Analysis on Stiffness Reliability for Bus Body Frame Based on Response Surface Method

2011 ◽  
Vol 48-49 ◽  
pp. 228-231
Author(s):  
Da Qian Zhang ◽  
Wei Tao Zhao ◽  
Lian Fang Qin
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Body Frame ◽  
Index Method ◽  
Central Index ◽  
Reliability Calculation ◽  
Surface Method ◽  
Interpolation Technique ◽  
Calculation Results ◽  
Bus Body

This paper describes how to calculate stiffness reliability for bus body frame in the specified case. The principle of the Response Surface Method and solving process of reliability calculation are presented. Designing response surface by central index method and solving undetermined coefficients of unknown function by interpolation technique, the fitted response surface placed on check point is founded. Then failure probability of bus frame is got by Monto Carlo method and corresponding reliability is obtained. The calculation results of a specified bus body show that stiffness reliability of frame is 100% and it is necessary that the structure should be improved because of strong stiffness.

scholarly journals Influencing Factors’ Analysis of Disturbance Degree of Key Stratum Based on the Response Surface Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Li ◽  
Nan Wang ◽  
Yuqi Ren ◽  
Xiangji Ou ◽  
Yikun Liu ◽  
...  
Keyword(s):  
Coal Seam ◽  
Response Surface ◽  
Response Surface Method ◽  
Interlayer Spacing ◽  
Burial Depth ◽  
Key Stratum ◽  
Surface Method ◽  
Calculation Results ◽  
Seam Mining ◽  
Mining Height

The key stratum controls the activities of the overlying strata or the whole strata up to the surface, which is one of the important research objects in the coal seam mining. Based on the analysis of several geological factors affecting on the key stratum, the definition of “disturbance degree of key stratum” (KSDD) was proposed. And, the KSDD is quantified by the value among 0 to 10. Through the response surface method, experiments of three factors (mining height, buried depth, and interlayer spacing) with three different lithology types (soft, medium, and hard) between key stratum and coal seam are signed. And, the KSDD of each scheme is calculated by the developed calculation system. The response surface regression models of KSDD with three lithology types are established. And, the single influence and interactive influences of the three factors on the KSDD with different lithology types are studied. The results show that the following. (1) Mining height and buried depth are positively correlated with the value of KSDD, and the interlayer spacing is negatively correlated with KSDD. However, when the value of interlayer spacing exceeds 30 m, the change of the KSDD tends to be gentle. (2) The value of KSDD is not only affected by a single factor but also affected by the interaction of various factors. With the increase of burial depth, the decrease of interlayer spacing and the impact of mining height on key stratum are more severe. (3) The influence order of each factor on KSDD is as follows: the interlayer spacing > mining height > buried depth. (4) Although the three factors interact with each other, the three factors decrease with the increase of the lithology proportional coefficient. According to the above research results, based on the calculation results of KSDD on five mines, the variation laws of KSDD with actual situation are analysed. And, the calculation results further verify the above experimental rules, which provide a certain reference and theoretical basis for the design of backfilling parameters and the management of the roof.

An Improvement of the Response Surface Method Based on Successive Linear Interpolation

2012 ◽  
Vol 532-533 ◽  
pp. 724-727
Author(s):  
Wei Tao Zhao ◽  
Tian Jun Yu ◽  
Yi Yang
Keyword(s):  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Linear Interpolation ◽  
Computational Effort ◽  
Computational Burden ◽  
Surface Method ◽  
Interpolation Technique ◽  
The Common

The response surface method (RSM) is widely used to alleviate the computational burden of engineering analyses. For reliability analysis, the common approach in the RSM is to use a linear interpolation technique. However, the experimental points are all arranged using the classical RSM in each process of interpolation. Therefore, the number of experimental points is large that resulting in the efficiency is lower. In this study, an improvement of the RSM for structural reliability analysis has been proposed based on the technique of successive linear interpolation. As seen from the example, the proposed method yields better results than those of the classical RSM, and the number of experimental points using the proposed method is less than that of classical RSM. It seems that the proposed method improves the convergence speed and reduces the computational effort.

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

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