Experimental rig to estimate the coefficient of friction between tire and surface in airplane touchdown simulations

Li, Chengwei; Zhan, Liwei

doi:10.1063/1.4927681

Cited by 3 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To simulate aircraft landing and measure the friction coeffi cient between the aircraft tire and coating, a vertical drop test system was constructed [8]. This system included several sys tems, namely, drop, impact platform, fixture, frame, and tire turning, as shown in figure 1.…”

Section: Measurement Systemmentioning

confidence: 99%

“…Where n is the overall number of decomposed IMF. The friction coefficient is estimated under conditions of different drop heights and tire rotational speeds by [8], and the expression is the following:…”

Section: Measured Friction Coefficientmentioning

confidence: 99%

“…The friction coefficient is influenced by many factors when the airplane lands at the airport. In our study, a new exper imental rig was designed to simulate the status of airplane landing, and to measure the friction coefficient between the aircraft tire and coating [8]. This device mainly focuses on the research for aircraft tire.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

Zhan¹,

Li²

2016

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant , the parameter gamma corresponding to RBF kernel and the epsilon parameter in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (, and ), respectively. The regression accuracy could be reflected by the coefficient of determination (). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

show abstract

Section: Measurement Systemmentioning

confidence: 99%

Section: Measured Friction Coefficientmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

Zhan¹,

Li²

2016

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

An enhanced extrapolation method based on particle swarm optimization-support vector regression to determine the friction coefficient between aircraft tire and runway surface

Zhan

Song³

et al. 2019

Review of Scientific Instruments

View full text Add to dashboard Cite

This paper proposes a hybrid pretreating method in the support vector regression (SVR) machines to enhance the extrapolated performance of the traditional method based on particle swarm optimization (PSO)-SVR. This method is introduced into a novel domain wherein the friction coefficient (COF) is extrapolated between the aircraft tire and the runway surface. It includes two parts: the normalized data and the extrapolated method for COF. The first part develops a novel data normalized method, which allows the experiment data to follow sine distribution (SN) instead of traditional linear distribution. For the second part, the SVR with the training set initially extrapolates a COF, and the COF is subsequently added into the original training set to build a new training sample. This process is repeated until the extrapolation is finished. This method is named step-wise (SW) extrapolation. PSO is used to optimize the regularization constant C, the parameter gamma γ, and epsilon parameter ε in SVR. Finally, this study indicates that the proposed method (SN-SW-PSO-SVR) is more suitable for the extrapolation of the COF between the airplane tire and the runway surface than other extrapolated methods.

show abstract

Instrument for in situ study of rolling under normal load and torque

Rajchel

Varenberg

Leamy

et al. 2022

Review of Scientific Instruments

View full text Add to dashboard Cite

Instabilities that develop at the contact interface of solid rollers or airless tires while in motion can lead to increased energy losses and reduced service life. This manuscript describes an instrument that can give better insight into the origin of such instabilities by monitoring both local and global roller mechanics. This is done by simultaneously obtaining force and displacement data from sensors as well as optical measurements and local deformation fields across two different planes, extracted from images taken by a high-speed camera. Multiple loading configurations are possible, ranging from static normal loading of the roller to free rolling and rolling with a propulsive or a braking torque. Instrument functions, elements, and design are presented in detail and its capabilities are demonstrated by obtaining measurements such as width of the contact interface under normal loading, strain fields of the roller sidewall and contact interface under normal loading, and the roller’s resistance to motion for free and forced rolling.

show abstract

Experimental rig to estimate the coefficient of friction between tire and surface in airplane touchdown simulations

Cited by 3 publications

References 17 publications

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

An enhanced extrapolation method based on particle swarm optimization-support vector regression to determine the friction coefficient between aircraft tire and runway surface

Instrument for in situ study of rolling under normal load and torque

Contact Info

Product

Resources

About