An Optimization Design of Knee Airbag for Driver Protection from Inter-City Coach Frontal Impact

2014 ◽  
Vol 945-949 ◽  
pp. 40-43
Author(s):  
Han Yu Wang ◽  
Ji Kuang Yang ◽  
Xiao Qing Jiang ◽  
Li Li
Keyword(s):  
Genetic Algorithms ◽  
Lower Extremity ◽  
Mathematical Models ◽  
Optimization Design ◽  
Injury Risk ◽  
Influence Factors ◽  
Lower Extremities ◽  
Design Parameters ◽  
Frontal Impact ◽  
Multi Objective

<strong>In order to </strong><strong>reduce the injury risk of driver’s lower extremities, a driver knee airbag was de</strong><strong>veloped</strong><strong> and optimized by</strong><strong> using mathematical models</strong><strong>. The influence of eight design parameters of belt and knee airbag on the injuries of driver’s lower extremities was analyzed. The </strong><strong>result </strong><strong>shows that the key influence factors</strong><strong> </strong><strong>are</strong><strong> sensitive to the injuries of lower extremity</strong><strong>, including initiation </strong><strong>timing </strong><strong>of knee airbag,</strong><strong> the strap length of knee airbag and area coefficient for the exhaust openings of knee airbag</strong><strong>. Based on multi-objective genetic algorithms</strong><strong>,</strong><strong> an optimization of the knee airbag was conducted in terms of the three factors. After optimization, the injury risk of driver’s lower extremities is greatly reduced.</strong><strong></strong>

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.

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.

scholarly journals Construction design based on particle group optimization algorithm

2021 ◽  
Vol 30 (1) ◽  
pp. 1040-1053
Author(s):  
Ying Xia ◽  
Mohammad Asif Ikbal ◽  
Mohd Asif Shah
Keyword(s):  
Intelligent Agents ◽  
Optimization Design ◽  
Design Method ◽  
Target Function ◽  
Pso Algorithm ◽  
Design Parameters ◽  
Program Optimization ◽  
Cross Sectional ◽  
Multi Objective ◽  
Run Time

Abstract The machines exhibit an intelligence which is artificial intelligence (AI), and it is the design of intelligent agents. A system is represented by an intelligent agent who perceives its environment and the success rate is maximized by taking the action. The AI research is highly specialized and there are two subfields and each communication fails often. The popular AI approaches include the traditional symbolic AI and computational intelligence. In order to optimize the seismic design of the reinforced concrete pier structure, the particle swarm optimization (PSO) algorithm and the reaction spectrum analysis method are combined; they establish a regular bridge of the design variable with cross-sectional characteristics and reinforcement ratios, with the target function. The seismic optimization design framework of the pier is transformed into a multi-objective optimization problem. Calculations show that the method can quickly obtain the optimal design parameters that meet multi-objective requirements. The improved PSO main program and the calling push-over program run time are 4.32 and 1347.56 s, respectively; the push-over program running time is 99.68% of the run time of the total program. Optimization of the seismic performance of the rear bridge pier is significantly improved and is more in line with the design method; the design method proposed in this article is more practical.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

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.

Sensitivity Analysis and Interval Multi-Objective Optimization for an Occupant Restraint System Considering Craniocerebral Injury

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Qiming Liu ◽  
Xingfu Wu ◽  
Xu Han ◽  
Jie Liu ◽  
Zheyi Zhang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Optimization Design ◽  
Element Model ◽  
Design Parameters ◽  
Craniocerebral Injury ◽  
Restraint System ◽  
Multi Objective Optimization ◽  
Occupant Restraint System ◽  
Multi Objective ◽  
Vehicle Collision

Abstract In vehicle collision accidents, an occupant restraint system (ORS) is crucial to protect the human body from injury, and it commonly involves a large number of design parameters. However, it is very difficult to quantify the importance of design parameters and determine them in the ORS design process. Therefore, an approach of the combination of the proposed approximate sensitivity analysis (SA) method and the interval multi-objective optimization design is presented to reduce craniocerebral injury and improve ORS protection performance. First, to simulate the vehicle collision process and obtain the craniocerebral injury responses, the integrated finite element model of vehicle-occupant (IFEM-VO) is established by integrating the vehicle, dummy, seatbelt, airbag, etc. Then, the proposed approximate SA method is used to quantify the importance ranking of design parameters and ignore the effects of some nonessential parameters. In the SA process, the Kriging metamodel characterizing the relationships between design parameters and injury responses is fitted to overcome the time-consuming disadvantage of IFEM-VO. Finally, according to the results of SA, considering the influence of uncertainty, an interval multi-objective optimization design is implemented by treating the brain injury criteria (BRIC, BrIC) as the objectives and regarding the head injury criterion (HIC) and the rotational injury criterion (RIC) as the constraints. Comparison of the results before and after optimization indicates that the maximum values of the translational and rotational accelerations are greatly reduced, and the ORS protection performance is significantly improved. This study provides an effective way to improve the protection performance of vehicle ORS under uncertainty.

Multi-objective lightweight design of the container S-beam based on MNSGA-II with grey relational analysis

2019 ◽  
Vol 233 (10) ◽  
pp. 3376-3387
Author(s):  
Dengfeng Wang ◽  
Shuang Wang
Keyword(s):  
Weight Reduction ◽  
Grey Relational Analysis ◽  
Optimization Design ◽  
Lightweight Design ◽  
Parametric Model ◽  
Design Parameters ◽  
Combined Method ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

A novel bottom corrugated cross-beam (S-beam) structure improved the dynamic and static performance of a container based on the combination of a modified non-dominated sorting genetic algorithm (MNSGA-II) and grey relational analysis. First, a parametric model was established and used to verify the structure’s validity through static physical testing. Eight design variables for the S-beam container structure were also defined according to the parametric model technology. Second, MNSGA-II was used for the multi-objective lightweight optimization design of an S-beam container to obtain the optimal combination of design parameters that are considerably affected by weight reduction under peak bending stress and peak loading deflection as well as first-order natural frequency variations within the allowable range. A set of non-dominated solutions was used to obtain a multi-objective optimization design. Finally, grey relational analysis and grey entropy theory are applied to rank all solutions and determine the best compromise solution. The comparison of the technique for the order of preference by similarity to ideal solution method with grey relational analysis demonstrates the extraordinary reliability and superiority of the latter. In addition, the combined method can achieve a weight reduction of up to 23.54%, which can enhance the utilization of materials and demonstrates the superiority of the combined method relative to the initial model.

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