regions. The fitness evaluations are carried out through a RANS based hybrid unstructured solver. The utilization of hybrid unstructured methodology provides flexibility in incorporating large changes in design and mesh regeneration is carried out in an automated manner through a scripting process within the grid generator GRIDGEN. The design optimization procedure is carried out simultaneously on both the stabilizer and the flap. Shape changes to the trailing edge of the flap strongly influence the secondary flow patterns that set up in the gap region between the stabilizer and the flap. These, in turn, are found to have a profound influence on lift and torque characteristics. The paper will discuss these results and provide details of the optimization procedure including coupling with hybrid unstructured framework and grid generator.

A Novel Design Optimization Method for Obtaining Desired Deformation Behavior in Additively Manufactured Multi-Material Parts

2014 ◽  
Author(s):  
Ranjit Gopi ◽  
Sonjoy Das ◽  
Rahul Rai
Keyword(s):  
Design Optimization ◽  
Cross Sections ◽  
Deformation Behavior ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Full Scale ◽  
Scale Model ◽  
Fe Model ◽  
Computationally Efficient ◽  
Novel Design

This paper introduces a novel design optimization method to optimize models with multiple material layers and complex cross-sections, for desired behavior. The developed optimization procedure utilizes an upscaling approach to approximate a full scale finite element (FE) model, by only analyzing a small material volume element. This approach requires less modeling efforts and is computationally less expensive than the full scale model. The developed method helps in building computationally efficient models for obtaining desired deformation behavior. The efficacy of the proposed method is illustrated through a couple of example design problems.

scholarly journals Ship Pipe Routing Design Using NSGA-II and Coevolutionary Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Wentie Niu ◽  
Haiteng Sui ◽  
Yaxiao Niu ◽  
Kunhai Cai ◽  
Weiguo Gao
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Branch Pipe ◽  
Optimization Procedure ◽  
Piping System ◽  
Planning Problem ◽  
Nsga Ii ◽  
Pipe Routing ◽  
Maze Algorithm ◽  
Nondominated Sorting

Pipe route design plays a prominent role in ship design. Due to the complex configuration in layout space with numerous pipelines, diverse design constraints, and obstacles, it is a complicated and time-consuming process to obtain the optimal route of ship pipes. In this article, an optimized design method for branch pipe routing is proposed to improve design efficiency and to reduce human errors. By simplifying equipment and ship hull models and dividing workspace into three-dimensional grid cells, the mathematic model of layout space is constructed. Based on the proposed concept of pipe grading method, the optimization model of pipe routing is established. Then an optimization procedure is presented to deal with pipe route planning problem by combining maze algorithm (MA), nondominated sorting genetic algorithm II (NSGA-II), and cooperative coevolutionary nondominated sorting genetic algorithm II (CCNSGA-II). To improve the performance in genetic algorithm procedure, a fixed-length encoding method is presented based on improved maze algorithm and adaptive region strategy. Fuzzy set theory is employed to extract the best compromise pipeline from Pareto optimal solutions. Simulation test of branch pipe and design optimization of a fuel piping system were carried out to illustrate the design optimization procedure in detail and to verify the feasibility and effectiveness of the proposed methodology.

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 Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

Sign in / Sign up

Export Citation Format

Share Document