scholarly journals Optimization Design on Functionally Graded Cem for Trains Based on LPM Model with Calibrated Parameters

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ruixian Qin ◽  
Bingzhi Chen
Keyword(s):  
Optimization Design ◽  
Optimization Method ◽  
Functionally Graded ◽  
Optimization Techniques ◽  
Design Parameters ◽  
Nsga Ii ◽  
Longitudinal Response ◽  
Lumped Parameter ◽  
1D Model ◽  
Crash Model

Lumped parameter modeling (LPM) combined with optimization techniques is an efficient approach for parametric configuration design of energy absorption to improve crashworthiness performance during train collision. This work proposed a simplified model by introducing a bar element to consider the influence of the carbody in a collision process. The optimization method is applied to calibrate the equivalent parameters of the bar element. Bar elements with calibrated parameters are adopted in establishing a one-dimensional (1D) model for the train crash. Subsequently, a novel crash energy management (CEM) mode with functionally graded energy (FGE) configuration is introduced to the train crash model for improving crashworthiness performance. The influence of parameters in graded function on interfacial force and peak acceleration is investigated and optimal design parameters are obtained by Nondominated Sorting Genetic Algorithm (NSGA-II). It is concluded that considering the behavior of the carbody can improve the accuracy of LPM in predicting the longitudinal response, and the gradient CEM is a potential energy configuration mode to improve the crashworthiness of the train in a collision.

Parameter study and optimization design of a hat oblique tunnel portal

2021 ◽  
pp. 095440972110140
Author(s):  
Zijian Guo ◽  
Tanghong Liu ◽  
Wenhui Li ◽  
Yutao Xia
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Method ◽  
Design Parameters ◽  
Parameter Study ◽  
Buffer Structure ◽  
Tunnel Entrance ◽  
The Ideal

The present work focuses on the aerodynamic problems resulting from a high-speed train (HST) passing through a tunnel. Numerical simulations were employed to obtain the numerical results, and they were verified by a moving-model test. Two responses, [Formula: see text] (coefficient of the peak-to-peak pressure of a single fluctuation) and[Formula: see text] (pressure value of micro-pressure wave), were studied with regard to the three building parameters of the portal-hat buffer structure of the tunnel entrance and exit. The MOPSO (multi-objective particle swarm optimization) method was employed to solve the optimization problem in order to find the minimum [Formula: see text] and[Formula: see text]. Results showed that the effects of the three design parameters on [Formula: see text] were not monotonous, and the influences of[Formula: see text] (the oblique angle of the portal) and [Formula: see text] (the height of the hat structure) were more significant than that of[Formula: see text] (the angle between the vertical line of the portal and the hat). Monotonically decreasing responses were found in [Formula: see text] for [Formula: see text] and[Formula: see text]. The Pareto front of [Formula: see text] and[Formula: see text]was obtained. The ideal single-objective optimums for each response located at the ends of the Pareto front had values of 1.0560 for [Formula: see text] and 101.8 Pa for[Formula: see text].

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

scholarly journals Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1107
Author(s):  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim ◽  
Mohamed El-Nemr
Keyword(s):  
Optimization Techniques ◽  
Design Parameters ◽  
Jaya Algorithm ◽  
Switched Reluctance Machine ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Machine ◽  
Linear Relationships ◽  
Forward Equations

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

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 Thermal performance analysis and optimization design of dry type air core reactor with the double rain cover

2021 ◽  
pp. 67-67
Author(s):  
FaTing Yuan ◽  
Shouwei Yang ◽  
Shihong Qin ◽  
Kai Lv ◽  
Bo Tang ◽  
...  
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Temperature Rise ◽  
Optimization Design ◽  
Orthogonal Experiment ◽  
Structural Parameters ◽  
Fluid Velocity ◽  
Optimization Method ◽  
Design Parameters ◽  
Air Core

In this paper, a fluid-thermal coupled finite element model is established according to the design parameters of dry type air core reactor. The detailed temperature distribution can be achieved, the maximum error coefficient of temperature rise is only 6% compared with the test results of prototype, and the accuracy of finite element calculate method is verified. Taking the equal height and heat flux design parameters of reactor as research object, the natural convection cooling performance of reactor with and without the rain cover is investigated. It can be found that the temperature rise of reactor is significantly increased when adding the rain cover, and the reasons are given by analyzing the fluid velocity distribution of air dcuts between the encapsulation coils. In order to reduce the temperature rise of the reactor with the rain cover, the optimization method based on the orthogonal experiment design and finite element method is proposed. The six factors of the double rain cover are given, which mainly affect the temperature rise of reactor, and the five levels are selected, the influence curve and contribution rate of each factor on the temperature rise of reactor are analyzed. The results show that the contribution ratio of the parameter H1, L1 and L2, are obviously higher than the parameter H2, L3 and ?, so the more attention should be paid in the design of double rain cover. Meanwhile, the optimal structural parameters of rain cover are given based on the influence curves, and the temperature rise is only 43.25?C. The results show that the optimization method can reduce the temperature rise of reactor significantly. In addition, the temperature distribution of inner encapsulations coils of reactor are basically the same, the current carrying capacity of coils can be fully utilized, which provides an important guidance for the optimization design of reactor.

Multi-Objective Shape Optimization Design for Liquefied Natural Gas Cryogenic Helical Corrugated Steel Pipe

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Zhixun Yang ◽  
Jun Yan ◽  
Jinlong Chen ◽  
Qingzhen Lu ◽  
Qianjin Yue
Keyword(s):  
Natural Gas ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Liquefied Natural Gas ◽  
Steel Pipe ◽  
Structural Deformation ◽  
Design Parameters ◽  
Nsga Ii ◽  
Multi Objective ◽  
Operation Conditions

Recently, the flexible cryogenic hose has been preferred as an alternative to exploit offshore liquefied natural gas (LNG), in which helical corrugated steel pipe is the crucial component with C-shaped corrugation. Parametric finite element models of the LNG cryogenic helical corrugated pipe are established using a three-dimensional shell element in this paper. Considering the nonlinearity of cryogenic material and large geometric structural deformation, mechanical behaviors are simulated under axial tension, bending, and internal pressure loads. In addition, design parameters are determined to optimize the shape of flexible cryogenic hose structures through sectional dimension analysis, and sensitivity analysis is performed with changing geometric parameters. A multi-objective optimization to minimize stiffness and stress is formulated under operation conditions. Full factorial experiment and radial basis function (RBF) neural network are applied to establish the approximated model for structure analysis. The set of Pareto optimal solutions and value range of parameters are obtained through nondominated sorting genetic algorithm II (NSGA-II) under manufacturing and stiffness constraints, thereby providing a feasible optimal approach for the structural design of LNG cryogenic corrugated hose.

Multi-Objective Shape Optimization Design for LNG Cryogenic Helical Corrugated Steel Pipe

2016 ◽  
Author(s):  
Zhixun Yang ◽  
Jun Yan ◽  
Qingzhen Lu ◽  
Jinlong Chen ◽  
Shanghua Wu ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Steel Pipe ◽  
Structural Deformation ◽  
Design Parameters ◽  
Operation Condition ◽  
Nsga Ii ◽  
Multi Objective

The flexible cryogenic hose has been a favored alternative for offshore liquefied natural gas (LNG) exploitation recently, of which helical corrugated steel pipe is the crucial component with C shaped corrugation. Parametric finite-element models of LNG cryogenic helical corrugated pipe are presented based on 3D shell element in this paper. Taking account of nonlinearity such as cryogenic material and large geometric structural deformation, mechanical behavior characteristics results are obtained under axial tensional, bending and inner pressure loads. Meanwhile, the design parameters are determined for the shape optimization of structures of the flexible cryogenic hose through sectional dimension analysis, and sensitivity analysis is performed with changing geometric parameters. A multi-objective optimization with the object of minimizing stiffness and strength stress is formulated based on operation condition. Full factorial experiment and radial basis function (RBF) neural network are applied to establish the approximated model for the analysis of the structure. The Pareto optimal solution set and value range of parameters are obtained through NSGA-II GA algorithm under manufacturing and stiffness constraints. It provides a feasible optimal approach for the structural design of LNG cryogenic corrugated hose.

Study of Parameters Optimization on Seismic Isolated Railway Bridges

2011 ◽  
Vol 50-51 ◽  
pp. 135-139
Author(s):  
Tie Yi Zhong ◽  
Chao Yi Xia ◽  
Feng Li Yang
Keyword(s):  
Optimal Design ◽  
Optimization Design ◽  
Seismic Isolation ◽  
Optimization Method ◽  
Relative Displacement ◽  
Parameters Optimization ◽  
Design Parameters ◽  
Isolation System ◽  
Railway Bridges ◽  
Design Variables

Based on optimization theories, considering soil-structure interaction and running safety, the optimal design model of the seismic isolation system with lead-rubber bearings (LRB) for a simply supported railway beam bridge is established by using the first order optimization method in ANSYS, which the parameters of the isolation bearing are taken as design variables and the maximum moments at the bottom of bridge piers are taken as objective functions. The optimal calculations are carried out under the excitation of three practical earthquake waves respectively. The research results show that the ratio of the stiffness after yielding to the stiffness before yielding has important effect on the structural seismic responses. Through the optimal analysis of isolated bridge system, the optimal design parameters of isolation bearing can be determined properly, and the seismic forces can be reduced maximally as meeting with the limits of relative displacement between pier top and beam, which provides efficient paths and beneficial references for dynamic optimization design of seismic isolated bridges.

scholarly journals Predicting Soccer Ball Target through Dynamic Simulation

2020 ◽  
pp. 6-18
Author(s):  
Ying Li ◽  
Junxian Meng ◽  
Qi Li
Keyword(s):  
Dynamic Simulation ◽  
Optimization Design ◽  
Orientation Angle ◽  
Optimization Method ◽  
Design Parameters ◽  
Dynamics Modeling ◽  
Soccer Ball ◽  
Body Dynamics ◽  
Multi Body ◽  
Ball Motion

The intelligent sports analysis of a soccer ball requires accurately simulating its motion and finding the best design parameters (position and orientation) to kick the ball.  An optimization method is proposed to plan, evaluate, and optimize the traveling trajectory of a soccer ball. The theoretical studies go through the multi-body dynamics modeling, dynamic simulation, and optimal objective modeling Based on Newton second law and Hooke’s law, the motion of a soccer ball is established as the time-dependent ordinary differential equations (ODEs). The expected target is expressed as a function of all design parameters. An example is used to simulate a soccer ball shooting a goal. The result of optimization design has given the most optimal combination of the design parameters, which involve theinitial velocity,initial projectile angle, andinitial orientation angle. This research provides a useful method in predicting the trajectory and adjusting the design parameters for the optimization design of a soccer ball motion.

scholarly journals Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II

2017 ◽  
Author(s):  
Chol Nam Mun ◽  
De Chun Ba ◽  
Xiang Ji Yue ◽  
Myong Il Kim
Keyword(s):  
Draft Tube ◽  
Fluid Dynamic ◽  
Hydraulic Turbine ◽  
Optimization Method ◽  
Approximate Model ◽  
Francis Turbine ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Design Matrix ◽  
Nsga Ii

In order to improve the performance of the draft tube in hydraulic turbine, a multi–objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non–dominated sorting genetic algorithm (NSGA–II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA–II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

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