Interplanetary multiple gravity assist (MGA) trajectory optimization has long been a field of interest to space scientists and engineers. Gravity assist maneuvers alter a spacecraft's velocity vector and potentially allow spacecraft to achieve changes in velocity which would otherwise be unfeasible given our current technological limitations. Unfortunately, designing MGA trajectories is difficult and in order to find good solutions, deep space maneuvers (DSM) are often required which further increase the complexity of the problem. In addition, despite the active research in the field over the last 50 years, software for MGA trajectory optimization is scarce. A few good commercial, and even fewer open-source, options exist, but a majority of quality software remains proprietary. The intent of this thesis is twofold. The first part of this work explores the realm of global optimization applied to multiple gravity assist trajectories with deep space maneuvers (MGA-DSM). With the constant influx of new global optimization algorithms and heuristics being developed in the global optimization community, this work aims to be a high level optimization approach which makes use of those algorithms instead of trying to be one itself. Central to this approach is PaGMO, which is the open-source Parallel Multiobjective Global Optimizer created by ESA's Advanced Concepts Team (ACT). PaGMO is an implementation of the Island Model Paradigm which allows the parallelization of different global optimizers. The second part of this work introduces the IGATO software which improves PaGMO by complementing it with dynamic restart capabilities, a pruning algorithm which learns over time, subdomain decomposition, and other techniques to create a powerful optimization tool. IGATO aims to be an open-source platform independent C++ application with a robust graphical user interface (GUI). The application is equipped with 2D plotting and simulations, real time Porkchop Plot generation, and other useful features for analyzing various problems. The optimizer is tested on several challenging MGA-DSM problems and performs well: consistently performing as well or better than PaGMO on its own.

Spacecraft trajectory optimization is a near-infinite problem space with a wide variety of models and optimizers. As trajectory complexity increases, so too must the capabilities of modern optimizers. Common objective cost functions for these optimizers include the propellant utilized by the spacecraft and the time the spacecraft spends in flight. One effective method of minimizing these costs is the utilization of one or multiple gravity assists. Due to the phenomenon known as the Oberth effect, fuel burned at a high velocity results in a larger change in orbital energy than fuel burned at a low velocity. Since a spacecraft is flying fastest at the periapsis of its orbit, application of impulsive thrust at this closest approach is demonstrably capable of generating a greater change in orbital energy than at any other location in a trajectory. Harnessing this extra energy in order to lower relevant cost functions requires the modeling of these “powered flybys” or “powered gravity assists” (PGAs) within an interplanetary trajectory optimizer. This paper will discuss the use and modification of the Spacecraft Trajectory Optimization Suite, an optimizer built on evolutionary algorithms and the island model paradigm from the Parallel Global Multi-Objective Optimizer (PaGMO). This variant of STOpS enhances the STOpS library of tools with the capability of modeling and optimizing single and multiple powered gravity assist trajectories. Due to its functionality as a tool to optimize powered flybys, this variant of STOpS is named the Spacecraft Trajectory Optimization Suite - Flybys with Impulsive Thrust Engines (STOpS-FLITE). In three test scenarios, the PGA algorithm was able to converge to comparable or superior solutions to the unpowered gravity assist (uPGA) modeling used in previous STOpS versions, while providing extra options of trades between time of flight and propellant burned. Further, the PGA algorithm was able to find trajectories utilizing a PGA where uPGA trajectories were impossible due to limitations on time of flight and flyby altitude. Finally, STOpS-FLITE was able to converge to a uPGA trajectory when it was the most optimal solution, suggesting the algorithm does include and properly considers the uPGA case within its search space.

Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25 to 93% of intersection crashes. The effectiveness of I-ADAS will be limited by driver's pre-crash behavior and other environmental factors. This study will characterize real-world intersection traversals to evaluate the effectiveness of I-ADAS while accounting for driver behavior in crash and near-crash scenarios. This study characterized real-world intersection traversals using naturalistic driving datasets: the Second Strategic Highway Research Program (SHRP-2) and the Virginia Traffic Cameras for Advanced Safety Technologies (VT-CAST) 2020. A step-by-step approach was taken to create an algorithm that can identify three different intersection traversal trajectories: straight crossing path (SCP); left turn across path opposite direction (LTAP/OD); and left turn across path lateral direction (LTAP/LD). About 140,000 intersection traversals were characterized and used to train a unique driver behavior model. The median average speed for all encounter types was about 7.2 m/s. The driver behavior model was a Markov Model with a multinomial regression that achieved an average 90.5% accuracy across the three crash modes. The model used over 124,000 total intersection encounters including 301 crash and near-crash scenarios. I-ADAS effectiveness was evaluated with realistic driver behavior in simulations of intersection traversal scenarios based on proposed US New Car Assessment Program I-ADAS test protocols. All near-crashes were avoided. The driver with I-ADAS overall helped avoid more crashes. For SCP and LTAP the collisions avoided increased as the field of view of the sensor increased in I-ADAS only simulations. There were 18% crash scenarios that were not avoided with I-ADAS with driver. Among near-crash scenarios, where NHTSA expects no I-ADAS activation, there were fewer I-ADAS activations (58.5%) due to driver input compared to the I-ADAS only simulations (0%). / Master of Science / Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25-93% of intersection crashes. I-ADAS can assist drivers in preventing or mitigating these crashes using a collision warning system or automatically applying the brakes for the driver. One way I-ADAS may assist in crash prevention is with automatic emergency braking (AEB), which will automatically apply braking without driver input if the vehicle detects that a crash is imminent. The United States New Car Assessment Program (US-NCAP) has also proposed adding I-ADAS with AEB tests into its standard test matrix. The US-NCAP has proposed three different scenarios. All the tests have two crash-imminent configurations where the vehicles are set up to collide if no deceleration occurs and a near-miss configuration where the vehicles are set up to barely miss each other. This study will use intersection traversals from naturalistic driving data in the US to build a driver behavior model. The intersection travels will be characterized by their speed, acceleration, deceleration, and estimated time to collision. The driver behavior model was able to predict the longitudinal and lateral movements for the driver. The proposed US-NCAP test protocols were then simulated with varied sensors parameters where one vehicle was equipped with I-ADAS and a driver. The vehicle with I-ADAS with a driver was more successful than a vehicle only equipped with I-ADAS at preventing a crash.

Since 2015, there have been more than 35,000 fatalities annually due to crashes on United States roads [1], [2]. Typically, road departure crashes account for less than 10% of all annual crash occupants yet comprise nearly one third of all crash fatalities in the US [3]. In the year 2020, road departure crashes accounted for 50% of crash fatalities [2]. Road departure crashes are characterized by a vehicle leaving the intended lane of travel, departing the roadway, and striking a roadside object, such as a tree or pole, or roadside condition, such as a slope or body of water. One strategy currently implemented to mitigate these types of crashes is the use of roadside barriers. Roadside barriers, such as metal guardrails, concrete barriers, and cable barriers, are designed to reduce the severity of road departure crashes by acting as a shield between the departed vehicle and more hazardous roadside obstacles. Much like new vehicles undergo regulatory crash tests, barriers must adhere to a set of crash test procedures to ensure the barriers perform as intended. Currently, the procedures for full-scale roadside barrier crash tests used to evaluate the crash performance of roadside safety hardware are outlined in The Manual for Assessing Safety Hardware (MASH) [4]. During roadside barrier tests, the assessment of occupant injury risk is crucial, as the purpose of the hardware is to prevent the vehicle from colliding with a more detrimental roadside object, all the while minimizing, and not posing additional, risk to the occupants. Unlike the new vehicle regulatory crash tests conducted by the National Highway Traffic Safety Administration (NHTSA), MASH does not require the use of instrumented anthropomorphic test devices (ATD). Instead, one of the prescribed occupant risk assessment methods in MASH is the flail space model (FSM), which was introduced in 1981 and models an occupant as an unrestrained point mass. The FSM is comprised of two crash severity metrics that can be calculated using acceleration data from the test vehicle. Each metric is prescribed a maximum threshold in MASH and if either threshold is exceeded during a crash test the test fails due to high occupant injury risk. Since the inception of the FSM metrics and their thresholds, the injury prediction capabilities of these metrics have only been re-investigated in the frontal crash mode, despite MASH prescribing an oblique 25-degree impact angle for passenger vehicle barrier tests. The focus of this dissertation was to use EDR data from real-world crashes to assess the current relevance of roadside barrier crash test occupant risk assessment methods to the modern vehicle fleet and occupant population. Injury risk prediction models were constructed for the two FSM-based metrics and five additional crash severity metrics for three crash modes: frontal, side, and oblique. For each crash mode and metric combination, four injury prediction models were constructed: one to predict probability of injury to any region of the body and three to predict probability of injury to the head/face, neck, and thorax regions. While the direct application of these models is to inform future revisions of MASH crash test procedures, the developed models have valuable applications for other areas of transportation safety besides just roadside safety. The final two chapters of this dissertation explore these additional applications: 1) assessing the injury mitigation effectiveness of an advanced automatic emergency braking system, and 2) informing speed limit selection that supports the safe system approach. The findings in this dissertation indicate that both the FSM and additional crash severity metrics do a reasonable job predicting occupant injury risk in oblique crashes. One of the additional metrics performs better than the two FSM metrics. Additionally, several occupant factors, such as belt status and age, play significant roles in occupant risk prediction. These findings have important implications for future revisions of MASH, which could benefit from considering additional metrics and occupant factors in the occupant risk assessment procedures. / Doctor of Philosophy / Every year, there are more than 35,000 fatalities due to crashes on United States roads. While there are many different types of crashes, there is a small collection of crash types that are responsible for the majority of these fatalities. One of the worst crash types is a road departure crash. Road departure crashes describe when a vehicle leaves the roadway and collides with an object off the roadway (such as a tree, pole, or ditch). Road departure crashes typically comprise 10% of crashes but are responsible for more than 30% of the annual crash fatalities. In 2020, road departure crashes were responsible for 50% of the 39,000 fatalities. One strategy that is currently used to reduce road departure fatalities is the use of roadside barriers. Common roadside barrier types include metal guardrails, concrete barriers, and cable guardrails, and are used to prevent vehicles that are departing the roadway from hitting an object that would be more dangerous than the barrier. To ensure barriers successfully protect the vehicle and vehicle occupants from heightened danger, they are crash tested in scenarios that are designed to mimic real-world crashes. The Manual for Assessing Safety Hardware (MASH) is the document that currently outlines the details necessary to conduct one of these crash tests. During roadside barrier tests, it is crucial to determine whether occupants are at risk of injury or fatality. For a variety of reasons, barrier tests do not use the traditional crash test dummies, which are designed to replicate human presence in a crash vehicle. Instead, MASH recommends using vehicle velocity data to assess how much risk is posed to an occupant. Using this velocity data, two values can be computed and if either value exceeds the maximum values provided in MASH, the crash test fails due to high occupant risk. The suggestion to use velocity data to assess occupant risk was first introduced in 1981. Since then, there have been significant advances in vehicle design, barrier design, and occupants' willingness to partake in safe habits, such as wearing seatbelts. Therefore, it is necessary to determine if the occupant risk values used in MASH are still applicable today. The focus of this dissertation was to use real-world crash data to assess the current relevance of roadside barrier crash test occupant risk values. The results presented in this dissertation can be used to select new occupant risk values in future versions of MASH. The findings within this dissertation show that the current methods in MASH do a good job estimating an occupant's risk of injury. Additionally, the findings show that certain occupant factors, such as the age of an occupant and whether the occupant is belted, help to more accurately estimate occupant injury risk. This finding has important implications for MASH, which does not currently consider different occupant conditions.

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13580号 / 論医博第2302号 / 新制||医||1069(附属図書館) / (主査)教授 平井 豊博, 教授 江木 盛時, 教授 齋藤 潤 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

The aim of this retrospective study was to investigate whether there would be an association between periodontal disease parameters and positive bacterial findings at the driveline of patients with a left ventricular assist device (LVAD). Patients with an LVAD, who underwent a full oral and microbiological examination between 2016 and 2018, were included. During oral examination, periodontitis severity (stage and grade) and the periodontal inflamed surface area (PISA) were evaluated. A microbiological analysis was performed from swabs of the driveline, whereby different bacterial species were cultivated and analyzed. A total of 73 patients were included in the current study. The majority of participants (80.8%) had at least one positive bacterial finding during the study period. Most patients had a periodontitis stage of III-IV (80.9%). The determined PISA of the total group was 284.78 ± 352.29 mm2. No associations were found between the periodontal disease parameters and the bacterial findings in general, the bacterial findings on the day of oral examination or the bacterial findings 12 months prior to/after the oral examination (p > 0.05). Periodontitis is not associated with cultivated microbiological findings at the driveline of patients with an LVAD and thus appears not to be a risk indicator for driveline colonization. Nevertheless, the high periodontal burden in LVAD patients underlines the need for their improved periodontal care.

The rotary Left Ventricular Assist Device (LVAD) is a mechanical pump surgically implanted in patients with end-stage congestive heart failure to help maintain the flow of blood from the sick heart. The rotary type pumps are controlled by varying the impeller speed to control the amount of blood flowing through the LVAD. One important challenge in using these devices is to prevent the occurrence of excessive pumping of blood from the left ventricle (known as suction) that may cause it to collapse due to the high pump speed. The development of a proper feedback controller for the pump speed is therefore crucial to meet this challenge. In this thesis, some theoretical and practical issues related to the development of such a controller are discussed. First, a basic nonlinear, time-varying cardiovascular-LVAD circuit model that will be used to develop the controller is reviewed. Using this model, a suction index is tested to detect suction. Finally we propose a feedback controller that uses the pump flow signal to regulate the pump speed based on the suction index and an associated threshold. The objective of this controller is to continuously update the pump speed to adapt to the physiological changes of the patient while at the same time avoiding suction. Simulation results are presented under different conditions of the patient activities. Robustness of the controller to measurement noise is also discussed.

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<p>Respiratory distress syndrome is a major cause of mortality among pre-term and term neonatal population. To overcome the limitations of current therapies, a new form of respiratory support termed the, “Artificial Placenta” has been proposed. The Artificial Placenta is a type of oxygenator that is attached postnatally via the umbilical vessels to provide pumpless respiratory support to pre-term and term neonates. To develop this concept, our group previously reported on a novel polycarbonate membrane lung assist device (LAD). To build upon its development, the objectives of this thesis are to determine the optimal interface for gas exchange, and characterize the gas exchange properties of the LAD under ambient and oxygen rich atmosphere. Subsequently, its feasibility was determined by studying the effects of extracorporeal flow rates on cardiovascular parameters and gas exchange performance was assessed in a newborn piglet model.</p> <p>In vitro testing demonstrated that PDMS based membrane is the optimal interface for gas exchange in the LAD. In vitro testing of the LAD demonstrated 2.4 µL/min/cm² -3.8 µL/min/cm² and 6.4 µL/min/cm²- 10.1 µL/min/cm² of O₂ and CO₂ transfer respectively under ambient air and oxygen rich atmospheric conditions. Based on these results, the LAD theoretically could provide 6-11% of metabolic O₂ while eliminating 18-26% of CO₂in a newborn healthy pre term infant. Experiments in newborn piglet models achieved pumpless configuration with flow rates up to 60.9ml/kg/min without presenting decompensation. Preliminary, in vivo gas exchange experiments demonstrated O₂ transfer of 3ul/min/cm², which matches closely to in vitro data.</p> <p>A novel pumpless LAD is reported, which provides sufficient respiratory support. High extracorporeal flow rates with stable cardiovascular parameters demonstrate feasibility of the artificial placenta concept. This novel LAD could potentially serve as a rescue device when all other therapies such as nasal continuous positive airway and mechanical ventilation fail.</p> / Master of Applied Science (MASc)