Tire Type Research Articles

An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses – a contribution towards a digital twin of the road system

ABSTRACT The high-resolution prediction of tyre-pavement contact stresses is crucial for advancing pavement design and vehicle safety. A significant knowledge gap exists in accurately and quickly predicting tyre-pavement contact stresses, especially under varying conditions. Addressing this challenge, this study is part of the concerted effort within the project CRC/TRR 339, contributing to the series ‘A Contribution towards a Digital Twin of the Road System’. It aims to bridge this gap by introducing a novel Multimodal Conditional Deep Convolutional Generative Adversarial Network (MC-DCGAN) model for forecasting tyre-pavement contact stresses, offering an efficient and accurate method. The incorporation of a multimodal conditional module within the GAN-based framework allows for controllable outputs tailored to specific tyre types, inflation pressures, and loads. Leveraging the DCGAN architecture significantly enhances prediction accuracy. Experimental validation confirms the method’s high accuracy, strong generalisation capabilities, and a substantial reduction in computational time – nearly two orders of magnitude lower compared to traditional Finite Element simulations. This research not only fills a critical void in the high-precision prediction of tyre-pavement contact stresses, but also holds significant implications for optimising pavement design and enhancing road traffic safety, marking a significant step towards realising a digital twin of the road system.

Typical Tire Additives in River Water: Leaching, Transformation, and Environmental Risk Assessment.

Tire wear particles (TWPs) released during vehicle driving can enter water bodies, leading to leaching of tire additives (TAs) in aquatic environments. However, the transformation behavior and related ecological impacts of TAs and their transformation products (TPs) remain unclear. In this study, laboratory-based simulation experiments and field investigations were conducted to explore the transformation mechanisms and ecological risks of TAs. After being placed in river water for 24 h, about 7-95% of 12 investigated TAs in TWPs were leached. Forty-eight TPs from eight TAs were tentatively identified along with different transformation pathways via suspect screening by high-resolution mass spectrometry. Semiquantitative results indicated that TPs derived from N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylene-diamine (6PPD) were predominant in leachates, while aryl hydrolysis and quinone pathways were the main transformation pathways. Field investigations on urban surface water samples from 16 sites in Hong Kong revealed the occurrence of 17 TAs and 1 TP, with concentrations ranging from 13.9 to 2230 ng/L (median ± standard deviation: 226 ± 534 ng/L). Sixteen TPs from six TAs were additionally identified via suspect screening. It is estimated that 6PPD-quinone and seven TAs could pose medium to high ecological risk, while N-(1,3-dimethylbutyl)-N'-phenyl-p-quinonediimine, a frequently detected TP, was identified as a persistent-bioaccumulative-toxic substance.

Measurement of tyre-based microplastics using traditional and quantum cascade laser-based infrared spectrometry

BackgroundDespite the potential environmental impact of TWPs (tyre wear particles), there is a lack of reliable analytical methodologies suitable for their routineidentification and characterization. The number of papers dealing with this topic is, so far, very reduced and, therefore, there is a need for addressing it, mostly because traditional transmittance-based IR techniques are suboptimal due to scattering caused by black carbon in tyres. ResultsThis study aims to evaluate the most appropriate infrared (IR) spectrometric technique for monitoring TWPs. Macro attenuated total reflectance (ATR), reflectance microscopy, and quantum cascade laser-based micro transflectance (QCL-LDIR) were employed to analyse samples from used car and truck tyres in two sample configurations: small tyre fragments (∼1 cm2) and TWPs (< 1 mm). ATR yielded well-defined spectra with good signal-to-noise ratios, allowing for a straightforward interpretation of the major functional moieties. Despite reflectance measurements on tyre fragments provided good results, those on TWPs offered limited information due to noise and scattering. Transflectance offered clear peaks and enhanced resolution in the fingerprint region –compared to the other techniques-, much faster analysis times and the ability to effectively measure particles down to 20–10 µm, thus, emerging as the most effective technique for TWPs analysis. However, spectral interpretation is not immediate. Further, a proof-of-concept study was done to evaluate whether the analytical techniques contain information to differentiate types of tyres. An unsupervised pattern recognition and a supervised classification technique (principal components analysis and classification trees, CART) were used, which were able to differentiate among the tyres, notably the truck tyre from the cars tyres. SignificanceThe study presents first time the use of micro transflectance IR to study tyre particles down to 20 µm. Traditional total attenuated reflectance is demonstrated as a suitable way to analyse bigger microplastics. These two options open pathways to monitor this important emerging contaminant in environmental matrices

A physical‒data-driven combined strategy for load identification of tire type rail transit vehicle

Tire load is one of the basic input parameters required for vehicle design and safety evaluation. Identifying the tire load with high accuracy is of great significance. However, the direct measurement of tire load is often high-cost and complex. Meanwhile, load identification solely based on physical measurement or data has great limitations of low accuracy and low robustness. This paper proposes a physical‒data-driven combined load identification strategy that includes an extended Kalman filter (EKF) and a data-driven correction model. These two models are configured in series. Derived from a physical state-space model of the vehicle dynamics, the EKF is used for the preliminary identification of the load. The data-driven model extracts the spatial and temporal characteristics of signals through the convolutional neural network and bidirectional gated recurrent unit, and then predicts the errors of the extended Kalman filter and corrects the identified results. The presented strategy is applied to an APM300 rubber wheel vehicle for load identification. The results have shown that the physical‒data-driven combined strategy can reduce the influence of parameter perturbation and improve the identification accuracy (6.4%). Thanks to organically combining the physical- and data-driven methods and integrating the rules and experience of the entire system, the strategy has strong generalization performance. Compared with traditional algorithms, the presented strategy could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.

Investigating the Impact of Speed and Tire Pressure of a Wheel Tractor on Soil Properties: A Case Study in Northeastern Uzbekistan

In agriculture, machines engaged in various agrotechnical activities and operations have different impacts on the soil. The effect of mechanization is primarily reflected in two indicators: soil density and hardness. At the same time, considering the direct dependence of tractive resistance on soil hardness in processing machines and sprayers, we studied subsequent changes in the soil in the path of wheels affected by the soil after the passage of four-wheeled and three-wheeled tractors. We also examined various atmospheric pressures in the tractor’s tires and the impact of different types of tires on soil compaction and traction. The studies showed that to reduce the compression impact on the soil of four-wheeled tractor working systems during certain technical operations, it is necessary to choose the maximum permissible travel speed and the minimum air pressure in the tires specified in the technical conditions. This approach helps to decrease soil compaction and maintain its structure. Additionally, it was found that three-wheeled tractors exert less pressure on the soil compared to four-wheeled ones, which should also be considered when selecting equipment for different agrotechnical tasks. Optimizing tire pressure and tractor speed is crucial for minimizing negative soil impact and enhancing the efficiency of agricultural operations.

Evaluation of the Changes in Dimensions of the Footprint of Agricultural Tires under Various Exploitation Conditions

This paper presents an innovative method to determine the impact of agricultural wheels on soil. The experiment was conducted under controlled conditions, and the parameters of the tire footprints on the soil were analyzed. The tested parameters were the width, length, and depth of the footprint, the cross-section area of the tire, and the area of the footprint. All parameters were determined using the 3D scanning method. Two types of tires, two levels of vertical load, and three levels of inflation pressure were used. The aim of the research was to demonstrate differences in changes in the footprint parameters as a result of changes in the operational parameters of the tires. It was found the bias-ply tire was less responsive to changes in the width and length of the footprint than the radial tire. Moreover, it was shown that radial and bias-ply tires achieved similar values for the footprint area but in the case of bias-ply tires, there was a much greater footprint depth. This means that the side parts of the footprint of bias-ply tires have a more vertical profile, so they carry the vertical loads to a lesser extent.

Quantification of tire wear particles in road dust based on synthetic/natural rubber ratio using pyrolysis-gas chromatography–mass spectrometry across diverse tire types

Increase in road traffic leads to increased concentrations of tire-wear particles (TWPs), a prominent source of microplastics from vehicles, in road dust. These particles can re-enter the atmosphere or move into aquatic ecosystems via runoff, impacting the environment. Consequently, accurately assessing and managing TWP levels in road dust is crucial. However, the ISO method (ISO/TS 20593 and 21396) uses a constant ratio of styrene-butadiene rubber (SBR) to natural rubber (NR) for all tires, disregarding the variability in tire composition across different types and brands. Our study found substantial SBR content (15.7 %) in heavyweight truck tires, traditionally believed to be predominantly NR. We evaluated the SBR/NR content in 15 tire types and proposed a method to more accurately evaluate TWP concentrations in road dust from five different locations. Our findings suggest that the conventional ISO method may underestimate the concentrations of TWP due to its reliance on a static ratio of SBR/NR. This study underscores the necessity for a more flexible approach that can adapt to the variability in SBR and NR content across different tire types. By delineating the limitations inherent in current assessment methods, our research contributes to a more adaptable understanding of TWP concentrations in road dust. This advancement prompts the development of a revised methodology that more accurately reflects the diverse compositions of tire rubber in environmental samples.

Effects of road- vs. field-recommended tyre inflation pressures and small variations in soil moisture content on traffic-induced soil compaction during seedbed preparation

Soil compaction has become one of the most common forms of soil degradation, with associated negative effects on the functioning of agricultural soils. Decades of research have led to publication of several practical measures that mitigate risk of soil compaction, yet adoption among European farmers remains limited. To improve adoption by farmers, experiments under practical conditions using common operations should be performed. A field experiment was set up to evaluate the effects of road- (200 kPa) vs. field- (140 kPa and 100 kPa) recommended tyre inflation pressure and soil moisture content (0.24 g g−1 vs. 0.22 g g−1) on traffic-induced compaction of a silt loam soil during seedbed preparation. The various tyre inflation pressures were tested using two tyre types (standard radial tyre at 200 kPa and 140 kPa; VF tyre at 100 kPa) of similar dimensions mounted onto the same tractor. Variation in experimental soil moisture content was achieved by performing the seedbed preparation on two dates in spring (early, March 30th, vs. late, April 14th). In this one-year study, reducing the standard-tyre inflation pressure from road- (200 kPa) to field-recommended (140 kPa) pressure did not prevent soil compaction. The use of VF tyres at field-recommended (100 kPa) tyre inflation pressure resulted in significant reductions in vertical contact stress and mean normal stress as compared to 200 kPa. Although non-significant, the pressure reduction from 200 kPa to 140 kPa did result in a 4 % increase in in-track dry matter sugar beet yield while a reduction from 200 kPa to 100 kPa resulted in an 11% increase. Lower tyre inflation pressure showed no significant effect on fuel consumption and penetration resistance. Seedbed preparation under slightly drier (0.22 g g−1) soil conditions negatively affected fuel consumption (+11 %) and resulted in an in-track dry matter sugar beet yield increase of 11 %. For both tested prevention strategies, the impact of soil compaction was better observable by sugar beet yield than by most of the measured soil physical parameters.

Design and simulation of a new type of composite flexible-spoke non-pneumatic tire

Design and simulation of a new type of composite flexible-spoke non-pneumatic tire

Key dynamic performance analysis of flexible spoke non-pneumatic tire for matched unmanned ground vehicles

To fundamentally improve the lifespan of unmanned ground vehicles and their internal parts, it is urgent to address the problem of tire jumping during driving, steering, or turning. This article proposes the use of flexible spoke non-pneumatic tires instead of pneumatic tires. Therefore, a three-dimensional finite element model of a certain type of pneumatic tire and flexible spoke non-pneumatic tire was constructed and the validity of the model was verified. A simulation scheme was designed to investigate the tire cushioning and lateral performance. In order to explore the degree of influence of the structural parameters of the flexible spoke non-pneumatic tire on its cushioning and lateral performance, an orthogonal experimental simulation scheme based on L9 (33) was developed. The results show that the non-pneumatic tire requires 0.006 s to recover stability after experiencing significant vibration due to passing over a bump, while the pneumatic tire requires 0.028 s. Under rated working conditions, the non-pneumatic tire is subjected to a lateral force of 285.29 N, which is 1.9 times that of the pneumatic tire’s 142.59 N. The cushioning and lateral performance of the non-pneumatic tire is most affected by the element angle α, with an impact level of over 90%. The flexible spoke non-pneumatic tire proposed in this article can effectively solve the tire jumping problem of unmanned ground vehicles during driving, while providing design ideas for improving the cushioning and lateral performance of non-pneumatic tires.

A multispectral vision-based machine learning framework for non-contact vehicle weigh-in-motion

The present study proposes an automated non-contact weigh-in-motion framework using multispectral vision and machine learning method, which provides an all-weather monitoring of heavy vehicles on transportation infrastructures. Multispectral based computer vision techniques consist of two steps, one is acquiring the tire deformation parameters from thermal imaging camera and the latter is obtaining the sidewall characters from the ordinary one. A novel edge detection algorithm is designed for thermal image-based tire deformation estimation. A general recognition model with well-established tire characters database for universal tire sidewall markings, such as non-standard fonts or non-text characters, curved markings or inverted markings, and is developed based on Efficient neural network (E-NET) and YOLOV5S methods. A machine learning-based model for estimating the various vehicles tire-roadway contact forces is established based on eXtreme Gradient Boosting (XGBoost). A database of different vehicles’ tires with a total of 1127 samples from 19 types of tires and 648 of laboratory datasets from common vehicles are collected to train the model. The Bayesian optimization is employed to capture the optimal model, and a hybrid training strategy including biased data is applied to update the model. An importance analysis for 11 features is conducted to validate the performance proposed model. The accuracy, feasibility, and effectiveness of the proposed method is carefully investigated by field experiments for trucks and sport utility vehicles under static and low-speed drive. A maximum of 5 % difference is witnessed by comparing the results with that of the traditional WIM system. The present method presents a cost-effective means with no sensors on structures for vehicle weighting, that demonstrates a great potential to implement in toll station, highway infrastructures as well as health monitoring of civil engineering fields.

Интеллектуальная технология определения оптимального давления воздуха в шинах колёс сельскохозяйственных тракторов

The use of powerful tractors and wide-beam agricultural machinery, which have a strong compaction effect on the soil, is conditioned by the agricultural production profitability. The lack of a holistic systematic approach to reducing the anthropogenic compaction effect of wheel propulsors and working units of modern energy-intensive heavy machinery on the soil of agricultural landscapes requires the improvement of methods for determining the optimal air pressure in the tires of agricultural tractors. For this purpose, the authors developed an intelligent technology for determining the optimum air pressure in the various types of tires used in agricultural tractors. The problem was solved by processing “large” arrays of data on operating machine and tractor units and agrolandscapes in order to increase crop yields. Collection and analysis of primary data required for training the neural network were conducted on the fields of the Republic of Adygea during the cultivation of winter barley and winter wheat with the use of machinery equipped with Michelin AXIOBIB2 low-pressure tires. The Feed forward neural network was applied. Fourteen parameters were used as input factors to the neural network: types of soil, machinery and tires; field coordinates; presence and type of mounted equipment; season; type of tillage; granulometric composition, moisture and soil density; wheel diameters; motion speed of machines; field slope; agricultural background. The task set presumed the yield parameter as the main target function. The neural network pre-trained on a significant amount of input data calculates the optimal air pressure in tires when inputting the necessary data. Based on the designed software, the authors plan to develop a system of automatic adjustment of tire inflation depending on the incoming factors made in the offline and online modes.

Efisiensi Rem Utama dan Rem Parkir Kendaraan Mobil Barang Dengan Variasi Tekanan Angin Ban

Tire pressure requirements may vary for different vehicles depending on factors such as the brand, load, and tire type. Inadequate tire pressure can result in suboptimal braking performance. This study aims to examine the impact of tire pressure variations on the efficiency of the main brake and parking brake in vehicles. The brake tester was utilized to conduct experiments on pickup goods vehicles with a GVWR less than 3,500 kg. The findings reveal that tire pressure has a significant influence on the efficiency of the main brake and parking brake. Higher tire pressure corresponds to lower efficiency values for both brakes, while lower tire pressure yields higher efficiency values. It is crucial to raise awareness among vehicle owners about the importance of maintaining proper tire pressure as it directly impacts braking performance and overall vehicle safety.

Risk implications induced by behaviors of artificial and pavement-generated TWPs in river water: Role of particle-self properties and incubation aging

Here, we investigated the pristine properties of three typical tire wear particles (TWPs) and their aging properties after incubation in runoff (primary aging) and sewage (further aging), and captured the differences in the behavioral characteristics of nine TWPs in river water, with a view to paving the way for revealing the intrinsic mechanism of the hydroecological effects of TWPs. Our results highlight that the generation modes of three pristine tire wear particles (TWPs), stemming from typical tire and road wear processes—specifically, rolling friction (R-TWPs) and sliding friction (S-TWPs), alongside cryogenically milled tire treads (C-TWPs)—significantly impact their pristine physicochemical properties. This impact encompasses surface structure, particle size (D [4,3]: 8.5–121.3 μm), surface potential (−10.4 ∼ −1.8 mV), contact angle (95.2–129.8°), density (1.09–1.75 kg/m3), etc., consequently, these differences significantly influence their migration capability and sorption capacity during the incubation and aging in runoff and sewage. Interestingly, after incubation and aging in the migrating aqueous phase, particularly with additional aging in sewage, not only do distinctions in the aforementioned physicochemical properties (namely, particle size (5.6–6.6 μm), surface potential (−18.4 ∼ −18.1 mV), contact angle (124.5–125.4°), density (1.05–1.16 kg/m3)) among various types of TWPs diminish, but the environmental behaviors (encompassing, desorption capacity, aggregation kinetics, photochemical activity—formation of persistent free radicals, and exudation—derivative (6PPD-Quinone) of N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine: 6PPD) exhibited by this array of TWPs demonstrate a remarkable coherence within the downstream river water. Concerningly, the aforementioned features of aquatic system behaviors appear to be predisposed towards exacerbating the heightened toxicity of TWPs, for example, the leaching concentration of 6PPD-Q increased by two to three times after aging, aligning with established precedents regarding the toxicological causes associated with the quinone derivatives of antioxidants in rubber contaminants.

Tire Performance and Testing under Longitudinal and Combined Slip States: Induced vs Resultant Wheel-Related Slip

ABSTRACT Tires influence many overall vehicle characteristics, including safety, performance, comfort, and handling. Testing is needed to characterize tires for the selection of original equipment manufacturers submissions as well as for the development of other suspension and chassis components. The reality that tire responses are highly nonlinear and change significantly with temperature, wear, and surface pairing makes testing to adequately characterize tires for vehicle development difficult. Furthermore, achieving robust, repeatable braking and driving measurements is particularly challenging. Unlike cornering tests, the test rig input is directly opposed to the tire response vector. Moreover, using an estimated parameter (e.g., slip ratio) as a control value prohibits a clear distinction between the linear test rig input and the nonlinear tire response. In this paper, the influence of the wheel-related slip control on tire performance under longitudinal and combined slip conditions is investigated. Two different tire types are measured in a laboratory environment on an MTS Flat-Trac CT+ tire test rig. A variety of test procedures are used to consider the influence of slip and torque rates under different operating conditions (e.g., wheel loads). A recommendation is given for a low-wear and cost-efficient testing methodology for an improved assessment of tire longitudinal and combined slip characteristics by using a linear test rig input.

Impact of Chemical Properties Crumb Rubber with Different Waste Tire in Hot Mix Asphalt Design

The purpose of this research is to assess the impact of chemical properties of rubber crumbs for various types of waste tires mixed in design asphalt. This paper gives a scientific contribution of the chemical properties in relationship with the mix design asphalt. Many tires are replaced and dumped as waste in many parts of the world every year. This has created severe environmental problems such as pollution which governments of many nations spend a lot of budgets to manage every year. However, incorporating rubber crumbs as part of asphalt construction would help solve the environmental pollution problem in the world. The analysis is important because chemical properties such as Zn, Carbon, and Mg, have different characteristics that must be considered in making the best asphalt mix design. This would help to achieve alternate solutions. Furthermore, Utilizing XRD, FTIR, and TGA, the granules of several kinds of waste tire were examined. To achieve the best mix design for the asphalt, the results had many viewpoints. The TGA result demonstrated that all waste tires decomposed at the temperature of 500°C. However, the truck tire waste had the highest maximum thermal decomposition temperature of 587°C. The FTIR result showed that all the tire waste has similar chemical functional groups of polystyrene. The result of XRF showed that truck tire waste possesses the highest Zn content which is considered as dangerous element to the environment.

Swelling effect on the microwave pyrolysis of waste car tires

AbstractSwelling can change the mechanical properties and structural characteristics of waste car tires (WCTs), so that the rubber molecular chain stretching, porousness becomes larger, which is conducive to improving the mass transfer effect and pyrolysis efficiency. Based on the modification characteristics of swelling, the swelling effects of different swelling agents, temperatures, tire types, and particle sizes were investigated. The results showed that coal tar can effectively swell WCTs, and the higher temperature and smaller particle size, contribute to better swelling. When the temperature is 40°C and the particle size is 1 × 1 cm, the equilibrium mass absorption rate of WCTs can reach up to 1.67 g/g, and the swelling effect is the best. In addition, combined with the unique microwave advantages, the swelling rate was further accelerated, WCTs reached swelling saturation 58.5 h earlier than in natural swelling. Moreover, the swelling effect on the three‐phase product output of microwave pyrolysis of WCTs was investigated. After swelling treatment, we achieved the deeper pyrolysis degree of WCTs and the yield of pyrolysis oil was relatively increased by about 39.2%, while the yield of both gases and solids decreased.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

Experimental investigation of road surface parameters affecting tire/road noise

It is known that tire/road noise is strongly affected by the properties of the road surface such as its profile and sound absorption coefficient. Several parameters, represented by mean profile depth (MPD), have been proposed or standardized as surface profile parameters, but none of them clearly explains the relationship with tire/road noise generation. This study attempted to identify a new surface parameter that correlates more closely with tire/road noise. Surface profile measurements and close proximity (CPX) measurements were conducted on several road surfaces with different properties on test tracks. Tire P1, the reference tire for the CPX method, and two types of passenger car tires were used for the CPX measurements. According to our experience, the variation of the peak height of the road surface with distance is closely related to the excitation input to the tires and thus noise generation. Therefore, we devised a road profile parameter to quantitatively evaluate the variation of peak height. A relatively strong correlation was observed between this parameter and the CPX level. The validity of this parameter will be verified by further measurements on general roads in the future.

Theoretical analysis of conversion process of tire tread vibration to road noise exciting force

Tire tread vibration due to unevenness of the road surface, through some process, acts on the axle, which is the excitation source of solid-borne components of road noise. However, this conversion process has not been fully understood until now. Therefore, the author decomposed it into two parts and analyzed it theoretically. They are: a) variations in cavity pressure, b) variations in sidewall contact angle with the wheel rim. As a result, it is now possible to formulate the relationship between tread displacement/velocity and the force acting on the axle. This study enabled us to calculate the effect of these two processes on a typical tire shape, size and pressure.