scholarly journals Optimal Constrained Layer Damping of Beams: Experimental and Numerical Studies

1995 ◽  
Vol 2 (6) ◽  
pp. 445-450 ◽  
Author(s):  
J.-L. Marcelin ◽  
S. Shakhesi ◽  
F. Pourroy
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Problem ◽  
Damping Factor ◽  
Constrained Layer Damping ◽  
Numerical Studies ◽  
Design Variables ◽  
Optimal Damping ◽  
Viscoelastic Layers

This article deals with the optimal damping of beams constrained by viscoelastic layers when only one or several portions of the beam are covered. The design variables are the dimensions and locations of the viscoelastic layers and the objective function is the maximum damping factor. The discrete design variable optimization problem is solved using a genetic algorithm. Numerical results for minimum and maximum damping are compared to experimental results. This is done for a various number of materials and beams.

Multiobjective Genetic Algorithm Approach to the Optimization of a Rotating Flexible Arm With ACLD Patch

Aerospace ◽  
2004 ◽  
Author(s):  
Eric H. K. Fung ◽  
L. C. Hau ◽  
David T. W. Yau
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Viscoelastic Layer ◽  
Control Voltage ◽  
Constrained Layer Damping ◽  
Integrated Optimization ◽  
Flexible Arm ◽  
Design Variables ◽  
Centrifugal Stiffening ◽  
Viscoelastic Layers

This paper describes the use of multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem of a rotating flexible arm with Active Constrained Layer Damping (ACLD) treatment. The arm is rotating in a horizontal plane with triangular velocity profiles. The ACLD patch is placed at the clamped end of the arm. The design objectives are to minimize the total treatment weight, the control voltage and the tip displacement of the arm, as well as to maximize the passive damping characteristic of the arm. Design variables include the control gains, the maximum angular velocity, the shear modulus of the viscoelastic layer, the thickness of the piezoelectric constraining and viscoelastic layers, and the length of the ACLD patch. The finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the ACLD flexible arm to predict its dynamic behavior, in which the effects of centrifugal stiffening due to the rotation are taken into account. Reasonable Pareto solutions are successfully obtained. It is shown that MOGA is applicable to the present integrated optimization problem.

scholarly journals Optimal Location of Plate Damped Parts by Use of a Genetic Algorithm

1994 ◽  
Vol 1 (6) ◽  
pp. 541-547 ◽  
Author(s):  
J.-L. Marcelin ◽  
Ph. Trompette
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Design Variable ◽  
Optimization Problem ◽  
Optimal Location ◽  
Element Analysis ◽  
Modal Damping ◽  
Design Variables ◽  
Optimal Damping

Optimal damping of plates (or beams) partially covered by viscoelastic constrained layers is presented. The design variables are the locations and the sizes of the damped parts. The objective function is a linear combination of the first modal damping factors calculated from a specific finite element analysis. The discrete design variable optimization problem is solved using a genetic algorithm.

Shape Optimization of Hydraulic Structures: an Example of an Optimum Design of a Fish Passage

10.29007/2k64 ◽  
2018 ◽  
Author(s):  
Pat Prodanovic ◽  
Cedric Goeury ◽  
Fabrice Zaoui ◽  
Riadh Ata ◽  
Jacques Fontaine ◽  
...  
Keyword(s):  
Numerical Model ◽  
Shape Optimization ◽  
Objective Function ◽  
Optimum Design ◽  
Optimization Problem ◽  
A Priori ◽  
Coupled System ◽  
Fish Passage ◽  
Hydraulic Structures ◽  
Design Variables

This paper presents a practical methodology developed for shape optimization studies of hydraulic structures using environmental numerical modelling codes. The methodology starts by defining the optimization problem and identifying relevant problem constraints. Design variables in shape optimization studies are configuration of structures (such as length or spacing of groins, orientation and layout of breakwaters, etc.) whose optimal orientation is not known a priori. The optimization problem is solved numerically by coupling an optimization algorithm to a numerical model. The coupled system is able to define, test and evaluate a multitude of new shapes, which are internally generated and then simulated using a numerical model. The developed methodology is tested using an example of an optimum design of a fish passage, where the design variables are the length and the position of slots. In this paper an objective function is defined where a target is specified and the numerical optimizer is asked to retrieve the target solution. Such a definition of the objective function is used to validate the developed tool chain. This work uses the numerical model TELEMAC- 2Dfrom the TELEMAC-MASCARET suite of numerical solvers for the solution of shallow water equations, coupled with various numerical optimization algorithms available in the literature.

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

Back Analysis of Material Parameters of Concrete Dam Using Genetic Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1955-1958
Author(s):  
Jin Bao Liu ◽  
Shou Ju Li ◽  
Wei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Optimization Problem ◽  
Back Analysis ◽  
Water Level Change ◽  
Concrete Dam ◽  
Material Parameters ◽  
Level Change ◽  
Inversion Procedure ◽  
The Difference

The inverse problem of parameter identification is deal with by minimizing an objective function that contains the difference between observed and calculated dam displacements. The optimization problem of minimizing objective function is solved with genetic algorithm. The calculated dam displacements are simulated by using finite element method according to water level change acting on dam upstream. The practical dam displacements are observed on the dam crest. The investigation shows that the forecasted dam displacements agree well with observed ones. The effectiveness of proposed inversion procedure is validated.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

scholarly journals Transmissivity Identification by Combination of CVFEM and Genetic Algorithm: Application to the Coastal Aquifer

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Hassan Smaoui ◽  
Abdelkabir Maqsoud ◽  
Sami Kaidi
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Objective Function ◽  
Direct Problem ◽  
Optimization Problem ◽  
Control Volume ◽  
High Accuracy ◽  
Global Optimum ◽  
Control Volume Finite Element

The solution of inverse problems in groundwater flow has been massively invested by several researchers around the world. This type of problem has been formulated by a constrained optimization problem and this constraint is none other than the direct problem (DP) itself. Thus, solving algorithms are developed that simultaneously solve the direct problem (Darcy’s equation) and the associated optimization problem. Several papers have been published in the literature using optimization methods based on computation of the objective function gradients. This type of method suffers from the inability to provide a global optimum. Similarly, they also have the disadvantage of not being applicable to objective functions of discontinuous derivatives. This paper is proposed to avoid these disadvantages. Indeed, for the optimization phase, we use random search-based methods that do not use derivative computations, but based on a search step followed only by evaluation of the objective function as many times as necessary to the convergence towards the global optimum. Among the different algorithms of this type of methods, we adopted the genetic algorithm (GA). On the other hand, the numerical solution of the direct problem is accomplished by the CVFEM discretization method (Control Volume Finite Element Method) which ensures the mass conservation in a natural way by its mathematical formulation. The resulting computation code HySubF-CVFEM (Hydrodynamic of Subsurface Flow by Control Volume Finite Element Method) solves the Darcy equation in a heterogeneous porous medium. Thus, this paper describes the description of the integrated optimization algorithm called HySubF-CVFEM/GA that has been successfully implemented and validated successfully compared to a schematic flow case offering analytical solutions. The results of this comparison are qualified of excellent accuracy. To identify the transmissivity field of the realistic study area, the code HySubF-CVFEM/GA was applied to the coastal “Chaouia” groundwater located in Western of Morocco. This aquifer of high heterogeneity is essential for water resources for the Casablanca region. Results analysis of this study has shown that the developed code is capable of providing high accuracy transmissivity fields, thus representing the heterogeneity observed in situ. However, in comparison with gradient method optimization the HySubF-CVFEM/GA code converges too slowly to the optimal solution (large CPU-time consuming). Despite this disadvantage, and given the high accuracy of the obtained results, the HySubF-CVFEM/GA code can be recommended to solve in an efficient and effective manner the identification parameters problems in hydrogeology.

Optimization Design of the Crane Girder Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 123-126
Author(s):  
Peng Fei Wang ◽  
Xiu Hui Diao
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Design ◽  
Optimal Solution ◽  
Population Diversity ◽  
Main Beam ◽  
Gantry Crane ◽  
Adaptive Genetic Algorithm ◽  
Accelerate Convergence

With taking weight of single main beam of gantry crane as objective function, and taking main beam upper & lower cored, diagonal & horizontal bracing, and width & weight as design variable, this essay adopted population diversity adaptive genetic algorithm to optimize its structure and improved program design through MATLAB. This algorithm could accelerate convergence speed, which make much it easier to realize comprehensive optimal solution, since it effectively avoided weakness of basic genetic algorithm, such as partial optimal solution, prematurity and being lack of continuity, etc.

Optimum design of rolling element bearings using a genetic algorithm—differential evolution (GA—DE) hybrid algorithm

2010 ◽  
Vol 225 (3) ◽  
pp. 714-721 ◽  
Author(s):  
W Y Lin
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimum Design ◽  
Dynamic Loads ◽  
Representative Case ◽  
De Algorithm ◽  
Design Variables ◽  
Deep Groove ◽  
Rolling Element

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA—DE algorithm can successfully find the better dynamic loads rating, about 1.3—11.1 per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA—DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA—DE algorithm to be much more effective and efficient than the BGA.

scholarly journals Design of a Parallel Robotic Manipulator Using Evolutionary Computing

10.5772/50922 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 27 ◽  
Author(s):  
António M. Lopes ◽  
E.J. Solteiro Pires ◽  
Manuel R. Barbosa
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Condition Number ◽  
Optimization Problem ◽  
Robotic Manipulator ◽  
Inverse Kinematic ◽  
Search Process ◽  
Algorithm Optimization ◽  
Structural Configuration ◽  
Optimization Search

In this paper the kinematic design of a 6-dof parallel robotic manipulator is analysed. Firstly, the condition number of the inverse kinematic jacobian is considered as the objective function, measuring the manipulator's dexterity and a genetic algorithm is used to solve the optimization problem. In a second approach, a neural network model of the analytical objective function is developed and subsequently used as the objective function in the genetic algorithm optimization search process. It is shown that the neuro-genetic algorithm can find close to optimal solutions for maximum dexterity, significantly reducing the computational burden. The sensitivity of the condition number in the robot's workspace is analysed and used to guide the designer in choosing the best structural configuration. Finally, a global optimization problem is also addressed.

Sign in / Sign up

Export Citation Format

Share Document