Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

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Tire-Pavement Interaction Noise (TPIN) becomes the main noise source contributor for passenger vehicles traveling at speeds above 40 kph. Therefore, it represents one of the main contributors to noise environmental pollution in residential areas nearby highways. TPIN has been subject of exhaustive studies since the 1970s. Still, almost 50 years later, there is still not an accurate way to model it. This is a consequence of a large number of noise generation mechanisms involved in this phenomenon, and their high complexity nature. It is acknowledged that the main noise mechanisms involve tire vibration, and air pumping within the tire tread and pavement surface. Moreover, TPIN represents the only vehicle noise source strongly affected by an external factor such as pavement roughness. For the last decade, new machine learning algorithms to model TPIN have been implemented. However, their development relay on experimental data, and do not provide strong physical insight into the problem. This research studied the correct configuration of such tools. More specifically, Artificial Neural Network (ANN) configurations were studied. Their implementation was based on the problem requirements (acoustic sound pressure prediction). Moreover, a customized neuron configuration showed improvements on the ANN TPIN prediction capabilities. During the second stage of this thesis, tire noise test was undertaken for different tires at different pavements surfaces on the Virginia Tech SMART road. The experimental data was used to develop an approach to account for the pavement profile when predicting TPIN. Finally, the new ANN configuration, along with the approach to account for pavement roughness were complemented using previous work to obtain what is the first reasonable accurate and complete tool to predict tire noise. This tool uses as inputs: 1) tire parameters, 2) pavement parameters, and 3) vehicle speed. Tire noise narrowband spectra for a frequency range of 400-1600 Hz is obtained as a result. / Master of Science / Tire-Pavement Interaction Noise (TPIN) becomes the main noise source contributor for passenger vehicles traveling at speeds above 40 kph. Therefore, it represents one of the main contributors to noise environmental pollution in residential areas nearby highways. TPIN has been subject of exhaustive studies since the 1970s. Still, almost 50 years later, there is still not an accurate way to model it. This is a consequence of a large number of noise generation mechanisms involved in this phenomenon, and their high complexity nature. It is acknowledged that the main noise mechanisms involve tire vibration, and air pumping within the tire tread and pavement surface. Moreover, TPIN represents the only vehicle noise source strongly affected by an external factor such as pavement roughness. For the last decade, machine learning algorithms, based on the human brain structure, have been implemented to model TPIN. However, their development relay on experimental data, and do not provide strong physical insight into the problem. This research focused on the study of the correct configuration of such machine learning algorithms applied to the very specific task of TPIN prediction. Moreover, a customized configuration showed improvements on the TPIN prediction capabilities of these algorithms. During the second stage of this thesis, tire noise test was undertaken for different tires at different pavements surfaces on the Virginia Tech SMART road. The experimental data was used to develop an approach to account for the pavement roughness when predicting TPIN. Finally, the new machine learning algorithm configuration, along with the approach to account for pavement roughness were complemented using previous work to obtain what is the first reasonable accurate and complete computational tool to predict tire noise. This tool uses as inputs: 1) tire parameters, 2) pavement parameters, and 3) vehicle speed.

A cada ano aproximadamente 1,25 milhões de pessoas são vítimas fatais de acidentes no trânsito no mundo. Por conta disso, os acidentes viários são globalmente considerados um problema de saúde pública, aumentando os esforços para criação de projetos seguros que evitem sua ocorrência. Para tanto, um dos fatores contribuintes mais estudados são as condições superficiais das estradas, principalmente no que tange ao atrito entre pneu/pavimento e as condições climáticas que possam influir nessa interação. Estudos sugerem intervenções no sentido da troca de revestimento asfáltico das vias, aumentando sua drenagem superficial e o atrito oferecido, a fim de se observar os efeitos dessa técnica na redução do número de acidentes. Nesse contexto, o objetivo desse trabalho é avaliar a importância da textura superficial dos pavimentos na ocorrência de acidentes rodoviários em condição de pista molhada por meio de estudo de caso em um trecho de serra da pista sentido Norte da Rodovia Régis Bittencourt - BR116, que interliga Curitiba a São Paulo. Nele foram selecionados dois trechos experimentais que receberam intervenção no pavimento com a incorporação de revestimento asfáltico com granulometria descontínua do tipo gap-graded. Foram realizados ensaios de campo e de laboratório, além do acompanhamento do número de acidentes. Quanto a essa última etapa, percebeu-se que, imediatamente após a intervenção, o número de acidentes decai significativamente. Para o Trecho Experimental 1 a ocorrência desses eventos volta a crescer, contudo, para o Trecho Experimental 2 se atinge um nível de constância. Em se tratando dos ensaios de campo, observam-se oscilações entre as medições, porém, os valores calculados de International Friction Index (IFI) foram classificados majoritariamente como \"ótimos\". Quanto aos ensaios de laboratório, os resultados obtidos indicam boas características de drenagem e de aderência pneu/pavimento. Por fim, como principal contribuição deste trabalho, apresenta-se o cálculo da efetividade da intervenção por meio dos Métodos Empírico de Bayes (EB) e Diferenças em Diferenças (DD). O primeiro retornou um valor de redução no número total acidentes, o segundo computou além do total, a redução para pista seca e pista molhada, separadamente. / Each year approximately 1.25 million people are fatal victims of traffic accidents in the world. Because of it, road accidents are globally considered as public health problem, increasing efforts to create safe projects, avoiding its occurrence. Therefore, one of the most studied contributing factors is the surface conditions of the roads, especially with regard to the tire/pavement friction and the climatic conditions that may influence this interaction. Studies suggest interventions to change the asphalt pavement of the roadways, increasing its surface drainage and the friction offered, in order to observe the effects of this technique in reducing the number of accidents. In this context, the objective of this work is to evaluate the importance of the superficial texture of the pavements in the occurrence of road accidents in wet road conditions by means of a case study in a section of the north - western Régis Bittencourt Highway - BR116, which connects Curitiba to São Paulo. In it, two experimental sections were selected that received intervention in the pavement with the incorporation of asphalt coating with discontinuous granulometry of the gap-graded type. Field and laboratory tests were performed, as well as the monitoring of the number of accidents. Regarding this last stage, it was noticed that, immediately after the intervention, the number of accidents decreases significantly. For Experimental Section 1 the occurrence of these events grows again, however, for Experiment 2 if a level of constancy is reached. In the case of field tests, fluctuations between the measurements are observed, however, the calculated International Friction Index (IFI) values were classified as \"optimal\". Regarding laboratory tests, the results obtained indicate good drainage and tire/pavement friction characteristics. Finally, as the main contribution of this work, the calculation of the effectiveness of the intervention is presented through the Methods Empirical of Bayes (EB) and Differences in Differences (DD). While the former returned a reduction value in the total number of accidents, the second counted besides the total, the reduction in dry and wet road, separately.

Existe uma preocupação geral quanto à aderência que se pode obter entre os pneus de uma aeronave e as superfícies das pistas de aeroportos, principalmente naqueles em que operam aeronaves a jato, em altas velocidades, tornando a aderência um fator importante relacionado à segurança de vôo. O atrito nas pistas muda ao longo do tempo, em função do tráfego, das condições climáticas e das práticas de manutenção adotadas. Contaminantes, tais como água e resíduos de borracha, causam diminuição do atrito das superfícies das pistas, em grandes extensões, principalmente nas zonas de toque. Este trabalho analisa a aderência pneu-pavimento em revestimentos asfálticos aeroportuários, por meio da avaliação da macrotextura, do atrito dinâmico, do atrito medido com o Pêndulo Britânico e da drenabilidade, em regiões das pistas submetidas a diferentes solicitações de tráfego e diferentes ações de manutenção. O Aeroporto Internacional de São Paulo / Congonhas foi escolhido para o estudo de caso, pois conta com duas pistas de pouso e decolagem com revestimentos asfálticos com grooving e tráfego de aeronaves com intensidade suficiente para promover os problemas de aderência relatados na literatura. Os resultados indicam a influência do volume de tráfego, do grooving, do acúmulo de resíduos de borracha e das práticas de manutenção sobre a aderência pneu-pavimento proporcionada pelos revestimentos. / There is a general concern about braking performance in runways pavements surfaces, particularly in airports operating turbojet aircrafts with high landing speeds, making friction become a significant safety subject. Runway friction changes along the time depending on aircraft traffic, weather conditions and maintenance works. Contaminants such as rubber deposits and water cause friction loss on pavement surface, mainly in the touchdown zone on runways, and it can reach quite extensive areas. This work analyzes the tire/pavement adherence on asphalt runways surfaces, by evaluating parameters as macrotexture, friction (using British Pendulum and MuMeter) and draining capability, in different areas of runways, submitted to different traffic and different maintenance actions. The field surveys were performed at Congonhas Airport. Congonhas operates two asphalt runways (grooved) with enough traffic to promote the adherences problems reported in literature. The results indicate how volume of traffic, grooving, rubber deposits and pavement maintenance practices can influence on runway surface adherence level.

Existe uma preocupação geral quanto à aderência que se pode obter entre os pneus de uma aeronave e as superfícies das pistas de aeroportos, principalmente naqueles em que operam aeronaves a jato, em altas velocidades, tornando a aderência um fator importante relacionado à segurança de vôo. O atrito nas pistas muda ao longo do tempo, em função do tráfego, das condições climáticas e das práticas de manutenção adotadas. Contaminantes, tais como água e resíduos de borracha, causam diminuição do atrito das superfícies das pistas, em grandes extensões, principalmente nas zonas de toque. Este trabalho analisa a aderência pneu-pavimento em revestimentos asfálticos aeroportuários, por meio da avaliação da macrotextura, do atrito dinâmico, do atrito medido com o Pêndulo Britânico e da drenabilidade, em regiões das pistas submetidas a diferentes solicitações de tráfego e diferentes ações de manutenção. O Aeroporto Internacional de São Paulo / Congonhas foi escolhido para o estudo de caso, pois conta com duas pistas de pouso e decolagem com revestimentos asfálticos com grooving e tráfego de aeronaves com intensidade suficiente para promover os problemas de aderência relatados na literatura. Os resultados indicam a influência do volume de tráfego, do grooving, do acúmulo de resíduos de borracha e das práticas de manutenção sobre a aderência pneu-pavimento proporcionada pelos revestimentos. / There is a general concern about braking performance in runways pavements surfaces, particularly in airports operating turbojet aircrafts with high landing speeds, making friction become a significant safety subject. Runway friction changes along the time depending on aircraft traffic, weather conditions and maintenance works. Contaminants such as rubber deposits and water cause friction loss on pavement surface, mainly in the touchdown zone on runways, and it can reach quite extensive areas. This work analyzes the tire/pavement adherence on asphalt runways surfaces, by evaluating parameters as macrotexture, friction (using British Pendulum and MuMeter) and draining capability, in different areas of runways, submitted to different traffic and different maintenance actions. The field surveys were performed at Congonhas Airport. Congonhas operates two asphalt runways (grooved) with enough traffic to promote the adherences problems reported in literature. The results indicate how volume of traffic, grooving, rubber deposits and pavement maintenance practices can influence on runway surface adherence level.

No description available.

Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tire-pavement interaction noise is tire tread excitation. The tire tread contributes to the tire-pavement interaction noise mainly through two mechanisms: (1) tread block impact, and (2) the compression and expansion of the air in the tread groove at the contact patch. The tread pattern is the critical part of the tire design since it can be easily modified. Hence, the main focus of this study is to quantify the tread pattern contribution in total tire-pavement interaction noise. To achieve this goal, the noise produced by the tread pattern is separated from the total tire-pavement interaction noise. Since the tread pattern excitation is periodic with tire rotation, the noise produced by the tread is assumed to be related to the tire rotation. Hence, the order domain synchronous averaging method is used in this study to separate and quantify the tread pattern contribution to the total tire-pavement interaction noise. The experiment has been carried out using an On-Board-Sound-Intensity (OBSI) system. Five tires were tested including the Standard Reference Test Tire (SRTT). Compared to the conventional OBSI system, an optical sensor was added to the system to monitor the tire rotation. The once per revolution signal provided by the optical sensor is used to identify the noise signals associate to each revolution. In addition to the averaging method using optical signals, other data processing techniques have been investigated for separating the tread-pattern noise without utilizing the once per revolution signal. These techniques are autocorrelation analysis, a frequency domain filter, principal component analysis, and independent component analysis. In the tread-pattern noise generation, the tread profile is the most important input parameter. To characterize the tread profile, the tread pattern spectral content and air volume velocity spectral content for all the five tires are computed. Then, the tread pattern spectrum and the air volume velocity spectrum are both correlated with the separated tread-pattern noise by visual inspection of the spectra shape. / Master of Science / Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tirepavement interaction noise is tire tread (the part that is in contact with the ground on the surface of the tire) excitation. This type of noise is called the tread-pattern noise. This study is dedicated to separating the tread-pattern noise from the total tire-pavement interaction noise, which has not been reported in the open literature. The separation of the tread-pattern noise can provide critical criteria for the tread-pattern acoustic design, which is one of the most important factors in the tire tread pattern design. Hence, the acoustic design of the tread pattern can be evaluated directly from the tread-pattern noise measurement, thus improving the design efficiency. In addition, the standalone study on the tread-pattern noise can reveal more fundamental physical underpins how the geometry of the tread can affect the noise generated. This finding has the potential to inspire the design of the tires with higher acoustic performance over the tires being used currently.

With advancements in technology, electric vehicles are dominating the world making Internal Combustion engines less relevant, and hence vehicles are becoming quieter than ever before. But noise levels remain a significant concern for both passengers and automotive manufacturers. The vehicle's interior noise can affect the overall driving experience and even the safety of the driver and the passengers. The two main sources of vehicle interior noise are attributed to air-borne noises and structure-borne noises. A modern automobile is a complicated vibration system with several excitation sources like the engine, transmission system, tire/road interface excitation, and wind noise. With electric vehicles on the rise, the engine and transmission noise is practically eliminated, and effective preventive measures and control systems are already in place to reduce the aerodynamic-based noise, vibrations, and harshness (NVH) in modern automobiles making the structure-borne noise the most crucial of the noise sources. Tire/road interaction noise being the most dominant among the structure-borne noise is the main concern of the vehicle interior noise. The two main sources of vehicle interior noise induced by the tire pavement interaction noise are structure-borne noise induced by the low-frequency excitation and air-borne noises produced by the mid and high-frequency excitation. The present study tested an all-season tire over varying operational conditions such as different speeds, normal loads, and inflation pressures on an asphalt surface. Two tri-axial accelerometers attached 1800 apart from each other on the inner liner of the tire of a Volkswagen Jetta were used to measure the circumferential, lateral, and radial acceleration data. An Inertial Measurement Unit (IMU) and velocity box (VBOX) were instrumented in the vehicle to measure the acceleration at the center of gravity (COG) position of the vehicle and the longitudinal velocity of the vehicle respectively. The vehicle was also equipped with a modified hybrid of Close Proximity Testing (CPX) and On-Board Sound Intensity (OBSI) sound measurement systems which were designed and manufactured in-house to measure the tire/road interaction noise at the leading and trailing edges of the tire/road contact patch. Another microphone was instrumented inside the passenger compartment of the vehicle at the passenger's seat right ear position over the tire mounted with the sound measurement system to measure the vehicle interior noise as interpreted by the passengers in the vehicle. Two data acquisition systems coupled with a real-time Simulink model were used to collect all the measured data, one for the noise signals and the other for velocity and acceleration signals. The focus of the current study is to review different generation and amplification mechanisms of the structure-induced tire/road interaction noise and find the relevant dominant frequency ranges of the vehicle interior noise induced by the structure-borne noises using already established physics-based models and correlation techniques. It also aims to find correlations between tire acceleration, vehicle interior noise, and tire pavement interaction noise and their effect on different operational conditions like load, inflation pressure, and velocity. All the signals are studied in the time, frequency, and spectral domain and insights have been drawn on different tire/road noise generation and amplification mechanisms. / Master of Science / Structure-induced vehicle interior noise is one of the main concerns surrounding the automotive NVH industry and tire/road interaction noise being the most dominant source among the structure-borne noises affecting the vehicle interior noise is a major problem to the tire and automotive manufacturers nowadays. It leads to discomfort for the driver and the passengers in the vehicle and can cause fatigue, which in turn can directly affect the vehicle's safety. Several attempts have been made to reduce vehicle interior noise using statistical, physics-based, and hybrid models, but the research is still nowhere near completion. The current study aims to identify the frequency ranges affecting the structure-borne noise-induced vehicle interior noise and uses data-driven approaches in estimating the vehicle interior noise using only the acceleration of the tire. A test setup was designed and developed in-house where a tri-axial accelerometer embedded inside the inner liner of the tire measures the X, Y, and Z acceleration signals. Several microphones are instrumented at the tire/road contact surface and inside the passenger cabin to measure the tire/road interaction noise and the vehicle interior noise. The longitudinal velocity of the vehicle and the accelerations at the center of gravity of the vehicle have also been measured. Multiple data-driven models have been developed to directly predict the vehicle interior noise and tire/road interaction noise using the accelerometer data. This research is directly helpful for the automotive and tire industries by giving them insights on designing and developing quieter tires by using data-driven approaches and further using these with active control systems can mask the vehicle interior noise to acceptable levels in real-time.

Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic. / PHD / A lot of people think the car noise is mostly from the engine, exhaust, or wind. However, this is not true. The noise in the exterior mainly comes from tires at over 25 mph. At normal highway speed, e.g., 60 mph, tire noise contributes over 70% of total noise. A quiet tire is desired for driving comfort. A number of attempts to reduce tire noise have been made in tire industries, including the tread pattern optimization and the tire structure design. In this work, a model was developed to predict the tire noise based on the tread pattern, tire size, tread rubber hardness, and vehicle speed. The model is called Artificial Neural Network Model of Tire-Pavement Interaction Noise (ANN Model of TPIN, or AMOT). This model is able to predict the noise contributions from the tread pattern and the pavement separately. Tire companies can use the model to design quite tires while customers can have an insight on choosing quite tires based on the tread patterns and/or tire structure.