Optimization of magnetic field design for Hall thrusters based on a genetic algorithm

Magnetic field design is essential for the operation of Hall thrusters.This study focuses on utilizing a genetic algorithm to optimize the magnetic field configuration of SPT70.A 2D hybrid PIC-DSMC and channel-wall erosion model are employed to analyze the plume divergence angle and wall erosion rate,while a Farady probe measurement and laser profilometry system are set up to verify the simulation results.The results demonstrate that the genetic algorithm contributes to reducing the divergence angle of the thruster plumes and alleviating the impact of high-energy particles on the discharge channel wall,reducing the erosion by 5.5%and 2.7%,respectively.Further analysis indicates that the change from a divergent magnetic field to a convergent magnetic field,combined with the upstream shift of the ionization region,contributes to the improving the operation of the Hall thruster.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

Reliability-Based Optimum Design of a Simple Offshore Platform Based on Genetic Algorithms

In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reliability optimum design of the structure under stochastic wave load. The fatigue damage model and the yield failure reliability analyzing model are used in the paper. The reliability of the models and the effectiveness of genetic algorithm are shown by the results of optimum design.

Optimal Design of Materials for DJMP Based on Genetic Algorithm

The genetic algorithm was used in optimal design of deep jet method pile.The cost of deep jet method pile in one unit area of foundation was taken as the objective function.All the restrains were listed following the corresponding specification.Suggestions were proposed and the modified.The real-coded Genetic Algorithm was given to deal with the problems of excessive computational cost and premature convergence.Software system of optimal design of deep jet method pile was developed.

Aerodynamic design of transonic fan/compressor by 3D viscous RNS combined with genetic algorithms

This paper presents an aerodynamic design of a small transonic fan by 3D viscous RNS solver combined with genetic algorithms.The aerodynamic design system based on the 3D viscous RNS solver reduces the dependency on the design experience for designers.Furthermore the optimum with genetic algorithms is an effective method for improving the transonic fan performance as a part of the design system.The design result showed that the transonic fan designed by this method reaches the design requirement even with more efficiency value.

Short Range Top Attack Trajectory Optimum Design Based on Genetic Algorithm

A flying-body is considered as the reference model, the optimized mathematical model is established. The genetic operators are designed and algorithm parameters are selected reasonably. The scheme control signal in short range top attack flight trajectory is optimized by using genetic algorithm. The short range top attack trajectory designed meets the design requirements, with the increase of the falling angle and the decrease of the minimum range. The application of genetic algorithm to top attack trajectory optimization is proved to be feasibly and effectively according to the analyses of results.

Development of an electrode intelligent design system based on adaptive fuzzy neural network and genetic algorithm

The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ＋＋ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files.

Optimal Building Frame Column Design Based on the Genetic Algorithm

Building structure is like the skeleton of the building,it bears the effects of various forces and forms a supporting system,which is the material basis on which the building depends.Hence building structure design is a vital part in architecture design,architects often explore novel applications of their technologies for building structure innovation.However,such searches relied on experiences,expertise or gut feeling.In this paper,a new design method for the optimal building frame column design based on the genetic algorithm is proposed.First of all,in order to construct the optimal model of the building frame column,building units are divided into three categories in general:building bottom,main building and building roof.Secondly,the genetic algorithm is introduced to optimize the building frame column.In the meantime,a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm.Finally,effectiveness of the mentioned algorithm is verified through the simulation experiment.

Genetic Algorithm for Injection Mould Design and Moulding

Obtaining the optimal values of the parameters for th e design of a required mould and the operation of the moulding process are diffi cult, this is due to the complexity of product geometry and the variation of pla stic material properties. The typical parameters for the mould design and mouldi ng process are melt flow length, injection pressure, holding pressure, back pres sure, injection speed, melt temperature, mould temperature, clamping force, inje ction time, holding time and cooling time. This paper discusses the difficulties of using the current computer aided optimization methods to acquire the values of the parameters. A method that is based on the concept of genetic algorithm is proposed to overcome the difficulties. The proposed method describes in details on how to attain the optimal values of the parameters form a given product geom etry.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Genetic Algorithms for the Optimal Design of Electromagnetic Micro-Motors

0 ＩｎｔｒｏｄｕｃｔｉｏｎＡｓａｃｒｕｘｄｅｖｉｃｅｉｎＭｉｃｒｏｅｌｅｃｔｒｏｍａｇｎｅｔｉｃＳｙｓｔｅｍｓ(ＭＥＭＳ),ｍａｎｙａｐｐｌｉｃａｔｉｏｎｓｏｆｍｉｃｒｏｍｏｔｏｒａｒｅｅｎｖｉｓａｇｅｄｉｎｄｉｆｆｅｒｅｎｔｆｉｅｌｄｓ(ｓｕｃｈａｓ?..

Immune Genetic Algorithm for Optimal Design

A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.

APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT

The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.

Optimal Design of Thermal Dissipation for the Array Power LED by using the RSM with Genetic Algorithm

In accordance with the enhancement for luminous efficiency improving, LED (Light Emitting Diode) has been gradually developed by combining the characteristics of small volume, impact resistance, good reliability, long life, low power consumption with multiple purposes for energy saving and environmental protection. Therefore, the array LED has been widely applied in human livings nowadays. This study applies the finite element analysis software ANSYS to analyze the thermal behavior of the array power LED work lamp which is modeled by four same-size LED with MCPCB (Metal Core Print Circuit Board) mounted on a base heat-sink. The Flotran heat flow analysis is applied to obtain the natural convection of air coefficient, while the convection value can be confirmed by the iterative method since it is set as the boundary condition for ANSYS thermal analysis to obtain the temperature distribution, accordingly the chip junction temperature and the base heat-sink temperature were followed through experiments in order to check if the simulation results meet the design requirements and coincide with the power LED product design specification. Prior to the optimal design process for chip junction temperature, the most significant parameters were first chosen by the fractional factorial design. The regressive models were respectively setup by the dual response surface method (RSM) and the mixed response surface method. Furthermore, the genetic algorithm combined with response surface method was applied to acquire the optimal design parameters, and the results were obtained from both methods, which are reviewed for comparison. Afterwards, the mixed response surface method is adopted to investigate the effects of interactions among various factors on chip junction temperature. In conclusion, it is found that the thermal conductivity of MCPCB and the height of base heat-sink are the two major significant factors. In addition, the interactive effects between chip size and thermal conductivity of chip adhesion layer are acknowledged as the most significant interaction influenced on the chip junction temperature.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Satellite Constellation Design with Multi-Objective Genetic Algorithm for Regional Terrestrial Satellite Network

Constellations design for regional terrestrial-satellite network can strengthen the coverage for incomplete terrestrial cellular network. In this paper, a regional satellite constellation design scheme with multiple feature points and multiple optimization indicators is proposed by comprehensively considering multi-objective optimization and genetic algorithm, and "the Belt and Road" model is presented in the way of dividing over 70 nations into three regular target areas. Following this, we formulate the optimization model and devise a multi-objective genetic algorithm suited for the regional area with the coverage rate under simulating, computing and determining. Meanwhile, the total number of satellites in the constellation is reduced by calculating the ratio of actual coverage of a single-orbit constellation and the area of targets. Moreover, the constellations' performances of the proposed scheme are investigated with the connection of C++ and Satellite Tool Kit(STK). Simulation results show that the designed satellite constellations can achieve a good coverage of the target areas.

Multi-Criterion Optimal Design of Automotive Door Based on Metamodeling Technique and Genetic Algorithm

A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.