Effects of Boundary Conditions and Inflation Pressure on the Natural Frequencies and 3D Mode Shapes of a Tire

Patil, Kiran Kumari; Baqersad, Javad; Bastiaan, Jennifer

doi:10.4271/2017-01-1905

Cited by 7 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the most practical methods for the physical validation of tire FEA models involve comparisons of dynamic stiffness and deformation behavior. For example, Patil et al [21] performed a physical modal analysis of a tire to investigate the effect of inflation pressures on tire natural frequencies and mode shapes. This study found that tire natural frequencies are significantly influenced by inflation pressures, with an approximately linear relationship between inflation pressure and natural frequency.…”

Section: Review Of Published Workmentioning

confidence: 99%

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

Bastiaan,

Chawan,

Eum

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle’s effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing.

show abstract

Section: Review Of Published Workmentioning

confidence: 99%

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

Bastiaan,

Chawan,

Eum

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…These sensors are inexpensive and have high sensitivity [2]. They have been used to perform impact testing and to capture the mode shapes of tires in different conditions [3]. However, accelerometers can only provide information at their discrete measurement locations, and they may induce mass loading effects.…”

mentioning

confidence: 99%

An Optical-Based Technique to Obtain Vibration Characteristics of Rotating Tires

Mange¹,

Atkinson²,

Bastiaan³

et al. 2019

SAE Int. J. Veh. Dyn., Stab., and NVH

Self Cite

View full text Add to dashboard Cite

The dynamic characteristics of tires are critical in the overall vibrations of vehicles because the tireroad interface is the only medium of energy transfer between the vehicle and the road surface. Obtaining the natural frequencies and mode shapes of the tire helps in improving the comfort of the passengers. The vibrational characteristics of structures are usually obtained by performing conventional impact hammer modal testing, in which the structure is excited with an impact hammer and the response of the structure under excitation is captured using accelerometers. However, this approach only provides the response of the structure at a few discrete locations, and it is challenging to use this procedure for rotating structures. Digital Image Correlation (DIC) helps in overcoming these challenges by providing the full-field response of the structure. Although there have been many experiments on tires, there are few published papers that investigate the full-field dynamics of rotating tires at high rotating speeds. In the current work, the Kettering University Formula SAE (FSAE) vehicle is loaded on a chassis dynamometer for the purpose of performing a tire experiment. A pair of high-speed cameras capture high-resolution images to obtain the response of the tire sidewall in stationary and rotating conditions. The modal characteristics of the tire are obtained by processing these images. The results reveal the resonant frequencies and the operational deflection shapes of the loaded and unloaded tire in stationary and rotating conditions. The current article provides full-field information about the dynamics of tires at high rotating speeds for engineers and scientists in the field.

show abstract

Tire mode shape categorization using Zernike annular moment and machine learning classification

Parthasarathy,

Seo,

Kapania

2024

Sci Rep

View full text Add to dashboard Cite

This research proposes a framework for categorizing the radial tire mode shapes using machine learning (ML) based classification and feature recognition algorithms, advancing the development of a digital twin for tire performance analysis. Tire mode shape categorization is required to identify modal features in a specific frequency range to maximize driving performance and secure safety. However, the mode categorization work requires a lot of manual effort to interpret modes. Therefore, this study suggests an ML-based classification tool to replace the conventional categorization process with two primary objectives: (1) create a database by categorizing the tire mode shapes based on the identified features and (2) develop an ML-based surrogate model to classify the tire mode shapes without manual effort. The feature map of the tire mode shape is built with the Zernike annular moment descriptor (ZAMD). The mode shapes are categorized using the correlation value derived by the modal assurance criteria (MAC) with all ZAMD values for each tire mode shape and subsequently creating the appropriate labels. The decision tree, random forests, and XGBoost, the representative supervised-learning algorithms for classification, are implemented for surrogate model development. The best-performed classifier can categorize the mode shapes without any manual effort with a high accuracy of 99.5%.

show abstract

Effects of Boundary Conditions and Inflation Pressure on the Natural Frequencies and 3D Mode Shapes of a Tire

Cited by 7 publications

References 13 publications

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

An Optical-Based Technique to Obtain Vibration Characteristics of Rotating Tires

Tire mode shape categorization using Zernike annular moment and machine learning classification

Contact Info

Product

Resources

About