Sensitivity Analysis of Airdrop Condition Parameters Based on Response Surface Method

2014 ◽  
Vol 565 ◽  
pp. 92-97 ◽  
Author(s):  
Jian Yang Li ◽  
Hong Yan Wang ◽  
Qiang Rui ◽  
Huang Jie Hong
Keyword(s):  
Sensitivity Analysis ◽  
Finite Element ◽  
Response Surface ◽  
Response Surface Method ◽  
Scientific Explanation ◽  
New Technology ◽  
Design And Optimization ◽  
Surface Method ◽  
Airborne Vehicle ◽  
Important Significance

The airborne vehicle would suffer from impact at landing. The magnitude of impact and stability of airborne vehicle are constraint parameters of successful landing. There was a lack of scientific explanation on the sensitivity of landing condition parameters. For overcoming the deficiency of classical sensitivity analysis, this paper describes the application of new technology for the sensitivity analysis. Based on the Finite Element and Response Surface method, the research on sensitivity analysis of landing condition parameters was proposed. The results have important significance in the design and optimization of airborne vehicle and airbags system. It can be also provide guidance for airdrop operation.

scholarly journals Sensitivity Analysis of Reliability of Low-Mobility Parallel Mechanisms Based on a Response Surface Method

2021 ◽  
Vol 11 (19) ◽  
pp. 9002
Author(s):  
Qiang Yang ◽  
Hongkun Ma ◽  
Jiaocheng Ma ◽  
Zhili Sun ◽  
Cuiling Li
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Inverse Kinematics ◽  
Parallel Mechanism ◽  
Error Component ◽  
Parallel Mechanisms ◽  
Kinematic Accuracy ◽  
Surface Method ◽  
Input Error

Kinematic accuracy is a crucial indicator for evaluating the performance of mechanisms. Low-mobility parallel mechanisms are examples of parallel robots that have been successfully employed in many industrial fields. Previous studies analyzing the kinematic accuracy analysis of parallel mechanisms typically ignore the randomness of each component of input error, leading to imprecise conclusions. In this paper, we use homogeneous transforms to develop the inverse kinematics models of an improved Delta parallel mechanism. Based on the inverse kinematics and the first-order Taylor approximation, a model is presented considering errors from the kinematic parameters describing the mechanism’s geometry, clearance errors associated with revolute joints and driving errors associated with actuators. The response surface method is employed to build an explicit limit state function for describing position errors of the end-effector in the combined direction. As a result, a mathematical model of kinematic reliability of the improved Delta mechanism is derived considering the randomness of every input error component. And then, reliability sensitivity of the improved Delta parallel mechanism is analyzed, and the influences of the randomness of each input error component on the kinematic reliability of the mechanism are quantitatively calculated. The kinematic reliability and proposed sensitivity analysis provide a theoretical reference for the synthesis and optimum design of parallel mechanisms for kinematic accuracy.

Design and Optimization of a Linear Piezoelectric Motor using Response Surface Method

2021 ◽  
Vol 56 (5) ◽  
pp. 873-884
Author(s):  
Adel Zemirline ◽  
Abdellah Abdellah El Hadj ◽  
Shayfull Z. B. Abd Rahim ◽  
Mohammed Ouali
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Piezoelectric Motor ◽  
Design And Optimization ◽  
Surface Method

Forecasting and Optimization of the Viscosity of Nano-oil Containing Zinc Oxide Nanoparticles Using the Response Surface Method and Sensitivity Analysis

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Yuanzhou Zheng ◽  
Shuaiqi Wang ◽  
Annunziata D’Orazio ◽  
Arash Karimipour ◽  
Masoud Afrand
Keyword(s):  
Sensitivity Analysis ◽  
Zinc Oxide ◽  
Response Surface ◽  
Response Surface Method ◽  
Zinc Oxide Nanoparticles ◽  
Volume Fraction ◽  
Good Prediction ◽  
Surface Method ◽  
Nano Lubricant ◽  
New Generation

Abstract In the current paper, the behavior of zinc oxide/SAE50 nano lubricant as a part of the new generation of coolants and lubricants is examined using response surface method (RSM). The data used in this study were viscosity at dissimilar volume concentrations (0–1.5%) and temperatures (5–50 °C) for dissimilar shear rate values. Therefore, sensitivity analysis based on variation of nanoparticle (NP) concentration and temperature was also implemented. The findings revealed that enhancing the volume fraction (φ) exacerbates the viscosity sensitivity to temperature. Given the noteworthy deviance between the experimental viscosity and the data forecasted by existing classical viscosity correlations, a novel regression model is gained. R2 and adj-R2 for this model were calculated as 0.9966 and 0.9965, respectively, which represent a very good prediction with a standard deviation of 3%.

Structural Optimization of a Machining Center Column Based on Response Surface Method

2012 ◽  
Vol 522 ◽  
pp. 663-667
Author(s):  
Ming Nan Sun ◽  
Guo Fu Yin ◽  
Teng Hu
Keyword(s):  
Finite Element ◽  
Structural Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Model ◽  
Element Analysis ◽  
Mean Frequency ◽  
Machining Center ◽  
Surface Method ◽  
Design Variables

In order to improve dynamic characteristics of a machining center column, this paper proposes a structural optimization method based on finite element method (FEM) and response surface method (RSM). In order to reduce number of design variables, the finite element analysis samples in design space are selected by using the central composite design (CCD) experiment method. On the basis of FEM results at these experiment samples, quadratic polynomials are employed to establish response surface model, which reflects the relationship between the response (mean frequency of the first four orders) and the design variables (the column structural sizes). The goal of getting maximum mean frequency is reached by using NLPQL algorithm in iSIGHT. Through the optimization, the mean frequency is increased by 8.12%.

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