IGLO algorithm-based large-scale structural optimization with mixed-discrete design variables

1999 ◽  
Author(s):  
Rong Shieh
Keyword(s):  
Structural Optimization ◽  
Large Scale ◽  
Design Variables ◽  
Discrete Design Variables

scholarly journals Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

2021 ◽  
Vol 11 (7) ◽  
pp. 3270
Author(s):  
Sadik Ozgur Degertekin ◽  
Mohammad Minooei ◽  
Lorenzo Santoro ◽  
Bartolomeo Trentadue ◽  
Luciano Lamberti
Keyword(s):  
Structural Optimization ◽  
Large Scale ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Nonlinear Constraints ◽  
Weight Minimization ◽  
Design Variables ◽  
Gradient Based ◽  
Nodal Displacements

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

Comparative Structural Optimization Study of Composite and Aluminum Horizontal Tail Plane of a Helicopter

2019 ◽  
Author(s):  
Bertan Arpacioglu ◽  
Altan Kayran
Keyword(s):  
Structural Optimization ◽  
Minimum Weight ◽  
Local Buckling ◽  
Laminated Composite ◽  
Element Model ◽  
Aerodynamic Load ◽  
Preliminary Design ◽  
Design Phase ◽  
Design Variables ◽  
Discrete Design Variables

Abstract This work presents structural optimization studies of aluminum and composite material horizontal tail plane of a helicopter by using MSC. NASTRAN SOL200 optimization capabilities. Structural design process starts from conceptual design phase, and structural layout design is performed by using CATIA. In the preliminary design phase, study focuses on the minimum weight optimization with multiple design variables and similar constraints for both materials. Aerodynamic load calculation is performed using ANSYS and the finite element model of the horizontal tail plane is created by using MSC.PATRAN. According to the characteristics of materials, design variables are chosen. For the aluminum horizontal tail, thickness and flange areas are used as the design variables; and for composite horizontal tail, attention is mainly focused on the ply numbers and ply orientation angles of the laminated composite panels. By considering the manufacturability issues, discrete design variables are used. For three different mesh densities, different initial values of the design variables, and similar design constraints, optimizations are repeated and the results of optimizations are examined and compared with each other. In the optimizations performed, constraints are taken as strength and local buckling constraints. It is shown that the optimization methodology used in this study gives confident results for optimizing structures in the preliminary design phase.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

Large scale structural optimization of trimetallic Cu–Au–Pt clusters up to 147 atoms

2017 ◽  
Vol 686 ◽  
pp. 103-110 ◽  
Author(s):  
Genhua Wu ◽  
Yan Sun ◽  
Xia Wu ◽  
Run Chen ◽  
Yan Wang
Keyword(s):  
Structural Optimization ◽  
Large Scale

Evaluation of Fuel Preparation Systems for Lean Premixing-Prevaporizing Combustors

1986 ◽  
Vol 108 (2) ◽  
pp. 391-395
Author(s):  
W. J. Dodds ◽  
E. E. Ekstedt
Keyword(s):  
System Design ◽  
Large Scale ◽  
Fuel Injection ◽  
Operating Conditions ◽  
Turbine Engine ◽  
Fuel Injectors ◽  
Design Concepts ◽  
Mixture Preparation ◽  
Design Variables ◽  
System Length

A series of tests was conducted to provide data for the design of premixing-prevaporizing fuel-air mixture preparation systems for aircraft gas turbine engine combustors. Fifteen configurations of four different fuel-air mixture preparation system design concepts were evaluated to determine fuel-air mixture uniformity at the system exit over a range of conditions representative of cruise operation for a modern commercial turbofan engine. Operating conditions, including pressure, temperature, fuel-air ratio, and velocity had no clear effect on mixture uniformity in systems which used low-pressure fuel injectors. However, performance of systems using pressure atomizing fuel nozzles and large-scale mixing devices was shown to be sensitive to operating conditions. Variations in system design variables were also evaluated and correlated. Mixture uniformity improved with increased system length, pressure drop, and number of fuel injection points per unit area. A premixing system compatible with the combustor envelope of a typical combustion system and capable of providing mixture nonuniformity (standard deviation/mean) below 15% over a typical range of cruise operating conditions was demonstrated.

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