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

Yes / Falls on stairs are a significant cause of morbidity and mortality in elderly people. A simple safety strategy to avoid tripping on stairs is increasing foot clearance. We determined whether a horizontal–vertical illusion superimposed onto stairs to create an illusory perceived increase in stair-riser height would increase stair ascent foot clearance in older participants. Methods.: Preliminary experiments determined the optimum parameters for the horizontal–vertical illusion. Fourteen older adults (mean age ± 1 SD, 68.5 ± 7.4 years) ascended a three-step staircase with the optimized version of the horizontal–vertical illusion (spatial frequency: 12 cycles per stair riser) positioned either on the bottom or top stair only, or on the bottom and top stair simultaneously. These were compared to a control condition, which had a plain stair riser with edge highlighters positioned flush with each stair-tread edge. Foot clearance and measures of postural stability were compared across conditions. Results.: The optimized illusion on the bottom and top stair led to a significant increase in foot clearance over the respective stair edge, compared to the control condition. There were no significant decreases in postural stability. Conclusions.: An optimized horizontal–vertical visual illusion led to significant increases in foot clearance in older adults when ascending a staircase, but the effects did not destabilize their postural stability. Inclusion of the horizontal–vertical illusion on raised surfaces (e.g., curbs) or the bottom and top stairs of staircases could improve stair ascent safety in older adults.

The objective for this project is to investigate various electrical and software means of control to support and advance the development of a novel vacuum adhesion system for a wall-climbing robot. The design and implementation of custom electronics and a wirelessly controlled real-time software system used to define and support the functionalities of these electronics is discussed. The testing and evaluation of the overall system performance and the performance of the several different subsystems developed, while working both individually and cooperatively within the system, is also demonstrated.

This thesis is devoted on passenger car tyres. Theoretical part covers description of production, construction and tyre parameters. Practical part is comprised experimental measurement of influence of tread grooves depth on achievable vehicle deceleration. First, it is focused on preparation conditions of measurement, with subsequent processing and evaluation of the data obtained.

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This thesis examines the representation of Italy and Italians in Henry James’ Daisy Miller and E.M. Forster’s Where Angels Fear to Tread. In a close reading of the two novels, with the use of the theory of the Triangle of Representation and the concept of Imperialist nostalgia, recurring themes in connection to the Grand Tour have been found. While both novels have already been studied in connection to the concepts of freedom, family, and homosexuality, not much has been written about the novels in connection to the Grand Tour studies and representation. Divided into three sections, this paper first defines representation and Christopher Prendergast’s Triangle of Representation, both in close connection to the concept of the gaze. Then, it gives a short historical context of the Grand Tour, to give the reader the necessary background for understanding both of the analysed novels. It then continues explaining the idea of nostalgia and, more specifically, Renato Ronaldo’s concept of Imperialist nostalgia. Finally, the paper analyses the two novels in detail in connection to the mentioned theory, connecting the two authors to the Grand Tour and their experiences in Italy.

During the last decade, a novel methodology for wheel wear simulation has been developed in Sweden. The practical objective of this simulation procedure is to provide an integratedengineering tool to support rail vehicle design with respect to wheel wear performance and detailed understanding of wheel-rail interaction. The tool is integrated in a vehicle dynamicssimulation environment.The wear calculation is based on a set of dynamic simulations, representing the vehicle, the network, and the operating conditions. The wheel profile evolution is simulated in an iterativeprocess by adding the contribution from each simulation case and updating the profile geometry.The method is being validated against measurements by selected pilot applications. To strengthen the confidence in simulation results the scope of application should be as wide aspossible in terms of vehicle classes. The purpose of this thesis work has been to try to extend the scope of validation of this method into the light rail area, simulating the light rail vehicleA32 operating in Stockholm commuter service on the line Tvärbanan.An exhaustive study of the wear theory and previous work on wear prediction has been necessary to understand the wear prediction method proposed by KTH. The dynamicbehaviour of rail vehicles has also been deeply studied in order to understand the factors affecting wear in the wheel-rail contact.The vehicle model has been validated against previous studies of this vehicle. Furthermore new elements have been included in the model in order to better simulate the real conditionsof the vehicle.Numerous tests have been carried out in order to calibrate the wear tool and find the settings which better match the real conditions of the vehicle.Wheel and rail wear as well as profile evolution measurements were available before this work and they are compared with those results obtained from the simulations carried out.The simulated wear at the tread and flange parts of the wheel match quite well the measurements. However, the results are not so good for the middle part, since themeasurements show quite evenly distributed wear along the profile while the results from simulations show higher difference between extremes and middle part. More tests would benecessary to obtain an optimal solution.

Tires, being the only component of the vehicle in contact with the road surface, are responsible for generating the forces for maintaining the vehicle pose, orientation and stability of the vehicle. Additionally, the on-board advanced chassis control systems require estimation of these tire-road interaction properties for their operation. Extraction of these properties becomes extremely important in handling limit maneuvers such as Double Lane Change (DLC) and cornering wherein the lateral force transfer is dependent upon these computations. This research focuses on the development of a high-fidelity vehicle-tire model and control algorithm framework for vehicle trajectory tracking for vehicles operating in this limit handling regime. This combined vehicle-tire model places an emphasis on the lateral dynamics of the vehicle by integrating the effects of relaxation length on the contact patch force generation. The vertical dynamics of the vehicle have also been analyzed, and a novel double damper has been mathematically modeled and experimentally validated. Different control algorithms, both classical and machine learning-based, have been developed for optimizing this vertical dynamics model. Experimental data has been collected by instrumenting a vehicle with in-tire accelerometers, IMU, GPS, and encoders for slalom and lane change maneuvers. Different state estimation techniques have been developed to predict the vehicle side slip angle, tire slip angle, and normal load to further assist the developed vehicle-tire model. To make the entire framework more robust, Machine Learning algorithms have been developed to classify between different levels of tire wear. The effect of tire tread wear on the pneumatic trail of the tire has been further evaluated, which affects the aligning moment and lateral force generation. Finally, a Model Predictive Control (MPC) framework has been developed to compare the performance between the conventional vehicle models and the developed vehicle models in tracking a reference trajectory. / Doctor of Philosophy / In our rapidly advancing world, self-driving or autonomous vehicles are no longer a vision of the future but a reality of today. As we grow more reliant on these vehicles, ensuring their safety and reliability becomes increasingly critical. Unlike traditional vehicles, self-driving cars operate without human intervention. Consequently, the onus of passenger and pedestrian safety falls squarely on the vehicle's control systems. The efficiency and effectiveness of these control systems are pivotal in preventing accidents and ensuring a smooth ride. One vital aspect of these control systems lies in understanding the tires' behavior, the only parts of the vehicle that are in contact with the road surface. A tire's interaction with the road surface significantly impacts the vehicle's handling and stability. Information such as how much of the tire is in contact with the road, the forces and moments generated at this contact point, becomes valuable for optimizing the vehicle's performance. This is particularly crucial when a vehicle is turning or cornering, where the forces developed between the tires and the road are key to maintaining control and stability. In this research, a framework has been designed to improve the vehicle performance, primarily by improving the modeling of tire lag dynamics. This refers to the delay or 'lag' between a change in tire conditions (such as pressure, wear, and temperature) and the corresponding change in tire behavior. In addition, in this research a vertical dynamics model of the vehicle has also been developed incorporated with a novel double damper suspension system. To complete the entire framework, the effect of tire wear over time and how this affects its performance and safety characteristics has also been examined. By estimating and understanding this wear, we can predict how it will affect the dynamic properties of the tire, thus improving the reliability and efficiency of our autonomous vehicles. The last piece of this framework comprises the development of an MPC controller to track a reference trajectory and evaluate the performance of the developed model.

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

Tire parameters play a very important role in tire performance. Depending on the driving conditions for which a given tire is designed, its parameters must be chosen appropriately (e.g., the radius of the tire, the width of the tire, material properties of different sections). Among tire characteristics, the material properties of the rubber compounds have a vital role in tire behavior. Previous studies show that the material properties of the rubber are highly dependent on temperature. Thus, a comprehensive study on the effect of the material properties of the rubber on tire performance for different temperatures as well as different road conditions is required. In this study, a theoretical model has been developed for tire-ice interaction. The temperature changes obtained from the model are used to calculate the height of the water film created by the heat generated due to the friction force. Next, the viscous friction coefficient at the contact patch is obtained. By using the thermal balance equation at the contact patch, dry friction is obtained. Knowing the friction coefficients for the dry and wet regions, the equivalent friction coefficient is calculated. The model has been validated using experimental results for three similar tires with different rubber compounds properties. For the experimental part of this study, four tires have been selected for testing. Three of them have identical tire geometry and structure but different rubber tread compounds. Several tests were conducted for the chosen tires in three modes: free-rolling, braking, and traction. The tests were performed for two different normal loads (4 kN and 5.6 kN), two different inflation pressures (21 psi (144.8 kPa) and 28 psi (193 kPa)), and three tire temperatures levels (-10°C, -5°C, and -1 °C). The Terramechanics Rig at TMVS at Virginia Tech has been used for conducting the tests. The results from this study show the sensitivity of the magnitude of the tractive force with respect to parameters such as tire temperature, normal load, etc. The results also indicate that the tire with the lowest value of the Young modulus has the highest traction among all four tires used in this study. The model developed can be used to predict the temperature changes at the contact patch, the tire friction force, the areas of wet and dry regions, the height of the water film for different ice temperatures, different normal loads, etc. The results from this study coincide with the obtained results from the experiments. According to the data available, tire B with the smallest value of Young modulus and the smallest value of the specific heat parameter was shown to have the highest friction coefficient in both simulation and experiment. After validating the results using experimentally collected data, the model was used to perform a sensitivity analysis on the tire performance with respect to six material properties of the tread rubber: thermal conductivity, rubber density, Young's modulus, specific heat, roughness parameter of the rubber, and radii of spherical asperities of the rubber. The results from this study show the sensitivity of the magnitude of the friction coefficient to the rubber material properties. The friction coefficient has a direct relationship with the density of the rubber and has an inverse relationship with Young's modulus, specific heat, and roughness parameter. / Doctor of Philosophy / In order to decrease the number of deaths and injuries caused by driving on icy roads and increase the safety of the vehicle, it is important to improve the tire performance on ice. To this, understanding the effects of different tire and road parameters such as material properties of the rubber, loading condition, and temperature on the tire-ice performance is required. Tire parameters play a very important role in tire performance. Depending on the driving conditions for which a given tire is designed, its parameters must be chosen appropriately In this project, the effects of different tire and terrain parameters such as rubber material properties on tire performance on ice using an experimental and modeling approach have been studied. For the experimental part of this study, several tests were conducted for more than 30 tires with different material properties. The results of this study show what are the most important material properties of the rubber for designing a tire with the best performance on ice. For the modeling part of this study, a semi-analytical model was developed. The model was validated using collected experimental data and was used to predict the performance of the tire by having information about its material and physical properties. The developed model called ATIIM2.0 has several advantages. First, it is a unique model for a complete tire (not a rubber block) that can be used to predict the performance of the tire by using its material properties. In addition, this model can be connected to vehicle models to improve the performance of the vehicle in general. The model developed can be used to predict the temperature changes at the contact patch, the tire friction force, the areas of wet and dry regions, the height of the water film for different ice temperatures, different normal loads, etc. The results from this study coincide with the obtained results from the experiments.