Turbine blades are critical to the performance of an aircraft engine and their life is central to the integrity of the engine. These blades, when manufactured, inevitably exhibit some deviations in shape from the desired design specifications as a result of manufacturing variability. An approach to characterizing these deviations may be made by analysing the blade measurements for any changes from the datum design values. The measurement data, is however, always affected by measurement errors that cloud these effects. In the present study, a methodology is proposed that employs the probabilistic data analysis techniques of Principal Component Analysis (PCA) and Fast Fourier Transform (FFT) analysis for de-noising the measurement data to capture the underlying effects of manufacturing variability as manufacturing drift with time and blade to blade manufacturing error. An approach using dimensionality reduction in the case of PCA and sub-selecting Fourier coeffcients in the case of FFT is proposed that uses prior knowledge on the measurement error. A Free-Form Deformation (FFD) based methodology is then presented for characterizing the 3-dimensional (3-d) geometric variability in blade shapes from the limited number of available measurements. This is followed by the application of a linear elasticity based approach for generating and morphing 3-d volume meshes in FEA ready form. A finite element analysis (FEA) of the resulting probable blade shapes indicates that the presence of manufacturing variability reduces their mean life by about 1.7% relative to the nominal design with a maximum relative reduction in life of around 3.7%. The probabilistic estimates of manufacturing perturbations are employed for robust design studies with the objectives of maximizing the mean and nominal lives and minimizing the blade life variability. A comparison of the robustoptimal solution with an optimal deterministic design is also performed. The designs explored by the multiobjective optimization process are analysed to understand the effects of geometric changes in turbine blades on the nominal values of life and the variations in blade life.

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Carbon emissions from road transport make up 20% of the total greenhouse gas emissions in the UK. Therefore, reducing carbon emissions from road transport is significant in reaching carbon reduction targets. In urban areas where signal controlled intersections are common, carbon emissions from vehicular traffic can be aggravated by aggressive driving and interruptions induced by traffic control. Considerable variations in speed and acceleration profiles could be observed between high carbon and low carbon driving. In view of the immediate effects that changing driving behaviour could have on carbon emissions without extra cost, this study had investigated the variations in carbon emissions at signalised intersection, which includes the scale of impacts of changing driving behaviour and flow interruption on carbon emissions. Characteristics which lead to high CO2 emissions could then be modified by addressing the behavioural change and control strategies. High frequency real world driving data was collected using the TRG highly instrumented vehicle. The vehicle was equipped with a number of on-board systems, i.e., on-board emission measurement system, velocity box, on-board diagnostic unit, Dashdyno and video recorder. Aggressive and economical driving styles observed for two drivers during initial tests showed distinct differences in terms of speed profiles and fuel consumption. These initial tests were used to examine the nature and scale of potential impacts on fuel consumption and to design main field tests. Natural driving observed from twenty nine drivers from the main field tests also showed significantly different levels of carbon emissions at signalised intersections, which were caused by variations in both driving behaviour and traffic control. In terms of driving behaviour, changing the worst driving to the best driving during interrupted driving was found to reduce CO2 emissions significantly. The carbon reductions were collectively contributed by 1) applying soft acceleration and keeping acceleration below 0.6m/s2 during the acceleration mode and 2) reducing leaving speed at intersections, 3) practising smooth deceleration and stable speed during the deceleration mode and 4) applying the idle-stop system. Carbon emission rates of different vehicles may vary from one to another. However, it was found that the amount of carbon savings demonstrated in this study could be possibly achieved by other internal combustion vehicles of the same class, and by hybrid electric vehicles to a lesser extent. In this study, changing driving behaviour is recommended as a cost effective way to achieve carbon reduction.

Low-frequency Earth-horizontal translational and rotational oscillations can cause motion sickness in transport. Previous studies have found that motion sickness depends on the frequency, magnitude, direction and duration of the motion, however, knowledge of the mechanisms of motion sickness is far from complete. The concept of sensory conflict – that motion sickness arises because of a conflict between sensed and expected sensory information is central to theories of motion sickness, but little is known about how the physical characteristics of motion influence sensed and expected sensory signals. The aim of this research was to advance understanding of the effect on motion sickness of factors which may influence sensed and expected vestibular signals during exposure to low-frequency translational and/or rotational oscillation. The first experiment investigated whether motion sickness depends on the phase between combined lateral acceleration and roll oscillation at 0.2 Hz. The roll oscillation had one of four phases relative to the lateral acceleration: 0° delay, 14.5° delay, 29° delay, and 29° advance. Sickness decreased as the delay in the roll motion increased; less sickness occurred with a phase advance than a phase delay, suggesting that motion sickness cannot be predicted from the acceleration in the plane of the seat. The second experiment investigated how motion sickness varies between four 60-minute exposures of 0.1 Hz combined lateral and roll oscillation which involved different combinations of a high and low magnitude motion: LLLL, HHHH, LHHL and HLHL. The high magnitude motion produced greater sickness than the low magnitude motion. For the two variable motion conditions, there was no significant difference in accumulated illness ratings when the motion sickness dose values were the same. In the third experiment, 0.2 Hz roll and pitch oscillation were studied at three displacements: ±1.83° ±3.66° or ±7.32°. A trend for motion sickness to increase with increasing displacement was observed; similar sickness was caused by roll and pitch oscillation at each magnitude. In the fourth experiment, subject head displacement was measured during 0.2 Hz fore-and-aft oscillation with and without a backrest at three magnitudes: 0.22, 0.44, and 0.89 ms-2 r.m.s. Illness increased systematically with increasing magnitude of oscillation with a backrest, but less systematically without a backrest, suggesting an interaction between the effect of motion magnitude and the influence of a backrest. There were no significant differences in illness with or without a backrest at any of the magnitudes studied. Between subjects, there was little evidence to suggest that greater fore-and-aft and pitch displacement of the head was associated with an increase in motion sickness. Combined findings from the third and fourth experiments suggest that 0.2 Hz fore-and-aft oscillation causes greater sickness than 0.2 Hz pitch oscillation at each of the three magnitudes studied (assuming that pitch motion can be represented by the gravitational component, gSinθ). A motion sickness model is proposed showing how the factors investigated in this thesis affect the sensed and expected semi-circular canal signals which are assumed to be involved in the causation of motion sickness. The model predicts how sensed and expected signals vary according to the phase between motions, the magnitude, direction and duration of motion, the type of motion and the postural support given to subjects. Explanations of how the model predicts motion sickness based on the findings of this study and previous studies are discussed.

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In preparation for changes in pavement design methodologies and to begin to assess the effectiveness of the weigh-in-motion (WIM) system in Utah, the Utah Department of Transportation (UDOT) contracted with a Brigham Young University (BYU) research team to conduct an evaluation of their commercial motor vehicle (CMV) data collection system statewide. The objective of this research was to evaluate the CMV data collection program in the state of Utah and to make limited recommendations for potential improvements and changes that will aid in more detailed and accurate CMV data collection across the state. To accomplish the research objectives, several tasks were conducted, including: 1) a review of literature to establish the state-of-the-practice for CMV monitoring, 2) collection of WIM data for the state of Utah, 3) analysis of the collected WIM data, 4) development of a calibration methodology for use in the state, and 5) presentation of recommendations and conclusions based on the research. The analysis of collected WIM data indicated that the CMV data collection system in the state of Utah currently produces data consistent with expectations with a few exceptions. Recommendations for improvements to the CMV data collection system come in the form of a proposed calibration methodology that is in line with current standards and the practices in other states. The proposed calibration methodology includes calibration, verification, and a quality assurance programs.

Heavy vehicle weights must be closely monitored to prevent fatigue-induced deterioration and critical fracture to civil infrastructure, among many other purposes. Developing a cost-effective weigh-in-motion (WIM) system remains challenging. This doctoral research describes the creation and experimental validations of a computer vision-based non-contact vehicle WIM system. The underlining physics is that the force exerted by each tire onto the roadway is the product of the two key vehicle parameters: tire-roadway contact pressure and contact area. Computer vision is applied (1) to measure the several tire parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire pneumatic pressure can be found. Consequently, a computer vision system is developed in this research. The computer vision system comprises a camera and computer vision software/techniques for measuring the tire parameters and recognizing the tire sidewall markings from individual tire images of a moving vehicle. Computer vision techniques, such as edge detection and optical character recognition (OCR), are applied to enhance the measurements and recognition accuracy. Numerous laboratory and field experiments were conducted on a sport utility vehicle and fully loaded or empty concrete trucks to demonstrate the feasibility of this novel method. The vehicle weights estimated by this novel computer vision-based non-contact vehicle WIM system agreed well with the curb weights verified by static weighing, demonstrating the potential of this computer vision-based method as a non-contact means for weighing vehicles in motion. To further illustrate and exemplify the versatility of this novel computer vision-based WIM system, this research explores the potential application capability of the system for structural health monitoring (SHM) in civil engineering. This work aims to investigate the potential of this proposed and prototyped computer vision-based vehicle WIM system to acquire vehicle weight and location information as well as to obtain corresponding bridge responses simultaneously for later structural model updating analysis and damage detection and identification framework. In order to validate the concept, a laboratory vehicle-bridge model was constructed. Subsequently, numerous experiments were carried out to demonstrate how the computer vision-based WIM system can be utilized as a resourceful application to (1) extract bridge responses, (2) estimate vehicle weight, and (3) localize the input force simultaneously. This doctoral research delivers a unique, pioneering, and innovative design and development of a computer vision-based non-contact vehicle WIM method and system that can remotely perform vehicle weight estimation. It also demonstrates a novel application of computer vision technology to solve challenging weigh-in-motion (WIM) and civil engineering problems.

Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness. / Master of Science

In 2004, 5,190 people were killed due to a traffic accident involving a commercial motor vehicle (CMV), up from 4,793 people killed in 2001 (Traffic Safety Facts, 2004; Traffic Safety Facts, 2001). Driver drowsiness is an important issue to consider when discussing CMVs. According to the FMCSA, over 750 people are killed and 20,000 people are injured each year due to drowsy CMV drivers (as cited in Advocates for Highway and Auto Safety, 2001). Driver drowsiness is an important issue for CMV drivers for several reasons, including long work shifts, irregular schedules and driving long hours on interstates and highways with no scenic interruptions to help keep the driver alert. Because of these and other factors, including the high mileage exposure that CMV drivers face, drowsiness is an important issue in a CMV driver's occupation. There were two main goals to this research: 1) gain a better understanding of the time-related occurrences of drowsy-related critical incidents (i.e., crashes, near-crashes and crash-relevant conflicts), and 2) obtain drivers' opinions of the 2004 Revised Hours-of-Service regulations. To do this, recent data were used from a Field Operational Test conducted by the Virginia Tech Transportation Institute in which 103 participants drove in an instrumented heavy vehicle for up to 16 weeks; video data, and sensor data were collected from each participant. In addition, actigraph data was collected from 96 of the 103 participants. Each vehicle was instrumented with four video cameras to capture images of the drivers face, the forward roadway, and the adjacent lanes on each side of the truck. In addition, multiple sensors were installed in the vehicle in order to collect data such as the driver's speed, braking patterns and steering wheel movement. These data were combined to provide a complete picture of each driver's environment and behavior while they drove their normal routes. Data analysts reviewed the data for critical incidents (crashes, near-crashes, and crash-relevant conflicts) and determined a drowsiness level for each incident; these downiness levels were compared to drowsiness levels of baseline incidents (i.e., normal driving periods). The results show that drivers were more likely to have a drowsy-related critical incident between 2:00 pm and 2:59 pm. In addition to the video and sensor data, each driver was asked to fill out a subjective questionnaire regarding the revised HOS regulations. Drivers preferred the revised HOS regulations over the old HOS regulations and the number one item that was preferred in the revised HOS regulations is the 34-hour restart which allows drivers to restart their work week by taking off 34 consecutive hours. / Master of Science

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