Reducing the number of motor vehicle crashes is one of the major challenges of our times. Current strategies to reduce crash rates can be divided into two groups: identifying risky driving behavior prior to crashes to proactively reduce risk and automating some or all human driving tasks using intelligent vehicle systems such as Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). For successful implementation of either strategy, a deeper understanding of human driving behavior is essential. This dissertation characterizes human driving behavior through an analysis of a large naturalistic driving study and offers four major contributions to the field. First, it describes the creation of the Surface Accelerations Reference, a catalog of all longitudinal and lateral surface accelerations found in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). SHRP 2 NDS is the largest naturalistic driving study in the world with 34.5 million miles of data collected from over 3,500 participants driving in six separate locations across the United States. An algorithm was developed to detect each acceleration epoch and summarize key parameters, such as the mean and maxima of the magnitude, roadway properties, and driver inputs. A statistical profile was then created for each participant describing their acceleration behavior in terms of rates, percentiles, and the magnitude of the strongest event in a distance threshold. The second major contribution is quantifying the effect of several factors that influence acceleration behavior. The rate of mild to harsh acceleration epochs was modeled using negative binomial distribution-based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as fixed effects, and a unique participant identifier was as the random effect. Subcategories of each fixed effect were compared using incident rate ratios. Roadway speed category was found to have the largest effect on acceleration behavior, followed by driver age, vehicle class, and location. This methodology accounts for the major influences while simultaneously ensuring that the comparisons are meaningful and not driven by coincidences of data collection. The third major contribution is the extraction of acceleration-based long-term driving styles and determining their relationship to crash risk. Rates of acceleration epochs experienced on ≤ 30 mph roadways were used to cluster the participants into four groups. The metrics to cluster the participants were chosen so that they represent long-term driving style and not short-term driving behavior being influenced by transient traffic and environmental conditions. The driving style was also correlated to driving risk by comparing the crash rates, near-crash rates, and speeding behavior of the participants. Finally, the fourth major contribution is the creation of a set of interactive analytics tools that facilitate quick characterization of human driving during regular as well as safety-critical driving events. These tools enable users to answer a large and open-ended set of research questions that aid in the development of ADAS and ADS components. These analytics tools facilitate the exploration of queries such as how often do certain scenarios occur in naturalistic driving, what is the distribution of key metrics during a particular scenario, or what is the relative composition of various crash datasets? Novel visual analytics principles such as video on demand have been implemented to accelerate the sense-making loop for the user. / Doctor of Philosophy / Naturalistic driving studies collect data from participants driving their own vehicles over an extended period. These studies offer unique perspectives in understanding driving behavior by capturing routine and rare events. Two important aspects of understanding driving behavior are longitudinal acceleration, which indicates how people speed up or slow down, and lateral acceleration, which shows how people take turns. In this dissertation, millions of miles of driving data were analyzed to create an open access acceleration database representing the driving profiles of thousands of drivers. These profiles are useful to understand and model human driving behavior, which is essential for developing advanced vehicle systems and smart roadway infrastructure. The acceleration database was used to quantify the effect of various roadway properties, driver demographics, vehicle classification, and environmental factors on acceleration driving behavior. The acceleration database was also used to define distinct driving styles and their relationship to driving risk. A set of interactive analytics tools was developed that leverage naturalistic driving data by enabling users to ask a large set of questions and facilitate open-ended analysis. Novel visualization and data presentation techniques were developed to help users extract deeper insight about driving behavior faster than previously exiting tools. These tools will aid in the development and testing of automated driving systems and advanced driver assistance systems.

Occupational accidents are a main source of traumatic brain injuries (TBIs), with TBIs accounting for a substantial portion of all work-related deaths. Motor vehicle accidents and falls are consistently leading causes of head injury and fatality across industries. These injuries can have serious long-term consequences on an individual's quality of life and lead to large economic costs within society. This thesis investigated sources of occupational TBI prevention within two industries, construction and professional motorsports. In the last twenty years there have been major safety advancements within these industries, and yet the risk of TBI still exists. There is a need for safety standards that better reflect real-world injury scenarios. First, this thesis considered improvements to construction hard hat safety standards by evaluating the ability of Type 1 and Type 2 hard hats to reduce head injuries due to falls. Hard hats were evaluated over a range of real-world fall heights and three impact locations, using a twin-wire drop tower. Linear acceleration was used to predict injury risks. Type 2 hard hats substantially reduced skull fracture and concussion risk when compared to Type 1, indicating that if more workers wore Type 2 hard hats the risk of severe head injuries in the construction industry would be reduced. Next, this thesis compared real-world motorsport crash simulations and head impact laboratory tests designed to simulate real-world head impacts. Deformation and change in velocity were used to compare the energy managed by each system. The laboratory results generally tested higher severity impacts, with higher accelerations, compared to the simulations, despite managing a similar amount of energy. This indicates a large amount of the energy involved in the simulations was managed by the surrounding protective systems. The differences between systems create challenges for representing real-world crashes in a laboratory setting. Overall, the comparison in this thesis raises considerations for future helmet testing protocols in order to better match real-world simulations. / Master of Science / Occupational accidents are a main source of traumatic brain injuries (TBIs), with TBIs accounting for a substantial portion of all work-related deaths. Motor vehicle accidents and falls are consistently leading causes of head injury and fatality across industries. These injuries can have serious long-term consequences on an individual's quality of life and lead to large economic costs within society. This thesis investigated sources of occupational TBI prevention within two industries, construction and professional motorsports. In the last twenty years there have been major safety advancements within these industries, and yet the risk of TBI still exists. There is a need for safety standards that better reflect real-world injury scenarios. This thesis considered improvements to construction hard hat safety standards by evaluating the ability of two different hard hat types to reduce head injuries due to falls. It also compared real-world motorsport crash simulations and head impact laboratory tests designed to simulate real-world head impacts. This comparison raises considerations for future helmet testing protocols in order to better represent real-world simulations.

As many as 3.8 million sports-related traumatic brain injuries (TBIs) occur each year, nearly all of which are mild or concussive. These injuries are especially concerning given recent evidence that repeated concussions can lead to long-term neurodegenerative processes. One way of reducing the number of injuries is through improvements in protective equipment design. Safety standards and relative performance ratings have led to advancements in helmet design that have reduced severe injuries and fatalities in sports as well as concussive injuries. These standards and evaluation methods frequently use laboratory methods and brain injury criteria that have been developed through decades of research dedicated to determining the human tolerance to brain injury. It is necessary to determine which methods are the most appropriate for evaluating the performance of helmets and other protective equipment. Therefore, the aims of this research were to evaluate the use of different brain injury criteria and apply them to laboratory evaluation of helmets. These aims were achieved through evaluating the predictive capability of different brain injury criteria and comparing laboratory impact systems commonly used to evaluate helmet performance. Laboratory methods were developed to evaluate the relative performance of hockey helmets given the high rate of concussions associated with the sport. The implementation of these methods provided previously unavailable data on the relative risk of concussion associated with different hockey helmet models. / Ph. D.

Generating unbiased permutations at run time has traditionally been accomplished through application specific optimized combinational logic and has been limited to very small permutations. For generating unbiased permutations of any larger size, variations of the memory dependent Fisher-Yates algorithm are known to be an optimal solution in software and have been relied on as a hardware solution even to this day. However, in hardware, this thesis proves Fisher-Yates to be a suboptimal solution. This thesis will show variations of Fisher-Yates to be suboptimal by proposing an alternate method that does not rely on memory and outperforms Fisher-Yates based permutation generators, while still able to scale to very large sized permutations. This thesis also proves that this proposed method is unbiased and requires a minimal input. Lastly, this thesis demonstrates a means to scale the proposed method to any sized permutations and also to produce optimal partial permutations. / Master of Science / In computing, some applications need the ability to shuffle or rearrange items based on run time information during their normal operations. A similar task is a partial shuffle where only an information dependent selection of the total items is returned in a shuffled order. Initially, there may be the assumption that these are trivial tasks. However, the applications that rely on this ability are typically related to security which requires repeatable, unbiased operations. These requirements quickly turn seemingly simple tasks to complex. Worse, often they are done incorrectly and only appear to meet these requirements, which has disastrous implications for security. A current and dominating method to shuffle items that meets these requirements was developed over fifty years ago and is based on an even older algorithm refer to as Fisher-Yates, after its original authors. Fisher-Yates based methods shuffle items in memory, which is seen as advantageous in software but only serves as a disadvantage in hardware since memory access is significantly slower than other operations. Additionally, when performing a partial shuffle, Fisher-Yates methods require the same resources as when performing a complete shuffle. This is due to the fact that, with Fisher-Yates methods, each element in a shuffle is dependent on all of the other elements. Alternate methods to meet these requirements are known but are only able to shuffle a very small number of items before they become too slow for practical use. To combat the disadvantages current methods of shuffling possess, this thesis proposes an alternate approach to performing shuffles. This alternate approach meets the previously stated requirements while outperforming current methods. This alternate approach is also able to be extended to shuffling any number of items while maintaining a useable level of performance. Further, unlike current popular shuffling methods, the proposed method has no inter-item dependency and thus offers great advantages over current popular methods with partial shuffles.

The research presented herein is a combination of work done in two distinct subcategories of sport related head injury research. The body of work is aimed at increasing the understanding of head impact biomechanics across a broad spectrum of impact scenarios as well as the ability of helmets to affect head impact biomechanics over time. The first study utilizes in situ testing of controlled impacts of an instrumented head form to more fully characterize head accelerations resulting from impacts to the ice, board, and glass surfaces present in an ice hockey rink. The full characterization of head impacts across a spectrum of loading conditions and impact surfaces gives researchers insight into head impact tolerance and head protection capabilities and limitations in ice hockey. The second study details the development of a method to impact helmet pads for repeated loading studies based on published head impact exposure data. The third study uses this newly developed methodology to test the effects of a season of impacts on the energy absorbing properties of three different helmet padding technologies. The body of work is aimed at increasing understanding of head impact and concussion and the ability of existing helmet technologies to prevent these injuries with a goal of reducing the occurrence of injury. / Master of Science

Syftet med detta examensarbete är att få reda på hur speciallärare i en mellansvensk kommun stöttar och utmanar särskilt begåvade elever i ämnet matematik.  Genom kvalitativa interjuver och en kvantitativ enkätundersökning fick speciallärare svara på hur de arbetar för att upptäcka och stötta särskilt begåvade elever i ämnet matematik. En kvalitativ intervju genomfördes även med en vårdnadshavare till en särskilt begåvad elev. Med dessa undersökningar framgår att kunskap om särskilt begåvade elever finns men att det saknas fastställda rutiner kring hur arbetet med dessa elever ska genomföras.  Enligt skollagen ska alla elever ges möjlighet att utvecklas vilket i praktiken är svårt att genomföra då det saknas rutiner för hur särskilt begåvade elever ska upptäckas och hur arbetet för att utmana och fortsätta utveckla dessa elever saknas idag.

An estimated 3.8 million sports-related concussions occur every year. Little research has been collected on soccer players, despite women's soccer having the third highest rate of concussion among all popular collegiate sports. The objective of this work was to evaluate multiple interventions that have been introduced to address the high rate of concussions in this population. Wearable head impact sensors were evaluated on their ability to accurately count and measure head impacts during a collegiate women's soccer season. Head impact exposure was quantified using video analysis of this season as well. Sensors were unable to accurately count impacts and reported nonsensical head acceleration measurements, indicating that data reported from head impact sensors should be interpreted with caution. The ability of soccer headgear to reduce linear and rotational head accelerations during common soccer impacts was examined in the laboratory. Ball-to-head and head-to-head impacts were performed at a range of speeds and impact orientations. Headgear resulted in small reductions during ball-to-head tests, which are not likely to be clinically relevant. In head-to-head tests, use of headgear on the struck head provided an overall 35% reduction in linear head acceleration, and a 53% reduction when another headgear was added to the striking head. The ten headgear tested varied greatly in performance. These data suggest that the use of protective headgear could reduce concussion incidence significantly in this population. Research presented in this thesis will inform soccer organizations on best practices for player safety with regard to head impacts. / Master of Science / Concussions in sports are an increasing concern for coaches, parents, and players. An estimated 3.8 million sports-related concussions occur every year. Little research has been collected on soccer players, despite women's soccer having the third highest rate of concussion among all popular collegiate sports. Various interventions have been proposed to address this high rate of concussion. The objective of this work was to evaluate some of these interventions. Wearable head impact sensors were evaluated on their ability to accurately count and measure all types of head impacts during a collegiate women's soccer season. The number and nature of head impacts was also gathered using video analysis. Results indicate that head impact sensors struggled with producing reliable data and thus we advise that data gathered using these types of sensors should be interpreted with caution. The ability of soccer headgear to reduce head impact severity during common soccer impacts was examined in the laboratory. Ball-to-head and headto-head impacts were performed to evaluate headgear performance. Headgear resulted in only small reductions during ball-to-head tests. In head-to-head tests, use of headgear on the struck head provided an overall 35% reduction, and a 53% reduction when another headgear was added to the striking head. The ten headgear tested varied greatly in performance. These data suggest that the use of protective headgear could reduce concussion incidence significantly in this female soccer players. Research presented in this thesis will inform soccer organizations on best practices for player safety with regard to head impacts.

Fit is often pointed to as the most important factor to consider when selecting a helmet. However, there is no published biomechanical evidence suggesting that of helmet fit effects concussion risk. The objectives of this study were to quantify helmet fit on a headform and to determine the effect fit has on helmet performance. An impact pendulum was used to strike a helmeted NOCSAE headform mounted on a Hybrid III neck. Helmets were impacted at 4 locations at 3 energies representing a range of concussive to sub-concussive impacts. The fit conditions evaluated in this study represent fitting scenarios in which an athlete is provided a helmet that is properly or improperly sized and cases in which a properly sized helmet is too loose, too tight, or properly adjusted. A custom pressure sensor was developed and used to characterize helmet fit in each condition with a quantitative fit metric representative of a variation from zero pressure on the headform. All helmets produced significant differences in both peak linear and peak angular acceleration due to fit. Differences were generally small with some exceptions. Furthermore, air bladder inflation generated significant differences in both peak linear and peak angular acceleration, but these were generally small in magnitude. While fit associated with size and air bladder inflation significantly affected linear and rotational head acceleration for most impact conditions, the best fit condition did not always generate the lowest accelerations. Differences can be attributed to varying helmet characteristics between and within helmet models. / Master of Science / Fit is often pointed to as the most important factor to consider when selecting a helmet. However, there is no published biomechanical evidence suggesting that of helmet fit effects concussion risk. The objectives of this study were to quantify helmet fit on a headform and to determine the effect fit has on helmet performance. An impact pendulum was used to strike a helmeted biofidelic headneck assembly in a multitude of impact velocities and locations in order to simulate a range of onfield head impacts in a laboratory setting. The fit conditions evaluated in this study represent fitting scenarios in which an athlete is provided a helmet that is properly or improperly sized and cases in which a properly sized helmet is too loose, too tight, or properly adjusted. A custom pressure sensor was developed and used to characterize helmet fit in each condition with a quantitative fit metric representative of a variation from zero pressure on the headform. Linear and rotational acceleration were evaluated to characterize concussion risk as they have been found to be the best correlate for concussion risk in previous work. In this study, the effects of helmet fit and helmet air bladder inflation on peak linear and rotational head acceleration were evaluated. In general, the effects of both fit and air bladder inflation were small, but there were cases of substantial differences. However, the best fit condition did not always result in the lowest head acceleration. Differences can be attributed to varying helmet characteristics between and within helmet models. This data can be used to progress helmet safety through improving helmet performance evaluation, which will increase consumer awareness.

Accurate physical modeling and accelerated numerical simulation of turbulent flows remain primary challenges in CFD for aerospace engineering and related fields. This dissertation tackles these challenges with a focus on Reynolds-Averaged Navier--Stokes (RANS) models, which will continue to serve as the backbone for many practical aircraft applications. Specifically, in RANS turbulence modeling, the challenges include developing more efficient ensemble filters to learn nonlinear eddy viscosity models from observation data that move beyond the classical Boussinesq hypothesis, as well as developing non-equilibrium models that break away from the weak equilibrium assumption while maintaining computational efficiency. For accelerating RANS simulations, the challenges include leveraging existing simulation data to optimize the computational workflow while maintaining the method's adaptability to various computational settings. From a fundamental and mathematical perspective, we view these challenges as problems of modeling and learning complex nonlinear and nonlocal mappings, which we categorize into three types: field-to-point, field-to-field, and ensemble-to-ensemble. To model and resolve these mappings, we build up on recent advancements in machine learning and develop novel neural operator-based methods that not only possess strong representational capabilities but also preserve critical physical and mathematical principles. With the developed tools, we have demonstrated promising preliminary results in addressing these challenges and have the potential to significantly advance the state of the art in RANS turbulence modeling and simulation acceleration. / Doctor of Philosophy / Understanding and accurately predicting turbulent flows, such as those around airplanes or ships, are among the biggest challenges in computational fluid dynamics (CFD). This research aims to improve Reynolds-Averaged Navier--Stokes (RANS) models, which are widely used in practical engineering applications. Traditional RANS turbulence models are based on simplified assumptions that are linear and local, making it difficult to capture the true complexity of turbulent flows. My work addresses this limitation by developing new models that leverage advanced machine learning techniques to better represent turbulence. Specifically, I have focused on developing methods that extend beyond conventional approaches by learning more accurate local nonlinear constitutive relations and incorporating nonlocal effects---an important step toward improving simulation accuracy. In addition, I have explored strategies to accelerate RANS simulations by making more effective use of existing data, providing better initial conditions for simulations, and ultimately reducing computational costs. Preliminary results indicate that these new methods have the potential to push the boundaries of RANS turbulence modeling, enabling more accurate and efficient simulations.

Laser-plasma accelerators are of great interest because of their ability to sustain extremely large acceleration gradients, enabling compact accelerating structures. Laser-plasma acceleration is realized by using a high-intensity short pulse laser to drive a large plasma wave or wakefield in an underdense plasma. This thesis considers the effect of axial plasma density upramps on laser wakefield acceleration. Theoretical groundwork shows that tapered plasma channels can be used to mitigate one of the main limitations of laser plasma acceleration, that is, dephasing of an electron beam with respect to the plasma wave. It is shown that it is possible to maintain an electron bunch at constant phase in the longitudinal electric fields of the laser wake field. This leads to an increased energy gain of an electron trapped in the wakefield. The required shape of the density slope is difficult to implement in experiments. Therefore, a linear density ramp is also considered which is predicted to also increase the energy gain beyond that possible in a uniform density plasma. Towards an experimental implementation it was studied how a suitable gas density profile can be established in a capillary. This was done employing simulations using the computational fluid dynamics tool kit OpenFoam and comparing these to measurements of the axial density profile based on Raman scattering. It was demonstrated that a linear density ramp could be established by applying different pressures on the capillary gas inlets. The dependence of the density profile on the capillary parameters, such as, capillary diameter and length and inlet diameter were also studied. The results of the simulations and the measurement showed excellent agreement and demonstrate that approximately linear density ramps can be generated by flowing gas along a capillary of constant cross-section Laser wakefield acceleration in plasmas with longitudinally varying density was investigated in an experiment at the Astra Laser at Rutherford Laboratories. The experiment utilised ionisation injection in order to operate in the mildly non-linear regime of laser-wakefield acceleration. The measured electron energies agree well with the theoretical predictions. It was demonstrated that an increase in the energy gain can be obtained by driving the accelerator in a ramped plasma, the electron spectrum is more narrow and the injected charge increases significantly. Measurements of the X-ray spectrum emitted by the betatron motion of the accelerated electron bunch allowed the transverse radius of the bunch to be deduced. These measurements showed that retrieved electron bunch radius is inversely proportional to the longitudinal density gradient, that is a plasma density upramp (downramp) has a decreased (increased) electron bunch radius.