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 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 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 design of spur gear based on NSGA-II and decision making

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882493 ◽  
Author(s):  
Qizhi Yao
Keyword(s):  
Decision Maker ◽  
Optimization Design ◽  
Optimal Solution ◽  
Spur Gear ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Deviation Index ◽  
Decision Variables

Optimization design of spur gear is a complicated work because the performance characteristics depend on different types of decision variables and objectives. Traditional single-objective optimization design of the spur gear always results in poor outcomes relative to other objectives due to objectives’ competition with each other. Therefore, this study works on the spur gear design based on the multi-objective optimization model of elitist non-dominated sorting genetic algorithm (NSGA-II). In the model, gear module, teeth number, and transmission ratio are decision variables, while center distance, bearing capacity coefficient, and meshing efficiency are objectives. Final optimal solutions are picked out from Pareto frontier calculated from NSGA-II using the decision makers of Shannon Entropy, linear programming technique for multidimensional analysis of preference (LINMAP), and technique of order preference by similarity to an ideal solution (TOPSIS). Meanwhile, a deviation index is used to evaluate the reasonable status of the optimal solutions. From triple-objective and dual-objective optimization results, it is found that the optimal solution selected from LINMAP decision maker shows a relatively small deviation index. It indicates that LINMAP decision maker may yield better optimal solution. This study could provide some beneficial information for spur design.

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 Load Sharing Multiobjective Optimization Design of a Split Torque Helicopter Transmission

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Chenxi Fu ◽  
Ning Zhao ◽  
Yongzhi Zhao
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimal Solution ◽  
Load Sharing ◽  
Operating Conditions ◽  
Design Parameters ◽  
Nonlinear Dynamic Model ◽  
Nsga Ii ◽  
Reference Design ◽  
Split Torque

Split torque designs can offer significant advantages over the traditional planetary designs for helicopter transmissions. However, it has two unique properties, gap and phase differences, which result in the risk of unequal load sharing. Various methods have been proposed to eliminate the effect of gap and promote load sharing to a certain extent. In this paper, system design parameters will be optimized to change the phase difference, thereby further improving load sharing. A nonlinear dynamic model is established to measure the load sharing with dynamic mesh forces quantitatively. Afterwards, a multiobjective optimization of a reference split torque design is conducted with the promoting of load sharing property, lightweight, and safety considered as the objectives. The load sharing property, which is measured by load sharing coefficient, is evaluated under multiple operating conditions with dynamic analysis method. To solve the multiobjective model with NSGA-II, an improvement is done to overcome the problem of time consuming. Finally, a satisfied optimal solution is picked up as the final design from the Pareto optimal front, which achieves improvements in all the three objectives compared with the reference design.

Optimization Design of Helical Spring Based on Multi-Objective Genetic Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1068-1071
Author(s):  
Kang Li Shao ◽  
Feng Wang ◽  
Yong Hai Wu
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Coil Spring ◽  
Design Quality ◽  
Nsga Ii ◽  
Suspension Spring ◽  
Multi Objective ◽  
Free Vibration Frequency ◽  
Light Vehicle

Suspension spring is used in the suspension system of light vehicle and medium buses widely, and its design quality related to stability and security of the vehicle. This paper take the suspension coil spring of a light vehicle as the research object, its multi-objective optimization model is established. The volume of spring and one frequency free vibration frequency are taken as optimization objective, the strength, stiffness, stability, fatigue strength and the winding ratio of the spring are taken as constraints, and use NSGA-II algorithm, obtained Pareto optimal solution set of the optimization problem. The coil spring model and optimization method used in this paper is also suitable for optimization design of other spring.

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.

A new jig-shape optimization method for the high aspect ratio wing

2018 ◽  
Vol 91 (1) ◽  
pp. 124-133
Author(s):  
Zhe Yuan ◽  
Shihui Huo ◽  
Jianting Ren
Keyword(s):  
Aspect Ratio ◽  
Shape Optimization ◽  
High Aspect Ratio ◽  
Optimization Design ◽  
Design Method ◽  
Aircraft Design ◽  
Optimization Method ◽  
Attack Angle ◽  
Structural Deformation ◽  
Content Type

Purpose Computational efficiency is always the major concern in aircraft design. The purpose of this research is to investigate an efficient jig-shape optimization design method. A new jig-shape optimization method is presented in the current study and its application on the high aspect ratio wing is discussed. Design/methodology/approach First, the effects of bending and torsion on aerodynamic distribution were discussed. The effect of bending deformation was equivalent to the change of attack angle through a new equivalent method. The equivalent attack angle showed a linear dependence on the quadratic function of bending. Then, a new jig-shape optimization method taking integrated structural deformation into account was proposed. The method was realized by four substeps: object decomposition, optimization design, inversion and evaluation. Findings After the new jig-shape optimization design, both aerodynamic distribution and structural configuration have satisfactory results. Meanwhile, the method takes both bending and torsion deformation into account. Practical implications The new jig-shape optimization method can be well used for the high aspect ratio wing. Originality/value The new method is an innovation based on the traditional single parameter design method. It is suitable for engineering application.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

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