scholarly journals Optimal design of watt six-bar transmission mechanism for morphing trailing edge

2021 ◽  
Vol 268 ◽  
pp. 01077
Author(s):  
Jin Zhou ◽  
Shiwei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Optimization Design ◽  
Aerodynamic Performance ◽  
Optimal Shape ◽  
Transmission Mechanism ◽  
Trailing Edge ◽  
Aerodynamic Shape ◽  
Optimization Stage ◽  
Smooth Deformation

The morphing trailing edge could realize a continuous smooth deformation compared with conventional trailing edge, which effectively improves the aerodynamic performance. In this paper, a multi-step optimization design of watt six-bar transmission mechanism for morphing trailing edge is proposed. In the first optimization stage, the most effective aerodynamic shape and bar position in the middle of the morphing trailing edge is determined. In the second optimization stage, a watt six link transmission mechanism is proposed by using genetic algorithm to match the optimal shape from the first optimization stage. Result shows that the optimal design could achieve the determined aerodynamic shape in the first optimization stage perfectly.

Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components

2014 ◽  
Author(s):  
Cheng Wang ◽  
Chang-qi Yan ◽  
Jian-jun Wang ◽  
Lei Chen ◽  
Gui-jing Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Nuclear Power ◽  
Optimization Design ◽  
Population Diversity ◽  
Geometric Constraints ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Benchmark Function ◽  
Nonlinear Constraint

Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.

Optimization Design of Anti-Sliding Pile Based on Genetic Algorithm

2011 ◽  
Vol 71-78 ◽  
pp. 3914-3917
Author(s):  
Yin Xu ◽  
Qing Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Slope Stability ◽  
Optimization Design ◽  
Fitness Function ◽  
Sliding Surface ◽  
Mutation Probability ◽  
Reinforcement Measures ◽  
Sliding Force

It is important to search the position of the dangerous sliding surface and to design the reinforcement measures in the study of the slope stability. At present, anti-sliding pile is one of the popular reinforcement measures. In this paper, to optimal design the anti-sliding pile, the residual thrust method (RTM) and genetic algorithm are carried out, while the idea that based on the position of the largest anti-sliding force rather than the most dangerous sliding surface to design the anti-sliding piles is presented. The new idea eliminates the possible unsafe situation. In the process of the optimization design of anti-sliding pile, firstly, chromosomes in the genetic algorithm are coded by using decimal method. Secondly, taking the residual thrust as the fitness function in the genetic algorithm, cross probability and mutation probability are all adjusted according to the fitness of individual population, while the best individual population is saved as population of the next step so that to improve the efficiency. Finally, the sliding surface which needs the largest anti-sliding force is searched out. The reasonability and the reliability of the idea are verified by an example.

Optimization and Design for Wind Turbine Airfoil at Multiple Working Conditions Based on Genetic Algorithm

2014 ◽  
Vol 668-669 ◽  
pp. 230-235
Author(s):  
Ya Qiong Chen ◽  
Yue Fa Fang ◽  
Sheng Guo
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Working Conditions ◽  
Optimization Design ◽  
Design Method ◽  
Aerodynamic Performance ◽  
Linear Perturbation ◽  
Airfoil Optimization ◽  
Wind Turbine Airfoil ◽  
Performance Results

S827 wind turbine airfoil was considered as original airfoil, which was created by NREL. Linear perturbation methods were used to get new shape parameters of wind turbine airfoil. Optimization of original airfoil was carried out based on genetic algorithm and XFOIL software, which was used to get aerodynamic performance. Results shows that the lift-drag radio of optimized airfoil was remarkable improved under multiple working conditions. Aerodynamic performance of optimized airfoil was much better comparing with the original airfoil. The optimal design method for wind turbine airfoil used in this paper can be used to optimization design of high lift-drag ratio wind turbine airfoil. Engineering practical value is considered by this method and it is feasible and efficient through example.

Optimal Design of Gears by Means of Genetic Algorithm and Neural Network

2013 ◽  
Vol 397-400 ◽  
pp. 816-820
Author(s):  
Yong Gang Li, ◽  
Yong Mei Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Relative Parameter

Optimal design of gears was complicated with much difficulty to determine the parameter of strength constraint equation, and find the optimal solution. Used BP Neural Network to approximate the relative parameter of gears optimization design which was shown by chart. Used Genetic Algorithm to search the optimal solution. The result shows that the application of Genetic Algorithm and Neural Network in gear optimization is effective.

Two Schemes of Multi-Objective Aerodynamic Optimization for Centrifugal Impeller Using Response Surface Model and Genetic Algorithm

2010 ◽  
Author(s):  
Xiaomin Liu ◽  
Wenbin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Optimization Design ◽  
Aerodynamic Performance ◽  
Centrifugal Impeller ◽  
Surface Model ◽  
Aerodynamic Optimization ◽  
Optimization Scheme ◽  
Impeller Blade ◽  
Multi Objective

This paper presents two schemes of multi-objective aerodynamic optimization design for centrifugal impeller blade. One is genetic algorithm(GA) combined with a commercial computational fluid dynamics(CFD) software, and the other is GA combined with the surrogate model. The two schemes are respectively applied to multi-objective optimization for the same centrifugal impeller blade. For multi-objective genetic algorithm(MOGA), non-uniform mutation and Pareto ranking and fitness-sharing technique are used to obtain fast convergence speed and good capability to search the Pareto front of GA. For the surrogate model based on radial basis function(RBF), design of experiments(DOE) technology is adopted to select samples. The parameters and weight coefficients in the surrogate model are solved by GA instead of traditional least square method. According to the geometrical feature of centrifugal impeller, a three-dimensional reconstruction method for the blade shape based on non-uniform rational B-spline(NURBS) is introduced. The numerical simulation is used to evaluate the aerodynamic performance of the optimal and initial impeller. The computational results show that the aerodynamic performance of impellers designed by both optimization schemes is improved to some extent. At the same time, the main reasons for the improvement in aerodynamic performance of the optimal impeller are revealed. For the optimal impellers, the isentropic efficiency and total pressure ratio are increased by about 1.0% and 3.0% respectively. Through comparison of two schemes applied to the centrifugal impeller optimization design, it is found that the computational performance of the second optimization scheme is superior to that of the first optimization scheme.

scholarly journals A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 984-991 ◽  
Author(s):  
Aimeng Wang ◽  
Jiayu Guo
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Electric Vehicle ◽  
Optimization Design ◽  
Fe Simulation ◽  
Hybrid Genetic Algorithm ◽  
Ipm Machine ◽  
Simulation Results ◽  
Rotor Structure

AbstractA novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

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