scholarly journals Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4366
Author(s):  
Yong-Qiang Wang ◽  
Rong-Heng Zhao ◽  
Ye Liu ◽  
Yi-Zheng Chen ◽  
Xiao-Yi Ma
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Optimization Method ◽  
Small Sample ◽  
Arch Dam ◽  
Genetic Optimization ◽  
The Finite Element Method ◽  
Kriging Surrogate Model ◽  
Arch Dams ◽  
Sample Set

Shape optimization of single-curvature arch dams using the finite element method (FEM) is often computationally expensive. To reduce the computational burden, this study introduces a new optimization method, combining a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM), for determining the optimal shape of a single-curvature arch dam. At the start of genetic optimization, a KSM was constructed using a small sample set. In each iteration of optimization, the minimizing predictor criterion and low confidence bound criterion were used to collect samples from the domain of interest and accumulate them into a small sample set to update the KSM until the optimization process converged. A practical problem involving the optimization of a single-curvature arch dam was solved using the introduced GA-SKSM, and the performance of the method was compared with that of GA-KSM and GA-FEM methods. The results revealed that the GA-SKSM method required only 5.40% and 12.40% of the number of simulations required by the GA-FEM and GA-KSM methods, respectively. The GA-SKSM method can significantly improve computational efficiency and can serve as a reference for effective optimization of the design of single-curvature arch dams.

scholarly journals Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Optimal Shape ◽  
Small Sample ◽  
Gravity Dam ◽  
Kriging Surrogate Model ◽  
Gravity Dams ◽  
Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

scholarly journals Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042005
Author(s):  
Xueyi Liu ◽  
Junhao Dong ◽  
Guangyu Tu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Performance Prediction ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Evaluation Index ◽  
Genetic Optimization ◽  
Quantum Genetic Algorithm

Abstract Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

Shape Optimization of Double Curved Arch Dams With Spline Function in Shape Description Module

1996 ◽  
Author(s):  
Seyed M. R. Behfar
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Sequential Quadratic Programming ◽  
Spline Function ◽  
Mathematical Formulation ◽  
Element Model ◽  
Arch Dam ◽  
Shape Description ◽  
Arch Dams ◽  
3 Dimensional

Abstract Shape description is the first step of shape optimization. This paper shows the possibility of shape description with spline function for surfaces of a double curved arch dam. A mathematical formulation of the boundary of the structure is necessary for the optimization algorithm. A finite element model with 3-dimensional elements is used for structural analysis of the arch dam and its foundation. The optimization algorithm, a sequential quadratic programming (SQP), can be started from initial unfeasible points.

Genetic Optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

2014 ◽  
Vol 12 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Xi Chen ◽  
Wenqi Zhong ◽  
Tiancai Wang ◽  
Fei Liu ◽  
Zhi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
Shaft Furnace ◽  
Fitness Function ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Genetic Optimization ◽  
Gas Consumption

Abstract Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

scholarly journals Compliance Model and Structure Optimization Method Based on Genetic Algorithm for Flexure Hinge Based on X-Lattice Structure

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yin Zhang ◽  
Jianwei Wu ◽  
Jiubin Tan
Keyword(s):  
Genetic Algorithm ◽  
Lattice Structure ◽  
Structural Parameters ◽  
Structure Optimization ◽  
Optimization Method ◽  
Flexure Hinge ◽  
The Finite Element Method ◽  
Flexure Hinges ◽  
Truss Model ◽  
Compliance Model

In order to obtain a new structure of beam flexure hinge with good performance, the flexure hinge based on the X-lattice structure is researched in this paper. The truss model in the finite element method is used to model the 6-DOF compliance of the flexure hinge based on the X-lattice structure. The influence of structural parameters on the compliance and compliance ratio of flexure hinges is analyzed based on this model, and the performance is compared with the traditional beam flexure hinge of the same size. In order to design a flexure hinge based on the X-lattice structure with good comprehensive performance, this paper proposes an intelligent structure optimization method based on a genetic algorithm. The feasibility of the optimization algorithm is verified by an example.

Thermal Stress Compensation of MgO Concrete in Construction of High Arch Dams in Cold Areas

2014 ◽  
Vol 852 ◽  
pp. 427-431 ◽  
Author(s):  
Xiang Yang Jing ◽  
Xing Hong Liu ◽  
Xu Zhang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Thermal Stress ◽  
Arch Dam ◽  
Mass Concrete ◽  
The Finite Element Method ◽  
Arch Dams ◽  
Stress Compensation ◽  
Mgo Concrete ◽  
High Arch Dams

When mixed with the admixture MgO, the volume contraction deformation of mass concrete could be compensated, which is an effective way to solve the temperature cracking problems in construction of concrete projects. Taking an arch dam in China as an example, the thermal stress compensation characteristics of MgO concrete in cold areas are studied using the finite element method. Results indicate that the stress compensation of MgO concrete is significant inside the dam, and with a better effect along with a larger mixing amount of MgO in a certain range. But on the surface of the dam, the stress compensation of MgO concrete is not significant.

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