scholarly journals Improved Immune Genetic Algorithm to Optimize the Design of Wing Structure of Competition Aircraft

2021 ◽  
Vol 2137 (1) ◽  
pp. 012075
Author(s):  
Xi Feng ◽  
Yafeng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Aircraft Design ◽  
Flight Test ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Immune Genetic Algorithm ◽  
Maximum Deformation ◽  
Design Variables ◽  
Wing Structure

Abstract An improved immune genetic algorithm is used to design and optimize the wing structure parameters of a competition aircraft. According to the requirements of aircraft design, multi-objective optimization index is established. On this basis, the basic steps of using immune algorithm to optimize the main design parameters of aircraft wing structure are proposed, and the optimization of the wing parameters of a competition aircraft is used as an example for simulation calculation. The design variables in the optimization are the size of the wing components, and the optimization goal is to minimize the weight of the wing and the maximum deformation of the wing structure. Research shows that compared with traditional optimization methods; the improved immune genetic algorithm is a very effective optimization method. At the same time, a prototype is made to check the validity and feasibility of the design. Flight test results show that the optimization method is very effective. Although the method is proposed for competition aircraft, it is also applicable to other types of aircraft.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

scholarly journals Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

2021 ◽  
Author(s):  
Wenjie Wang ◽  
Qifan Deng ◽  
Ji Pei ◽  
Jinwei Chen ◽  
Xingcheng Gan
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Pump ◽  
Pressure Fluctuation ◽  
Optimization Method ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Unsteady Pressure ◽  
Design Variables ◽  
Rotor Stator Interaction ◽  
Unsteady Simulation

Abstract Pressure fluctuation due to the rotor-stator interaction in turbomachinery is unavoidable, inducing strong vibration and even shortening the lifecycle. The investigation on optimization method of an industrial centrifugal pump was carried out to reduce the pressure fluctuation intensity. Considering the time-consuming transient calculation of unsteady pressure, a novel optimization strategy was proposed by discretizing design variables and genetic algorithm. Four highly related design parameters were chosen, and 40 transient sample cases were generated and simulated using an automatic simulation program. Furthermore, a modified discrete genetic algorithm (MDGA) was proposed to reduce the optimization cost by unsteady simulation. For the benchmark test, the proposed MDGA showed a great advantage over the original genetic algorithm in terms of searching speed and could deal with the discrete variables effectively. After optimization, an improvement in terms of the performance and stability of the inline pump was achieved.

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.

scholarly journals CONSTRUCTAL DESIGN AND SIMULATED ANNEALING EMPLOYED FOR GEOMETRIC OPTIMIZATION OF A Y-SHAPED CAVITY INTRUDED INTO CONDUCTIVE WALL

2015 ◽  
Vol 14 (1) ◽  
pp. 79
Author(s):  
G. V. Gonzales ◽  
E. D. Dos Santos ◽  
L. R. Emmendorfer ◽  
L. A. Isoldi ◽  
E. S. D. Estrada ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Solid Wall ◽  
Internal Heat ◽  
Optimization Methods ◽  
Optimization Method ◽  
Internal Heat Generation ◽  
Geometric Optimization ◽  
Constructal Design ◽  
Optimal Shapes

he problem study here is concerned with the geometrical evaluation of an isothermal Y-shaped cavity intruded into conducting solid wall with internal heat generation. The cavity acts as a sink of the heat generated into the solid. The main purpose here is to minimize the maximal excess of temperature (θmax) in the solid. Constructal Design, which is based on the objective and constraints principle, is employed to evaluate the geometries of Y-shaped cavity. Meanwhile, Simulated Annealing (SA) algorithm is employed as optimization method to seek for the best shapes. To validate the SA methodology, the results obtained with SA are compared with those achieved with Genetic Algorithm (GA) and Exaustive Search (ES) in recent studies of literature. The comparison between the optimization methods (SA, GA and ES) showed that Simulated Annealing is highly effective in the search for the optimal shapes of the studied case.

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

1999 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Structural Design ◽  
Hierarchical Structures ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Problems ◽  
Design Variables ◽  
The Hierarchical Structure ◽  
Machine Systems ◽  
System Designs ◽  
Crossover And Mutation

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

scholarly journals Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

2019 ◽  
Vol 11 (18) ◽  
pp. 5102
Author(s):  
Hongxia Zhu ◽  
Gang Zhao ◽  
Li Sun ◽  
Kwang Y. Lee
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Turbine Unit ◽  
Optimization Method ◽  
System Stability ◽  
Local Model ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Model Network ◽  
Local Model Network

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

Parameter Identification of Thermal Visco-Plastic Model Considering Dynamic Recrystallization

2007 ◽  
Vol 561-565 ◽  
pp. 1869-1874
Author(s):  
Quan Lin Jin ◽  
Yan Shu Zhang
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Parameter Identification ◽  
Optimization Methods ◽  
Optimization Method ◽  
Special Program ◽  
Global Optimization Method ◽  
Material Parameters ◽  
Real Coded Genetic Algorithm ◽  
Deformation Test

A hybrid global optimization method combining the Real-coded genetic algorithm and some classical local optimization methods is constructed and applied to develop a special program for parameter identification. Finally, the parameter identification for both 26Cr2Ni4MoV steel and AZ31D magnesium alloy is carried out by using the program. A comparison of deformation test and numerical simulation shows that the parameter identification and the obtained two sets of material parameters are all available.

Design Optimization of Valveless DTH Pneumatic Hammers by a Weighted Pseudo-Gradient Search Method

1998 ◽  
Vol 120 (4) ◽  
pp. 687-694 ◽  
Author(s):  
L. E. Chiang ◽  
E. B. Stamm
Keyword(s):  
Dynamic Model ◽  
Linear Equations ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Variables ◽  
Standard Procedures ◽  
Non Linear ◽  
Predicted Values ◽  
Gradient Search Method ◽  
Non Linear Equations

A design methodology for Down-The-Hole (DTH) pneumatic hammers used for rock drilling is proposed which renders an optimal design for a given set of constraints. A generic non-linear dynamic model developed by the authors is used to compute the hammer performance. This model consists of a set of six differential equations plus a set of twenty non-linear polynomial equations. In addition there are parameter range restrictions given by fabrication and operational standard procedures. In any given application, magnitudes such as power, impact energy, frequency, efficiency and mass flow may be sought for optimality. However these magnitudes must be computed by integration after solving the dynamic model over an entire cycle, thus traditional optimization methods for non-linear equations that are based in gradient information are not suitable. Hence a method that uses secant information is used to approximate the gradient of the space of design variables. Several prototypes using this optimization method have been designed and field tested. The results are in agreement with predicted values.

Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

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