A Systematic Optimization Design Method for Thermal Management of Passenger Vehicles

2021 ◽  
pp. 1-27
Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Thermal Management ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Optimal Solution ◽  
Optimization Method ◽  
Drive Shaft ◽  
Management Problem ◽  
Systematic Optimization

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

2017 ◽  
Vol 21 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Lu Chen ◽  
◽  
Bin Xin ◽  
Jie Chen ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Normal Vector ◽  
Pareto Optimal ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tangent Hyperplane ◽  
Conflicting Objectives

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Applied Technology in Optimization Design of Pile-Anchor Support for Foundation Pit Based on BP Neural Network

2014 ◽  
Vol 910 ◽  
pp. 419-424
Author(s):  
Dong Wei Cao ◽  
Lu De Zou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimization Method ◽  
Engineering Optimization ◽  
Constraint Condition ◽  
Foundation Pit ◽  
Random Samples ◽  
Applied Technology

A new optimization method of pile-anchor support for foundation pit based on BP neural network was been proposed and applied in engineering example. Uniform test can be used to construct study samples efficiently. BP neural network is taken advantage to build a prediction model and predicting results of large number of random samples. Then, according to the constraint condition of optimization criterions, the best optimization result screened out from results. Through an engineering optimization example, it is showed that this method is efficient and with good economic and practical value.

Multidisciplinary Object Compatibility Design Optimization Based on Simulated Annealing Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 2515-2519
Author(s):  
Bi Qiang Yu ◽  
Xiao Qun Wang ◽  
Lin Hao Wang
Keyword(s):  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Optimization Method ◽  
System Level ◽  
Program Termination ◽  
Annealing Algorithm ◽  
Basic Ideas

In studying Multidisciplinary Object Compatibility Design Optimization method for non-hierarchic system, Simulated Annealing algorithm is introduced to establish system level model , and the basic ideas and working principle is given. In the optimization of system level, the coupling relationship between different subsystems is improved by state accepting function which is embedded in constraint. In this way, abnormal program termination and premature convergence will be avoided and ideal global optimal solution will be achieved effectually. Then the method is proved by used in the optimization design of pendulous micromechanical accelerometer

An Improved Neutrosophic Number Optimization Method for Optimal Design of Truss Structures

2018 ◽  
Vol 14 (03) ◽  
pp. 295-305 ◽  
Author(s):  
Jun Ye
Keyword(s):  
Nonlinear Optimization ◽  
Structural Design ◽  
Optimization Design ◽  
Minimum Weight ◽  
Optimal Solution ◽  
Optimization Method ◽  
Truss Structures ◽  
Optimization Models ◽  
Solution Methods ◽  
Neutrosophic Number

To overcome the complex calculation and difficult solution problems in existing solution methods of neutrosophic number (NN) optimization models, this paper proposes an improved NN optimization method to solve NN optimization models by use of the Matlab built-in function “fmincon()” corresponding to the indeterminacy [Formula: see text] and the indeterminate scale [Formula: see text]. Next, the proposed NN optimization method is applied to a three-bar planar truss structural design with indeterminate information to achieve the minimum weight objective under stress and deflection constraints as a NN nonlinear optimization design example. The optimal solutions of the truss structural design demonstrate the feasibility and flexibility of the proposed NN nonlinear optimization method under indeterminate environment. Finally, by taking some specified indeterminate scale we can also obtain a suitable optimal solution to satisfy some specified actual requirement under indeterminate environments.

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.

Soil Nailing Optimization Design Based on Improved Response Surface

2013 ◽  
Vol 671-674 ◽  
pp. 126-132
Author(s):  
Qiu Wang ◽  
Zhi Gang Song ◽  
Qing Xu
Keyword(s):  
Response Surface ◽  
Optimization Design ◽  
Uniform Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
Soil Nailing ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Computationally Intensive

Gradient algorithm is difficult to obtain explicit analytic function of the optimization model, at the same time heuristic algorithm is computationally intensive with low speed and less efficient in soil nailing optimization. To overcome these problems, a new optimization method based on improved response surface (IRS) which constructed by uniform design (UD) and non-parametric regression (NR), is proposed. The soil nailing optimization is adopted by the combination of explicit analytic model based on IRS and composing program. The optimization process is explained and a soil nailing is optimized to verify the feasibility of the proposed method. The optimum results show that the introduction of UD and NR to construct the IRS calculate fast, do not need solving the specific analytic solution and can obtain global optimal solution.

scholarly journals Optimal solution of the fractional order breast cancer competition model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Hassani ◽  
J. A. Tenreiro Machado ◽  
Z. Avazzadeh ◽  
E. Safari ◽  
S. Mehrabi
Keyword(s):  
Breast Cancer ◽  
Fractional Order ◽  
Legendre Polynomials ◽  
Numerical Experiments ◽  
Optimal Solution ◽  
Optimization Method ◽  
Competition Model ◽  
Basis Functions ◽  
Cell Populations ◽  
Operational Matrices

AbstractIn this article, a fractional order breast cancer competition model (F-BCCM) under the Caputo fractional derivative is analyzed. A new set of basis functions, namely the generalized shifted Legendre polynomials, is proposed to deal with the solutions of F-BCCM. The F-BCCM describes the dynamics involving a variety of cancer factors, such as the stem, tumor and healthy cells, as well as the effects of excess estrogen and the body’s natural immune response on the cell populations. After combining the operational matrices with the Lagrange multipliers technique we obtain an optimization method for solving the F-BCCM whose convergence is investigated. Several examples show that a few number of basis functions lead to the satisfactory results. In fact, numerical experiments not only confirm the accuracy but also the practicability and computational efficiency of the devised technique.

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