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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.

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

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

Accurate characterization and evaluation of tire/pavement friction is critical in assuring runway and highway safety. Historically, Pavement Friction Measurement Devices (PFMDs) employing different measuring mechanisms have been used to evaluate tire/pavement friction. They yield significantly disparate friction coefficients under the same contact conditions. Currently, an empirically developed data harmonization method based on a reference device (Dynamic Friction Tester (DFT)) is used in an attempt to overcome the disparities between the measurements using various different PFMDs. However, this method, which has been standardized by the American Society for Testing and Materials (ASTM E1960), has been criticized for its inconsistency by researchers and runway/highway operations personnel. The objective of this dissertation research was to develop a systematic and physically intuitive harmonization method for PFMDs that will improve the comparability of their data. As a foundation for such a harmonization, the LuGre tire model that employs physically meaningful parameters to represent the main attributes of tire/pavement friction was evaluated and validated. Measurements of tire/pavement friction by three widely used PFMDs; Locked Wheel Skid Trailer (LWST), Runway Friction Tester (RFT) and DFT, were accurately predicted using nonlinear optimization of LuGre model parameters. The LuGre model was found to be superior compared to the model used in the current ASTM E1960 standardization procedure for predicting PFMD measurements. A sensitivity analysis was performed to identify the relative significance of the LuGre model parameters in characterizing tire/pavement friction, and to study the effects of variation of those parameters on predicted frictional behavior. A set of laboratory tire experiments was designed and performed to validate the physical significance of LuGre tire model parameters and to study how they behave under typical load, inflation pressure, excitation frequency, and amplitude conditions. An empirical method was developed to accommodate the effects of water film thickness on tire/pavement friction in the LuGre model. The results of the sensitivity analysis and the experiments to directly estimate the model parameters were used to identify and quantify appropriate modifications to the measurement mechanisms of PFMDs that can be introduced to improve the comparability of their results. Friction experiments performed after introducing such modifications to the LWST showed an average reduction of 20% in the deviations between the results of LWST and RFT measurements. The research carried out in this dissertation is significant because it: (i) identified the deficiencies in the current method for harmonizing PFMD measurements and the underlying reasons for these deficiencies, (ii) emphasized the importance of a standardization approach that regulates the physical condition of PFMDs, in order to achieve universal comparability of tire/pavement friction measurements, (iii) validated that the LuGre tire model is a tire/pavement friction model capable of facilitating a better standardization approach, and, (iv) initialized the development of a physically meaningful harmonization procedure for PFMDs.

Induced vibrations due to tire-pavement interaction are one of the main sources of vehicle exterior noise, especially near highways and main roads where traveling speeds are above 50 kph. Its dominant spectral content is approximately within 500-1500 Hz. However, accurate prediction tools within this frequency range are not available. Current methods rely on structural modeling of the complete tire using finite elements and modal expansion approaches that are accurate only at low frequencies. Therefore, alternative physically-based models need to be developed. This work proposes a new approach that incorporates wave behavior along the tire's circumferential direction, while modes are assumed along its transversal direction. The formulation for new infinite plate and cylindrical shell structural models of a tire is presented. These are capable of accounting for orthotropic material properties, different structural parameters between the belt and sidewalls, inflation pressure, and rotation of the tire. In addition, a new contact model between the pavement and the tire is developed presented. The excitation of the tire due to the impact of the tread-pattern blocks in the contact patch region is characterized and coupled to the structure of the tire. Finally, a Boundary Element Method is implemented in order to compute the vibration-induced noise produced by the tire. All the modeling components are combined in a single prediction tool named Wave Pro Tire. Lastly, simulated responses and validation cases are presented in terms of harmonic responses, Frequency Response Functions (FRF), and produced noise. / Doctor of Philosophy / Induced vibrations due to tire-pavement interaction are one of the main sources of vehicle exterior noise, especially near highways and main roads where traveling speeds are above 50 kph. Accurate prediction tools are not currently available. Therefore, new physically based models need to be developed. This work proposes a new approach to model the tire’s structure with a formulation that accounts for multiple physical phenomena. In addition, a model that simulates the contact between the pavement and the tire’s tread is presented. Finally, the vibrations are coupled to the produced noise in a single prediction tool named Wave Pro Tire. This work also includes simulated responses and validation cases.

Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction. / Doctor of Philosophy / Multiple data driven models were developed in this study to use the vibration of the tirecontact patch as an input to sense some characteristics of road such as asperity, surface type,and the surface condition, and use them to predict the structure-borne noise power. Also,instead of measuring the noise using microphones, forces at wheel spindle were measuredas a metric for the noise power. In other words, a statistical model was developed that bysensing the road, and using the data along with other inputs, one can predict forces at thewheel spindle.