scholarly journals Reliability optimization design method based on multi-level surrogate model

2020 ◽  
Vol 22 (4) ◽  
pp. 638-650
Author(s):  
Yong-Hua Li ◽  
Xiao-Jia Liang ◽  
Si-Hu Dong
Keyword(s):  
Surrogate Model ◽  
Optimization Design ◽  
Design Method ◽  
Reliability Optimization ◽  
Multi Level

Fuzzy Reliability Optimization Design Method of Flange Based on the Tightness of Bolted Flanged Connections

2010 ◽  
Vol 37-38 ◽  
pp. 501-504
Author(s):  
Zhen Guo Sun ◽  
Bo Qin Gu ◽  
Xing Lu Huang
Keyword(s):  
Probability Theory ◽  
Optimization Design ◽  
Design Method ◽  
Design Parameters ◽  
Reliability Optimization ◽  
Fuzzy Reliability ◽  
Leakage Model ◽  
Sealing Performance ◽  
Random Probability ◽  
Fuzzy Random

Based on the time-correlated leakage model and fuzzy random probability theory, a formula for calculating fuzzy random reliability of sealing performance of bolted flanged connections was derived. The optimization variables were defined according to the sensitivity analysis of design parameters on fuzzy reliability of sealing performance. A fuzzy reliability optimization design method of flange was investigated, in which both the tightness of bolted flanged connections and the strength of flange were taken into consideration. As an example, an integral welding necked pipe flange was designed according to the optimization design method proposed in this paper. The designed flange not only satisfies the requirements of flange strength and sealing reliability of connections, but also its weight is reduced by 16% compared with that prescribed in the standard.

Reliability optimization design of connecting rod of locomotive traction equipment

2014 ◽  
Vol 229 (9) ◽  
pp. 1623-1629 ◽  
Author(s):  
Changyou Li ◽  
Changshuai Qiao ◽  
Yimin Zhang ◽  
Song Guo
Keyword(s):  
Cross Sections ◽  
Optimization Design ◽  
Design Method ◽  
High Reliability ◽  
Reliability Optimization ◽  
Railway Transportation ◽  
Connecting Rod ◽  
Reliability Sensitivity ◽  
Traction Equipment

High reliability of a locomotive is important for the railway transportation. This high reliability was guaranteed by use of a large safety factor resulting in cost increase of railway transportation generally. To overcome this high cost without sacrificing reliability, this work focuses on an optimization design method for the design of the connecting rod used in locomotive traction equipment. The reliability model is formulated based on the stress–intensity distribution interference theory and the reliability of the original connecting rod was estimated using advanced first order and second moment method. Then, the reliability–sensitivity is analyzed. The results show that the reliability of the connecting rod used in China is almost equal to one and the reliability robustness is high. To minimize the quality of the connecting rod under the condition of ensuring high reliability and reliability robustness, the reliability optimization models are proposed for three cross sections. The optimization results show that the quality of the optimized connecting rod could be reduced to less than 40% of the original without sacrificing reliability and reliability robustness.

Reliability Optimization Design Based on the Blind Number Theory

2012 ◽  
Vol 217-219 ◽  
pp. 1385-1388
Author(s):  
De Xiang Zhao ◽  
Yong Cheng Ling ◽  
Ju Zhao
Keyword(s):  
Number Theory ◽  
Optimization Design ◽  
Design Method ◽  
Design Model ◽  
Reliability Optimization ◽  
Reliability Design ◽  
Operation Rules ◽  
Blind Number

The concept and operation rules of the blind number based on Unascertained Theory are introduced. This case study demonstrates the advantage of unascertained theory described in reliability optimization, and a stress-strength reliability optimization design model is built up at same time. In addition, the corresponding practical optimization calculating program is given. Test on the validity and practicability of this model is also verified by comparing the result of reliability gained from this model with that obtained from the traditional reliability-design method.

Reliability Optimization Design of Spacecraft Valve Spring

2012 ◽  
Vol 538-541 ◽  
pp. 851-857
Author(s):  
Ye Lin ◽  
Wei Min Cui ◽  
Bi Feng Song
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Mathematic Model ◽  
Optimization Method ◽  
Reliability Optimization ◽  
Mechanical Reliability ◽  
Reliability Design ◽  
Valve Spring ◽  
Design Requirements ◽  
Mechanical Optimization

Firstly, methodology of valve spring’s static strength and fatigue strength reliability analysis is built up, and detailed computational formulas to derive distributions of the stress and strength are described. Secondly, to make full use of the material characteristic and consider reliability of the mechanism part, taking the spring’s mass as the objective function and based on spring’s traditional and reliability constraints, mathematic model of reliability optimization design of valve spring is established using mechanical reliability design method and mechanical optimization design method. At last, the proposed method is applied to a practical spring design example by integrating optimization tools with the optimization model. The comparison of reliability optimization design results and traditional optimization design results shows that the reliability optimization method is practical and reliable; its design results can satisfy all design requirements with smaller mass.

Optimization design of centrifugal pump impeller based on multi-output Gaussian process regression

2021 ◽  
pp. 2150364
Author(s):  
Renhui Zhang ◽  
Liangde Gao ◽  
Xuebing Chen
Keyword(s):  
Centrifugal Pump ◽  
Surrogate Model ◽  
Optimization Design ◽  
Design Method ◽  
Gaussian Process Regression ◽  
Nsga Ii ◽  
Pump Impeller ◽  
Hydraulic Design ◽  
Sample Set ◽  
Centrifugal Pump Impeller

To overcome the problems of large calculation cost and high dependence on designers’ experience, an optimization design method based on multi-output Gaussian process regression (MOGPR) was proposed. The hydraulic design method of centrifugal pump based on the MOGPR model was constructed under Bayesian framework. Based on the available excellent hydraulic model, the complex relationship between the performance parameters such as head, flow rate and the geometric parameters of centrifugal pump impeller was trained. The hydraulic design of the impeller for M125-100 centrifugal pump was performed by the proposed MOGPR surrogate model design method. The initial MOGPR design was further optimized by using the proposed MOGPR and NSGA-II hybrid model. The initial sample set for NSGA-II was designed by Latin hypercube design based on the MOGPR initial design. The relationship between the impeller geometry and the CFD numerical results of the sample set was trained to construct the surrogate model for pump hydraulic performance prediction. The MOGPR surrogate model was used to evaluate the objective function value of the offspring samples in NSGA-II multi-objective optimization. The comparison of the pump hydraulic performance between the optimized designs and the initial design shows that the efficiency and the head of the tradeoff optimal design are increased by 2.5% and 2.6%, respectively. The efficiency of the optimal head constraint design is increased by 3.2%. The comparison of the inner flow field shows that turbulent kinetic energy decreases significantly and flow separation is effectively suppressed for the optimal head constraint design.

scholarly journals Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

2018 ◽  
Vol 29 (15) ◽  
pp. 3097-3107 ◽  
Author(s):  
Liheng Luo ◽  
Dianzi Liu ◽  
Meiling Zhu ◽  
Yijie Liu ◽  
Jianqiao Ye
Keyword(s):  
Optimal Design ◽  
Surrogate Model ◽  
Optimization Design ◽  
Local Level ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Human Motion ◽  
Latin Hypercube ◽  
Multi Level ◽  
Modeling Strategy

Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.

Multi-levels Kriging surrogate model-based robust aerodynamics optimization design method

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040115
Author(s):  
Neng Xiong ◽  
Yang Tao ◽  
Jun Lin ◽  
Xue-Qiang Liu
Keyword(s):  
Design Optimization ◽  
Surrogate Model ◽  
Optimization Design ◽  
Design Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Mean Value ◽  
Robust Design Optimization ◽  
Efficient Global Optimization ◽  
Kriging Surrogate Model

Robust design optimization has a great potential application in many engineering fields. In the conventional robust aerodynamics design optimization method, the main difficulty is expensive computational cost related to a large number of function evaluations for uncertainty quantification (UQ). To alleviate the expensive burden for UQ, two levels Kriging surrogate model was introduced. The first level is for the mean value and the second level is for the variances. Through the second level Kriging surrogate models, the method of Monte Carlo Simulation (MCS), which requires a huge number of function evaluations, can be effectively applied to the analysis of variance. Efficient Global Optimization algorithm (EGO) was employed to achieve the global optimized results. To validate the performance of the design method, both one-dimensional function and two-dimensional function were applied. Finally, robust aerodynamics design optimization was applied for a low-drag airfoil. The results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties to small manufacturing errors.

Research on Reliability Optimization Design of Mechanical Structure Based on Computational Intelligence

2021 ◽  
Vol 7 (5) ◽  
pp. 1304-1310
Author(s):  
Xinyu Feng ◽  
Xijing Zhu ◽  
Xiangmeng Li
Keyword(s):  
Computational Intelligence ◽  
Optimization Design ◽  
Design Method ◽  
Mechanical Equipment ◽  
Reliability Optimization ◽  
Reliability Design ◽  
Mechanical Structure ◽  
Cut Set ◽  
Final Calculation ◽  
Structure Object

In order to extend the actual service life of mechanical equipment components and realize the reasonable protection of applied structural components, the reliability optimization design of mechanical structure based on computational intelligence is studied. With the help of wavelet neural network, the established reliability and reliability index are obtained, and then the basic theory of reliability and computational intelligence technology are studied through the reliability optimization of mechanical zero structure. On this basis, the application concepts of reliability design method are defined, and the final calculation result of reliability optimization value is obtained by combining fuzzy set and level cut set, so as to realize the reliability optimization design of mechanical structure based on computational intelligence. The experimental results show that, compared with the traditional design method, the optimization design method supported by computational intelligence technology can better improve the disadvantages of mechanical structure object, and has strong practical application value in extending the reliability application time.

Research on Application of Electric Vehicle Collision Based on Reliability Optimization Design Method

2019 ◽  
Vol 16 (07) ◽  
pp. 1950034 ◽  
Author(s):  
Hequan Wu ◽  
Shijie Kuang ◽  
Haibin Hou
Keyword(s):  
Design Optimization ◽  
Electric Vehicle ◽  
Optimum Design ◽  
Optimization Design ◽  
Design Method ◽  
Optimization Methods ◽  
Side Impact ◽  
Vehicle Body ◽  
Reliability Optimization ◽  
The Impact

When mentioning multidisciplinary design optimization methods, the deterministic optimum design is frequently applied to set the constraint boundary. Furthermore, only a small amount of space tolerances (or uncertainty) is available in the process of design, manufacture and operation. Therefore, deterministic optimum design lacking uncertainty cannot meet the needs of reliability-based design optimization. In this paper, reliability optimization design method, finite element (FE) analysis, optimal Latin hypercube test design and response surface approximation model are combined to optimize the side structure of electric vehicles and improve its crashworthiness. Firstly, a side impact FE model of the electric vehicle is established and verified in this paper. Then, the dimensions and the material yield strength of the force-bearing structure in the vehicle are selected as design variables, and the impact speed in the actual collision is selected as a random variable to optimize the car crashworthiness in the side impact using the 95% reliability optimization method. The results show that the 95% reliability optimization design increases the total energy absorption of the side components by 9.45%, the intrusion of the B-pillar and the vehicle door inner panel decreased by 10.42% and 14.75%, respectively. The intrusion speed of the B-pillar and the inner panel of the vehicle door decreases by 10.35% and 17.78%, respectively. By comparing the results of traditional deterministic optimization and reliability optimization methods, the latter can better satisfy the crash safety objectives, and improve the reliability of vehicle body design.

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