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.

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.

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.