scholarly journals Response Surface Modelling of Electrosprayed Polyacrylonitrile Nanoparticle Size

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Sanaz Khademolqorani ◽  
Ali Zeinal Hamadani ◽  
Hossein Tavanai
Keyword(s):  
Response Surface ◽  
Fine Particles ◽  
Solution Concentration ◽  
Nanoparticle Size ◽  
Surface Model ◽  
Syringe Needle ◽  
Liquid Atomization ◽  
Response Surface Modelling ◽  
Needle Diameter ◽  
Electrohydrodynamic Spraying

Electrospraying (electrohydrodynamic spraying) is a method of liquid atomization by electrical forces. Spraying solutions or suspensions allow production of fine particles, down to nanometer size. These particles are interesting for a wide variety of applications, thanks to their unprecedented chemical and physical behaviour in comparison to their bulk form. Knowledge of the particle size in powders is important in many studies employing nanoparticles. In this paper, the effect of some process parameters on the size of electrosprayed polyacrylonitrile particles is presented in the form of response surface model. The model is achieved by employing a factorial design to evaluate the influence of parameters on the polyacrylonitrile nanoparticle size and response surface methodology. Four electrospraying parameters, namely, applied voltage, electrospraying solution concentration, flow rate, and syringe needle diameter were considered.

scholarly journals Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance

2021 ◽  
Vol 11 (12) ◽  
pp. 5445
Author(s):  
Shengyong Gan ◽  
Xingbo Fang ◽  
Xiaohui Wei
Keyword(s):  
Response Surface ◽  
Energy Absorption ◽  
Landing Gear ◽  
Surface Model ◽  
Absorption Properties ◽  
Surface Method ◽  
Design Variables ◽  
Landing Impact ◽  
Landing Response ◽  
Parametric Studies

The aim of this paper is to obtain the strut friction–touchdown performance relation for designing the parameters involving the strut friction of the landing gear in a light aircraft. The numerical model of the landing gear is validated by drop test of single half-axle landing gear, which is used to obtain the energy absorption properties of strut friction in the landing process. Parametric studies are conducted using the response surface method. Based on the design of the experiment results and response surface functions, the sensitivity analysis of the design variables is implemented. Furthermore, a multi-objective optimization is carried out for good touchdown performance. The results show that the proportion of energy absorption of friction load accounts for more than 35% of the total landing impact energy. The response surface model characterizes well for the landing response, with a minimum fitting accuracy of 99.52%. The most sensitive variables for the four landing responses are the lower bearing width and the wheel moment of inertia. Moreover, the max overloading of sprung mass in LC-1 decreases by 4.84% after design optimization, which illustrates that the method of analysis and optimization on the strut friction of landing gear is efficient for improving the aircraft touchdown performance.

An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Lei Shi ◽  
Ren-Jye Yang ◽  
Ping Zhu
Keyword(s):  
Response Surface ◽  
Bias Correction ◽  
Adaptive Response ◽  
Large Scale ◽  
Correction Method ◽  
Data Uncertainty ◽  
Correction Function ◽  
Surface Model ◽  
Model Bias ◽  
Adaptive Response Surface Method

The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.

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