scholarly journals Fabrication, Characterization and Response Surface Method (RSM) Optimization for Tetracycline Photodegration by Bi3.84W0.16O6.24- graphene oxide (BWO-GO)

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Chengjie Song ◽  
Xinying Li ◽  
Liping Wang ◽  
Weidong Shi
Keyword(s):  
Graphene Oxide ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Method ◽  
Rsm Optimization

scholarly journals Design Optimization of Rubber-Basalt Fiber- Modified Concrete Mix Ratios Based on a Response Surface Method

2020 ◽  
Vol 10 (19) ◽  
pp. 6753
Author(s):  
Yafeng Gong ◽  
Jiaxiang Song ◽  
Siyuan Lin ◽  
Jianxing Yang ◽  
Yang He ◽  
...  
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Basalt Fiber ◽  
Fiber Content ◽  
Quadratic Term ◽  
Rubber Content ◽  
Surface Method ◽  
Rsm Optimization ◽  
Binder Ratio ◽  
Water Binder Ratio

Rubber aggregates produced from waste rubber materials and environmentally friendly basalt fibers are excellent concrete modification materials, which significantly improve the working performance and mechanical properties of concrete. This paper studied the influences of water-binder ratio, basalt fiber content and rubber content on the properties of rubber-basalt fiber modified concrete (RBFC). Based on the response surface method (RSM), optimization schemes of three preparation parameters were designed. The results showed that all preparation parameters have significant impacts on the slump. The rubber content has a closer relationship with the compressive strength and the quadratic term of the basalt fiber content has a significant impact on the flexural strength. According to the analysis, the optimal mix ratio which possesses reliable accuracy compared with experimental results includes a water-binder ratio of 0.39, a basalt fiber content of 4.56 kg/m3 and a rubber content of 10%,

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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