Recent technological innovations and the need for increased safety on the world's roads have led to the introduction of In- Vehicle Information Systems (IVIS). These systems will provide navigation and advisory information to drivers while they are driving. One aspect of these systems, In-vehicle Signing Information Systems (ISIS), would provide the warning, regulatory, and advisory information that is currently found on road signs. These systems may be of particular benefit when external elements such as rain, snow, or night driving reduce or eliminate the opportunity for drivers to detect road signs. This study attempts to determine what benefits, if any, are realized by drivers using this system. Fifty-eight drivers operated an instrumented Oldsmobile Aurora under eight conditions. The eight conditions consisted of a daylight-clear weather-ISIS condition, a daylight-clear weather-No ISIS condition, a daylight-rain-ISIS condition, a daylight-rain-No ISIS condition, a night-clear weather-ISIS condition, a night-clear weather-No ISIS condition, a night-rain-ISIS condition, and a night-rain-No ISIS condition. Younger drivers (18-30 years old) and older drivers (65 years or older) took part in this study. Three measures of driver performance were collected along with subjective preference data. Each measure was evaluated in order to determine what impact, if any, weather, time of day, age, and ISIS use had on performance. Subjective data was evaluated to determine driver preference and acceptance of the ISIS display. The results indicated that use of the ISIS display led to reduced speeds and greater reaction distances for all drivers. Evidence was found that seems to indicate that older drivers may receive a greater benefit in complex, unfamiliar, or low visibility situations. Evidence was also found that indicates that all drivers may receive a greater benefit at night for the complex or unfamiliar situations. Subjectively, the majority of the drivers indicated that the ISIS display made them more aware of road sign information. / Master of Science

Although gas turbine engines are designed to use dry air as the working fluid, the great demand over the last decades for air travel at several altitudes and speeds has increased aircraft’s exposure to inclement weather conditions. Although, they are required to perform safely under the effect of various meteorological phenomena, in which air entering the engine contains water, several incidents have been reported to the aviation authorities about power loss during flight at inclement weather. It was understood that the rain ingestion into a gas turbine engine influences the performance of the engine and particular the compressor and the combustor. The effects of water ingestion on gas turbine engines are aerodynamic, thermodynamic and mechanical. These effects occur simultaneously and affect each other. Considering the above effects and the fact that they are timedependent, there are few gas turbine performance simulation tools, which take into account the water ingestion phenomenon. This study is a new research of investigating theoretically the water ingestion effects on a gas turbine performance. It focuses on the aerodynamic and mechanical effects of the phenomenon on the compressor and the combustor. The application of Computational Fluid Dynamics (CFD) is the basic methodology to examine the details of the flow in an axial compressor and how it is affected by the presence of water. The calculations of water film thickness, which is formed on the rotor blade, its motion (direction and speed) and the extra torque demand, are provided by a code created by the author using FORTRAN programming language. Considering the change in blade’s profile and the wavy characteristics of the liquid film, the compressor’s performance deterioration is calculated. The compressor and combustor’s deterioration data are imported to a gas turbine simulation code, which is upgraded to calculate overall engine’s performance deterioration. The results show a considerable alteration in engine’s performance parameters and arrive at the same conclusions with the relevant experimental observations.

Although gas turbine engines are designed to use dry air as the working fluid, the great demand over the last decades for air travel at several altitudes and speeds has increased aircraft’s exposure to inclement weather conditions. Although, they are required to perform safely under the effect of various meteorological phenomena, in which air entering the engine contains water, several incidents have been reported to the aviation authorities about power loss during flight at inclement weather. It was understood that the rain ingestion into a gas turbine engine influences the performance of the engine and particular the compressor and the combustor. The effects of water ingestion on gas turbine engines are aerodynamic, thermodynamic and mechanical. These effects occur simultaneously and affect each other. Considering the above effects and the fact that they are timedependent, there are few gas turbine performance simulation tools, which take into account the water ingestion phenomenon. This study is a new research of investigating theoretically the water ingestion effects on a gas turbine performance. It focuses on the aerodynamic and mechanical effects of the phenomenon on the compressor and the combustor. The application of Computational Fluid Dynamics (CFD) is the basic methodology to examine the details of the flow in an axial compressor and how it is affected by the presence of water. The calculations of water film thickness, which is formed on the rotor blade, its motion (direction and speed) and the extra torque demand, are provided by a code created by the author using FORTRAN programming language. Considering the change in blade’s profile and the wavy characteristics of the liquid film, the compressor’s performance deterioration is calculated. The compressor and combustor’s deterioration data are imported to a gas turbine simulation code, which is upgraded to calculate overall engine’s performance deterioration. The results show a considerable alteration in engine’s performance parameters and arrive at the same conclusions with the relevant experimental observations.

A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace.

Vehicle crashes remain the leading cause of accidental death and injuries in the United States, claiming tens of thousands of lives and injuring millions of people each year. Many of these crashes occur during nighttime, where a variety of modifiers affect the risk of a crash, primarily through the reduction of object visibility. Furthermore, many of these modifiers also affect the nighttime mobility of older drivers, who avoid driving during the nighttime. Thus, a two-fold need exists for new technologies that enhance night visibility. Two separate studies were completed as part of this research. Study 1 served as a baseline by evaluating visual performance during nighttime driving under clear weather conditions. Visual performance was evaluated in terms of the detection and recognition distances obtained when different vision enhancement systems were used at the Smart Road testing facility. Study 2, also using detection and recognition distances, compared the visual performance of drivers during low visibility conditions (i.e., due to rain) to the risk perception of driving during nighttime under low visibility conditions. These comparisons were made as a function of various vision enhancement systems. The age of the driver and the characteristics of the object presented (e.g., contrast, motion) were variables of interest in both studies. The pivotal contribution of this investigation is the generation of a model describing the relationships between driver characteristics, risk perception, and visual performance in nighttime driving in the context of a variety of standard and prototype vision enhancement systems. Improvement of mobility, especially for older individuals, can be achieved through better understanding of the factors that increase risk perception, identification of systems that improve detection and recognition distances, and consideration of drivers' opinions on possible solutions that improve nighttime driving safety. In addition, this research effort empirically described the night vision enhancement capabilities of 12 different vision enhancement systems during clear and adverse weather environments. / Ph. D.

<p dir="ltr">Tornadoes have caused billions of dollars in damage and are one of the leading causes of weather-related deaths in the United States each year. Recent studies have suggested spatial shifts in tornado activity, though the reason is unclear. Extratropical cyclones (ETCs), which are strongly associated with the jet stream, are known to produce an environment favorable for tornadoes in their warm sector. However, little recent research has been done on the spatiotemporal relationship between tornadoes and ETCs, so there is a poor understanding of whether or not the changes in tornado activity are affected by ETC patterns. ERA5 reanalysis, ETC tracking, and historical tornado data from 1980-2022 are used to analyze the climatology of tornadoes relative to ETCs. We found that 73% of F/EF1+ tornadoes occurred within 2000km of an ETC and are likely associated with the ETC. Most of those tornadoes occurred near the median position around 465km away from and to the southeast of the ETC center. Of those tornadoes, 68% occurred in large outbreaks of 6 or more tornadoes, where most tornadoes formed closer to and to the southeast of an ETC track as compared to small outbreak and isolated tornadoes. The spatial and relative distributions were similar across all intensity levels, though stronger tornadoes tended to have more tornadoes directly to the southeast of an ETC. Seasonal variances in tornadoes strongly corresponded with seasonal changes in the jet stream. Summer tornadoes occurred in northern portions of the US when the jet stream shifts poleward. The jet stream and ETCs are also weakest in the summer, and the weakest association was found in summer tornadoes based on their distribution relative to ETCs being more uniform towards the northeast and north-southeast directions. Winter tornadoes occurred in more southern portions of the US when the jet stream shifts equatorward, and they had a stronger association with most of the tornadoes occurring to the southeast and closer to the ETC center, aligning with a strong ETC and jet stream in the winter. Finally, tornadoes and ETCs had strong spatial covariance and showed similar linear trends, including a similar rate of change in the eastward shift, providing strong evidence that a shift in ETCs may be driving the shift in tornadoes. Furthering our understanding of the relationship between tornadoes and ETCs will help to better predict how tornadoes will change in the future based on changes in ETCs.</p>

Rainfall affects the performance of traffic operations and endangers safety. A common and conventional method (rain gauges) for rainfall measurements mostly provide precipitation records in hourly and 15-minute intervals. However, reliability, continuity, and wide area coverage pose challenges with this data collection method. There is also a greater likelihood for data misrepresentation in areas where short duration rainfall is predominant, i.e., reported values may not reflect the actual equivalent rainfall intensity during subintervals over the entire reporting period. With recent weather and climate patterns increasing in severity, there is a need for a more effective and reliable way of measuring rainfall data used for traffic analyses. This study deployed the use of precipitation radar data to investigate the spatiotemporal effect of rainfall on freeways in Jacksonville, Florida. The linear regression analysis suggests a speed reduction of 0.75%, 1.54%, and 2.25% for light, moderate, and heavy rainfall, respectively. Additionally, headways were observed to increase by 0.26%, 0.54%, and 0.79% for light, moderate, and heavy rainfall, respectively. Measuring precipitation from radar data in lieu of using rain gauges has potential for improving the quality of weather data used for transportation engineering purposes. This approach addresses limitations experienced with conventional rain data, especially since conventional collection methods generally do not reflect the spatiotemporal distribution of rainfall.

Adverse weather is considered as one of the important factors contributing to injuries and severe crashes. During rainy conditions, it can reduce travel visibility, increase stopping distance, and create the opportunity hydroplaning. This study quantified the relative crash risk on Oregon 217 southbound direction under rainy conditions by using a match-paired approach, applied one-year traffic data, crash data and NEXRAD Level II radar weather data. There are 26 crashes occurred in match-paired weather conditions for Oregon 217 in year 2007. The results of this study indicate that a higher crash risk and a higher property-damage-only crash risk occurred during rainy days. The crash risk level varies by the location of the highway, at milepost 2.55 station SW Allen Blvd has the highest driving risks under rainy conditions.

<p> It has been hypothesized that some measurable properties of $Z_{DR}$ arcs in supercells may change in the minutes prior to tornadogenesis and tornadogenesis failure, and that $Z_{DR}$ arc area will change with SRH and can be used as a real-time proxy to estimate SRH. Output form the Cloud Model 1 (CM1) along with a polarimetric emulator is used to simulate $Z_{DR}$ arcs in 9 tornadic and 9 non-tornadic supercells. A random forest algorithm is used to automatically identify the $Z_{DR}$ arcs. Finally the inflow sector SRH is calculated at times when $Z_{DR}$ arcs are identified. To analyze the change in intensity and area a comparison between the average $Z_{DR}$ value inside and outside of the arc, as well as the spatial size of the arc and storm was done. Model calculated SRH is then compared to these metrics.</p> <p> </p> <p> It has also been observed that hail fallout complicates the automatic identification of $Z_{DR}$ arcs. In this study, three experiments are run where the simulated $Z_{DR}$ arcs are produced. One using all categories of hydrometeors, one where wet growth and melting of hail is excluded, and one excluding the contribution to $Z_{DR}$ from the hail hydrometeor category. The same analysis as above is repeated for all three experiments. Finally observed $Z_{DR}$ arcs are analyzed to see if these results are applicable to the real world. </p>

A new approach to the evaluation of the reliability of electrical systems is presented. In this approach a Graph Trace Analysis based approach is applied to integrated system models and reliability analysis. The analysis zones are extended from the traditional power system functional zones. The systems are modeled using containers with iterators, where the iterators manage graph edges and are used to process through the topology of the graph. The analysis provides a means of computationally handling dependent outages and cascading failures. The effects of adverse weather, time-varying loads, equipment age, installation environment, operation conditions are considered. Sequential Monte Carlo simulation is used to evaluate the reliability changes for different system configurations, including distributed generation and transmission lines. Historical weather records and loading are used to update the component failure rates on-the-fly. Simulation results are compared against historical reliability field measurements. Given a large and complex plant to operate, a real-time understanding of the networks and their situational reliability is important to operational decision support. This dissertation also introduces using an Integrated System Model in helping operators to minimize real-time problems. A real-time simulation architecture is described, which predicts where problems may occur, how serious they may be, and what is the possible root cause. / Ph. D.