Search Results (19)

This study investigates the development of a tire wear model using finite element techniques. Experimental testing was conducted using the Hoosier R25B slick tire mounted onto a Mustang Dynamometer (MD-AWD-500) in the Automotive Center of Excellence, Oshawa, Ontario, Canada. A general acceleration/deceleration procedure was performed until the battery was completely exhausted. A high-fidelity finite element tire model using Virtual Performance Solution by ESI Group, a part of Keysight Technologies, was developed, incorporating highly detailed material testing and constitutive modeling to simulate the tire’s complex mechanical behavior. In conjunction with a finite element model, Archard’s wear theory is implemented algorithmically to determine the wear and volume loss rate of the tire during its acceleration and deceleration procedures. A novel application using a modified wear theory incorporates the temperature dependence of tread hardness to measure tire wear. Experimental tests show that the tire loses 3.10 g of mass within 45 min of testing. The results from the developed finite element model for tire wear suggest a high correlation to experimental values. This study demonstrates the simulated model’s capability to predict wear patterns, ability to quantify tire degradation under dynamic loading conditions and provides valuable insights for optimizing performance and wear estimation. Full article

Asphalt pavement skid resistance, governed by surface texture, is critical for traffic safety. Most research has focused on full-depth textural characteristics, often overlooking the depth of tire–pavement contact under real traffic conditions. This study introduces the concept of the Effective Depth of Skid Resistance (EDSR) to describe the effective depth of tire–asphalt contact, improving skid resistance assessment accuracy. Using blue linear laser scanning, surface textures of three common asphalt pavements with wearing courses—AC-13, AC-16, and SMA-13—were analyzed, and friction coefficients were measured using a British pendulum. After pre-processing three-dimensional texture data, fractal dimensions at various depths were calculated using the box-counting method and correlated with the friction coefficients. Previous studies show an insignificant correlation between full-depth asphalt pavement textures and skid resistance. However, this study found a significant positive correlation between skid resistance and pavement textures at specific depths or the EDSR. A depth with a correlation exceeding 0.9 was defined as the EDSR. Linear formulas were established for each pavement type within these EDSR ranges. A theoretical model was developed for predicting skid resistance, showing an over 80% accuracy against real-world data, indicating its potential for improving road surface performance detection. Full article

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study. Full article

This study aims to evaluate the tire–road-wear particles (TRWPs) and suspended dust generated based on the nominal maximum aggregate size (NMAS) of the polymer-modified stone mastic asphalt (SMA) mixtures indoors. The SMA mixtures containing styrene butadiene styrene (SBS) polymer and the NMASs of 19, 13, 10, 8, and 6 mm were used. Dust was generated from the wear of the tires and the pavement inside the indoor chamber by using the laboratory tire–road-wear particle generation and evaluation tester (LTRWP tester) developed by Korea Expressway Corporation (KEC). In this method, a cylindrical asphalt-mixture specimen rotates in the center, and a load is applied using three tires on the sides of the test specimen. During the test, a digital sensor was used to measure the concentration for each particle size. After the test was completed, the dust was collected and weighed. According to the test results, the generated TRWP emissions were reduced by approximately 0.15 g as the NMAS of the SMA mixture decreased by 1 mm. TRWP emissions decreased by 20% when using the 6 mm SMA mixture compared to the 13 mm SMA mixture. For practical application, a predicted equation of TRWP emissions estimation was developed by using the concentration of suspended dust measured by the digital sensor in the LTRWP tester. LTRWP can be used as an indoor test method to evaluate pavement and tire materials to reduce the amount of dust generated from tire and pavement wear. Full article

A non-pneumatic tire (NPT) overcomes the shortcomings of a traditional pneumatic tire such as wear, punctures and blowouts. In this respect, it shows great potential in improving driving safety, and has received great attention in recent years. In this paper, a carbon fiber-reinforced polyethylene terephthalate (PET/CF) honeycomb is proposed as a support structure for NPTs, which can be easily prepared using 3D printing technology. The experimental results showed that the PET/CF has high strength and modulus and provides excellent mechanical properties. Then, a finite element (FE) model was established to predict the compression performance of auxetic honeycombs. Good agreement was achieved between the experimental data and FE analysis. The influence of the cell parameters on the compressive performance of the support structure were further analyzed. Both the wall thickness and the vertically inclined angle could modulate the mechanical performance of the NPT. Finally, the application of vertical force is used to analyze the static load of the structure. The PET/CF honeycomb as the support structure of the NPT showed outstanding bearing capacity and stiffness in contrast with elastomer counterparts. Consequently, this study broadens the material selection for NPTs and proposes a strategy for manufacturing a prototype, which provides a reference for the design and development of non-pneumatic tires. Full article

Plastic debris is a growing threat in freshwater ecosystems and transport models predict that many plastics will sink to the benthos. Among the most common plastics found in the Laurentian Great Lakes sediments are polyethylene terephthalate (especially fibers; PET), polyvinylchloride (particles; PVC), and styrene-butadiene rubber resulting from tire wear (“crumb rubber”; SBR). These materials vary substantially in physical and chemical properties, and their impacts on benthic biogeochemistry and microbial community structure and function are largely unknown. We used a microcosm approach to evaluate the impact of these three plastics on benthic-pelagic coupling, sediment properties, and sediment microbial community structure and function using sediments from Irondequoit Bay, a major embayment of Lake Ontario in Rochester, New York, USA. Benthic metabolism and nitrogen and phosphorous cycling were all uniquely impacted by the different polymers. PET fibers and PVC particles demonstrated the most unique effects, with decreased ecosystem metabolism in sediments containing PET and greater nutrient uptake in sediments with PVC. Microbial diversity was reduced in all treatments containing plastic, but SBR had the most substantial impact on microbial community function, increasing the relative importance of metabolic pathways such as hydrocarbon degradation and sulfur metabolism. Our results suggest that individual polymers have unique impacts on the benthos, with divergent implications for ecosystem function. This provides deeper insight into the myriad ways plastic pollution may impact aquatic ecosystems and will help to inform risk assessment and policy interventions by highlighting which materials pose the greatest risk. Full article

The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate. Full article

One of the main challenges in maximizing vehicle performance is to predict and optimize tire behavior in different working conditions, such as temperature, friction, and wear. Starting from several approaches to develop tire grip and wear models, based on physical principles, experimental data, or statistical methods available in the literature, this work aims to propose a novel tire wear model that combines physical and statistical analysis on a large number of high-performance vehicle telemetries, tracks, and road data, as well as tires’ viscoelastic properties. Another contribution of this multidisciplinary study is the definition of the functional relationships that govern the tire–road interaction in terms of friction and degradation, conducting a thorough analysis of the car’s telemetry, the track and asphalt features, and the viscoelastic properties of the tires. Full article

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization. Full article

Excessive tire wear can affect vehicle driving safety. While there are various methods for predicting the tire wear amount in real-time, it is unclear which method is the most effective in terms of the difficulty of sensing and prediction accuracy. The current study aims to develop prediction algorithms of tire wear and compare their performances. A finite element tire model was developed and validated against experimental data. Parametric tire rolling simulations were conducted using various driving and tire wear conditions to obtain tire internal accelerations. Machine-learning-based algorithms for tire wear prediction utilizing various sensing options were developed, and their performances were compared. A wheel translational and rotational speed-based (V and ω) method resulted in an average prediction error of 1.2 mm. Utilizing the internal pressure and vertical load of the tire with the V and ω improved the prediction accuracy to 0.34 mm. Acceleration-based methods resulted in an average prediction error of 0.6 mm. An algorithm using both the vehicle and tire information showed the best performance with a prediction error of 0.21 mm. When accounting for sensing cost, the V and ω-based method seems to be promising option. This finding needs to be experimentally verified. Full article

As a result of rising environmental awareness, vehicle-related emissions such as particulate matter are subject to increasing criticism. The air pollution in urban areas is especially linked to health risks. The connection between vehicle-related particle emissions and ambient air quality is highly complex. Therefore, a methodology is presented to evaluate the influence of different vehicle-related sources such as exhaust particles, brake wear and tire and road wear particles (TRWP) on ambient particulate matter (PM). In a first step, particle measurements were conducted based on field trials with an instrumented vehicle to determine the main influence parameters for each emission source. Afterwards, a simplified approach for a qualitative prediction of vehicle-related particle emissions is derived. In a next step, a virtual inner-city scenario is set up. This includes a vehicle simulation environment for predicting the local emission hot spots as well as a computational fluid dynamics model (CFD) to account for particle dispersion in the environment. This methodology allows for the investigation of emissions pathways from the point of generation up to the point of their emission potential. Full article

Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R² = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear. Full article

The real-time monitoring of the vertical force of the tire is the basis for ensuring the driving safety, handling stability, fuel economy and the riding comfort of the vehicle. An algorithm for the vertical force of the tire estimation based on the combination of intelligent tire technology and neural network theory is herein proposed. Firstly, the finite element model of the 205/55/R16 radial tire was established by ABAQUS and the validity of the finite element model was verified through static experiment and dynamic experiment. Secondly, the effects of inflation pressure, speed, load and tread wear on the tire contact patch length and the radial displacement at the virtual acceleration sensor were analyzed based on the finite element analysis method and control variable method. Finally, three kinds of the vertical force prediction algorithms based on the GA-BP neural network algorithm were established, and the network performance of each prediction model was tested. The results show that the vertical force prediction model with inflation pressure and the peak value of radial displacement as the characteristic input parameters has the highest prediction accuracy and the shortest calculation time. At the same time, the mathematical formula of the Model 3 was built. Full article

Tribological properties of tread rubber is a key problem for the safety and durability of large aircraft tires. So, new molecular models of carbon nanotube (CNT) reinforced vulcanized natural rubber (VNR) composites have been developed to study the enhanced tribological properties and reveal the reinforced mechanism. Firstly, the dynamic process of the CNT agglomeration is discussed from the perspectives of fractional free volume (FFV) and binding energy. Then, a combined explanation of mechanical and interfacial properties is given to reveal the CNT-reinforced mechanism of the coefficient of friction (COF). Results indicate that the bulk, shear and Young’s modulus increase with the increasement of CNT, which are increasement of 19.13%, 21.11% and 26.89% in 15 wt.% CNT/VNR composite compared to VNR; the predicted results are consistent with the existing experimental conclusions, which can be used to reveal the CNT-reinforced mechanism of the rubber materials at atomic scale. It can also guide the design of rubber material prescription for aircraft tire. The molecular dynamics study provides a theoretical basis for the design and preparation of high wear resistance of tread rubber materials. Full article

Non-exhaust emissions, generated by the wear of brake systems, tires, roads, clutches, and road resuspension, are responsible for a large part of airborne pollutants in urban areas. Brake wear accounts for 55% of non-exhaust emissions and significantly contributes to urban health diseases related to air pollution. A major part of the studies reported in the scientific literature are focused on experimental methods to sample and characterize brake wear particles in a reliable, representative, and repeatable way. In this framework, simulation is an important tool, which makes it possible to give interpretations of the experimental results, formulate new testing approaches, and predict the emission produced by brakes. The present comprehensive literature review aims to introduce the state of the art of the research on the different aspects of airborne wear debris resulting from brake systems which can be used as inputs in future simulation models. In this review, previous studies focusing on airborne emissions produced by brake systems are investigated in three main categories: the subsystem level, system level, and environmental level. As well as all the information provided in the literature, the simulation methodologies are also investigated at all levels. It can be concluded from the present review study that various factors, such as the uncertainty and repeatability of the brake wear experiments, distinguish the results of the subsystem and system levels. This gap should be taken into account in the development of future experimental and simulation methods for the investigation of airborne brake wear emissions. Full article