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.

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.

Research on Lightweight Multi-Objective Optimization for Closed Body-in-White Structure

2015 ◽  
Vol 724 ◽  
pp. 93-99
Author(s):  
Chuan Qing Wang ◽  
Deng Feng Wang ◽  
Shuai Zhang
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Rbf Neural Network ◽  
Collaborative Optimization ◽  
Side Impact ◽  
Nsga Ii ◽  
Frontal Crash ◽  
Multi Objective ◽  
Body In White ◽  
Two Stages

The 100% frontal crash and side impact performances of a passenger car are analyzed and compared with tests. The structural optimization of the Closed Body-in-White (BIW) is divided into two stages which are 100% frontal crash safe part optimization and side impact safe part optimization. Use the Optimal Latin hypercube (Opt LHD) design method to generate sample points. Take the Radial Basis Functions (RBF) neural network method to establish optimization approximation model. The non-dominated sorting genetic algorithm (NSGA-II) was used to conduct multi-objective collaborative optimization design. The results show that the total mass of the closed BIW is reduced 9.745kg; the light weight rate was 10.27%. The Crashworthiness performance of the closed BIW does not change obviously.

scholarly journals Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 270
Author(s):  
Ning Huang ◽  
Zhenlin Li ◽  
Baoshan Zhu
Keyword(s):  
Natural Gas ◽  
Optimization Design ◽  
Draft Tube ◽  
Liquefied Natural Gas ◽  
Design Parameters ◽  
Surface Model ◽  
Blade Loading ◽  
Hydraulic Machinery ◽  
Cavitation Phenomenon ◽  
The Relationship

The application of a cryogenic liquefied natural gas expander can reduce the production of flash steam and improve the efficiency of natural gas liquefaction. Like traditional hydraulic machinery, cavitation will occur during the operation of a liquefied natural gas expander, in particular, there is a strong vortex flow in the draft tube, and the cavitation phenomenon is serious. In this paper, the energy loss coefficient of the draft tube is used to describe the cavitation flow in the draft tube, and the goal of reducing the cavitation in the draft tube is achieved through the optimization design of the runner. Different runner models within the range of design parameters were obtained using the Latin hypercube test, and the relationship between design parameters and objective functions is constructed by a second-order response surface model. Finally, the optimized runners were obtained using a genetic algorithm. The effects of blade loading distribution and blade lean angles on the cavitation in the draft tube were studied. According to the optimization results, the blade loading distribution and blade lean angles are recommended in the end.

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.

Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Özkan ◽  
Mustafa Serdar Genç
Keyword(s):  
Structural Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Optimization Study

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

scholarly journals Multi-Objective Optimization Design of a Notch Filter Based on Improved NSGA-II for Conducted Emissions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 83213-83223 ◽  
Author(s):  
Lu Zhang ◽  
Hongjuan Ge ◽  
Ying Ma ◽  
Jianliang Xue ◽  
Huang Li ◽  
...  
Keyword(s):  
Optimization Design ◽  
Notch Filter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conducted Emissions

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

Multi-Objective Optimization Design Method for Tandem Compressor Cascade at Design and Off Design Conditions

2010 ◽  
Author(s):  
Yaping Ju ◽  
Chuhua Zhang
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Pressure Ratio ◽  
Aerodynamic Performance ◽  
Multi Objective Optimization ◽  
Ann Model ◽  
Compressor Cascade ◽  
Control Cost ◽  
Multi Objective ◽  
Operation Conditions

Recently, there has been a renewed interest in the research of tandem compressor cascades due to the high stage pressure ratio and low control cost. Firstly, the computational fluid dynamics (CFD) method is employed to examine the particular aerodynamic performance of the tandem cascade. Then we propose an automatic multi-objective optimization design method of the tandem cascade for the superior aerodynamic performance under the multiple operation conditions. Particular efforts have been devoted to the gap geometry optimization in terms of the front and aft airfoil relative position, camber turning ratio as well as chord ratio. The multi-objective optimization algorithm comprises a refined multi-objective genetic algorithm (MOGA) and a developed artificial neural network (ANN) model which is used to fast approximate the aerodynamic performance of the tandem cascade. The results show that the tandem cascade outperforms the single cascade in terms of producing higher pressure ratio and lower losses while the operation range is rather narrow. The optimized all-better-than (ABT) tandem cascade has its design point performance significantly improved while the operation range slightly widened. We also find that a slight axial proximity and separation of the tandem airfoils are beneficial to widening the positive and negative operation range, respectively. This research is useful to the tandem compressor cascade design in minimizing the stage number of the engine compressors.

PD/PID Controller Design for Seismic Control of High-Rise Buildings Using Multi-Objective Optimization: A Comparative Study with LQR Controller

2017 ◽  
Vol 11 (03) ◽  
pp. 1750009 ◽  
Author(s):  
Sadegh Etedali ◽  
Saeed Tavakoli
Keyword(s):  
Optimization Design ◽  
Tall Buildings ◽  
Pid Controllers ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Seismic Control ◽  
High Rise ◽  
Lqr Controller ◽  
Structural Responses

This paper developed multi-objective optimization design of proportional–derivative (PD) and proportional–integral–derivative (PID) controllers for seismic control of high-rise buildings. The case study is an 11-story realistic building equipped with active tuned mass damper (ATMD). Four earthquakes and nine performance indices are taken into account to assess the performance of the controllers. To create a good trade-off between the performance and robustness of the closed-loop structural system, a non-dominated sorting genetic algorithm, NSGA-II, is employed. To evaluate the degree of robustness of the controllers, four structural models with uncertainties in the nominal model of the structure is considered. For comparison purposes, a linear quadratic regulator (LQR) controller is also designed in the numerical simulations. Simulation results show that the proposed PD and PID controllers significantly perform better than the LQR in reduction of structural responses. Also, it is shown that the LQR does not provide a good performance in strong earthquakes. However, PD and PID controllers are able to significantly reduce structural responses. Moreover, it is shown that the PID has a better performance than the PD. The results also show that the proposed controllers are capable of maintaining a desired performance in the presence of modeling errors. They also have several advantages over modern controllers in terms of simplicity and reduction of required sensors and computational resources in tall buildings.

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