Optimal design of flywheels using an injection island genetic algorithm

1999 ◽  
Vol 13 (5) ◽  
pp. 327-340 ◽  
Author(s):  
DAVID EBY ◽  
R.C. AVERILL ◽  
WILLIAM F. PUNCH ◽  
ERIK D. GOODMAN
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
High Speed ◽  
Design Space ◽  
Fitness Function ◽  
Unit Weight ◽  
Computational Time ◽  
Finite Element Code ◽  
Symmetric Plane

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.

Structural Design Optimization of Side/Rear Impact Guards Using a Coupled Genetic Algorithm Finite Element Method

2004 ◽  
Author(s):  
De-Shin Liu ◽  
Nan-Chun Lin ◽  
Chao-Chin Huang ◽  
Yin-Lee Meng
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Computational Time ◽  
Design Parameters ◽  
Fe Model ◽  
Passenger Cars ◽  
Rear Impact ◽  
Structural Design Optimization ◽  
Energy Absorbing

Underride protective structure can reduce serious injures when passenger cars collide with the rear end or side of the heavy vehicle. This paper describes the use of Genetic Algorithm (GA) coupled with a dynamic, inelastic and large deformation finite element (FE) code LS-DYNA to search optimal design of the Side/Rear impact guards. In order to verify the accuracy of the FE model, the simulation results were compared with real experiments follow with the regulation ECE R73. The validated FE model then used to study the optimal design base on under running distance and total amount of energy absorbing capacity. The results from this study shown that this newly developed method not only can found multi-objective design parameters but also can reduce computational time significantly.

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

2012 ◽  
Vol 504-506 ◽  
pp. 637-642 ◽  
Author(s):  
Hamdi Aguir ◽  
J.L. Alves ◽  
M.C. Oliveira ◽  
L.F. Menezes ◽  
Hedi BelHadjSalah
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Deep Drawing ◽  
Inverse Analysis ◽  
Computational Time ◽  
Ann Model ◽  
Drawing Test ◽  
Artificial Neural ◽  
Deep Drawing Test ◽  
Cylindrical Cup

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

Optimization Methodology for Composite Stiffened Panel Based on Genetic Algorithm

2014 ◽  
Vol 952 ◽  
pp. 34-37
Author(s):  
Da Feng Jin ◽  
Zhe Liu ◽  
Zhi Rui Fan
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Design Space ◽  
Design Guidelines ◽  
Stiffened Panel ◽  
Stacking Sequence ◽  
Design Variables ◽  
Composite Stiffened Panel ◽  
Optimization Methodology

A novel optimization methodology for stiffened panel is proposed in this paper. The purpose of the optimization methodology is to improve the first buckling load of the panel which is obtained by finite element method. The stacking sequence of the stiffeners is taken as design variables. In order to ensure the manufacturability of design, the design guidelines of stacking sequence are taken into account. A DOE based on Halton Sequence makes the initial points of genetic algorithm spread more evenly in the design space of laminate parameters and consequently accelerates the search to convergence. The numerical example verifies the efficiency of this method.

Finite Element Analysis of the Effects of Coated Tool on High Speed Orthogonal Machining

2008 ◽  
Vol 392-394 ◽  
pp. 879-883
Author(s):  
Hui Xia Liu ◽  
H. Yan ◽  
Xiao Wang ◽  
Shu Bin Lu ◽  
K. Yang ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
High Speed ◽  
Finite Element Models ◽  
Finite Element Code ◽  
Element Analysis ◽  
Orthogonal Machining ◽  
Coated Tool ◽  
Deform 2D ◽  
Updated Lagrangian

Two 3-D finite element models of coated tool and uncoated tool were established using the finite element code DEFORM-2D based on the updated Lagrangian formula. And their machinability on high speed orthogonal machining was simulated and compared. The investigation results indicate that the coated tool has higher surface temperature and lower inside temperature compared with the uncoated tool. Moreover, the cutting forces of the model using coated tool are lower than that using uncoated tool.

Optimal Design of 6-UPU Parallel Mechanism

2011 ◽  
Vol 480-481 ◽  
pp. 1055-1060
Author(s):  
Guang Hua Wu ◽  
Lie Hang Gong ◽  
Xin Wei Ji ◽  
Zhong Jun Wu ◽  
Yong Jun Gai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Design ◽  
Objective Function ◽  
Parallel Mechanism ◽  
Design Space ◽  
Jacobian Matrix ◽  
Optimal Model ◽  
The Real ◽  
Universal Joints

The methodology of the optimal design for the 6-UPU parallel mechanism (PM) is presented based on genetic algorithms. The optimal index which expressed by Jacobian matrix of the PM is first deduced. An optimal model is established, in which the kinematic dexterity of a parallel mechanism is considered as the objective function. The design space, the limiting length of the electric actuators and the limit angles of universal joints are taken as constraints. The real-encoding genetic algorithm is applied to the optimal design of a parallel mechanism, which is proved the validity and advantage for the optimal design of a similar mechanism.

Multi-point optimization of transonic airfoils using an enhanced genetic algorithm

2017 ◽  
Vol 232 (7) ◽  
pp. 1347-1360 ◽  
Author(s):  
Narjes Timnak ◽  
Alireza Jahangirian
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fitness Function ◽  
Single Point ◽  
Navier Stokes ◽  
Computational Time ◽  
Adaptive Mutation ◽  
Function Evaluation ◽  
New Techniques ◽  
New Methods

In this study, two new techniques are proposed for accelerating the multi-point optimization of an airfoil shape by genetic algorithms. In such multi-point evolutionary optimization, the objective function has to be evaluated several times more than a single-point optimization. Thus, excessive computational time is crucial in these problems particularly, when computational fluid dynamics is used for fitness function evaluation. Two new techniques of preadaptive range operator and adaptive mutation rate are proposed. An unstructured grid Navier–Stokes flow solver with a two-equation [Formula: see text] turbulence model is used to evaluate the objective function. The new methods are applied for optimum design of a transonic airfoil at two speed conditions. The results show that using the new methods can increase the aerodynamic efficiency of optimum airfoil at each operating condition with about 30% less computational time in comparison with the conventional genetic algorithm approach.

scholarly journals EXPERT SYSTEM TO CREATE BUILDING DESIGN SCHEME / EKSPERTINĖ SISTEMA STATINIO KONSTRUKCINEI SCHEMAI KURTI

2013 ◽  
Vol 5 (6) ◽  
pp. 601-604
Author(s):  
Žilvinas Steckevičius ◽  
Darius Mačiūnas ◽  
Elena Glėbienė ◽  
Renata Birbalaitė
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Expert System ◽  
Optimal Design ◽  
Finite Difference ◽  
Building Design ◽  
Material Strength ◽  
Design Elements ◽  
Design Scheme ◽  
Linear Load

In this paper the technology for creation of optimal design scheme for building is presented. Optimization of design scheme is based on genetic algorithm. Rectangular perimeter of single-storey structure with a linear load in all its locations is investigated. In such case, finite element, finite difference and other methods are not necessary – in order to evaluate the stress of design elements it is sufficient to use formulas of material strength. Santrauka Šiame darbe apžvelgta optimalios konstrukcinės schemoskūrimo technologija, naudojant genetinį optimizavimo algoritmą.Nagrinėjama stačiakampio perimetro vieno aukšto konstrukcijasu tolygiąja apkrova visose jos vietose. Šiuo atveju nebūtina naudotibaigtinių elementų, baigtinių skirtumų ar kitų metodų. Norintįvertinti konstrukcijos elementų įtempius, užtenka medžiagųatsparumo formulių.

scholarly journals Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 171
Author(s):  
Nihad Dib ◽  
Umar Al-Sammarraie
Keyword(s):  
Optimal Design ◽  
High Speed ◽  
Search Algorithm ◽  
Fitness Function ◽  
Logic Gate ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cmos Inverter ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

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