From the safety point of view, the bus stop is perhaps the most important part of the Bus Public Transport System, as it represents the point where bus passengers may interact directly with other road users and create conflicting situations leading to traffic accidents. For example, travellers could be struck walking to/from or boarding/alighting a bus. At these locations, passengers become pedestrians and at some stage crossing busy arterial roads at the bus stop in areas or at objects of main attraction usually outside of pedestrian designated facilities such as signal controlled intersections, zebra and pelican crossings. Pedestrian exposure to risk or risk-taking occurs when people want to cross the road in front of the stopped bus, at the rear of the bus or between the buses, particularly where bus stops are located on two-way roads (i.e. within the mid-block of the road with side streets, at non-signalised cross-section). However, it is necessary to have a better understanding of the pedestrian road-crossing risk exposure (pedestrian crossing distraction, obscurity and behaviour) within bus stop zones so that it can be incorporated into new design, bus stop placement, and evaluation of traffic management schemes where bus stop locations will play an increasingly important role. A full range of possible incidental interactions are presented in a tabular model that looks at the most common interacting traffic movements within bus stop zones. The thesis focused on pedestrian safety, discusses theoretical foundations of bus stops, and determines the types of accident risks between bus travellers as pedestrians and motor vehicles within the zones of the bus stop. Thus, the objectives of this thesis can be summarized as follows: (I) - Classification of bus stops, particularly according to objects of main attraction (pedestrian-generating activities); (II) - Analysis of traffic movement and interactions as an accident/risk exposure in the zone of bus stops with respect to that structure; (III) - Categorizing traffic accident in the vicinity of bus stops, and to analyse the interactions (interacting movements) that occur within bus stop zones in order to discover the nature of problems; (IV) - Formulation of tabular (pedestrian traffic accident prediction) models/forms (based on traffic interactions that creating and causing possibilities of accident conflict) for practical statistical methods of those accidents related to bus stop, and; (V) - Safety aspects related to the micro-location of bus stops to assist in the micro-location design, operations of bus stop safety facilities and safer pedestrian crossing for access between the bus stop and nearby objects of attraction. The scope of this thesis focuses on the theoretical foundation of bus stop microâ??location in areas of main attractions or at objects of main attraction, and traffic accident risk types as they occur between travellers as pedestrians and vehicle flow in the zone of the bus stop. The knowledge of possible interactions leads to the identification of potential conflict situations between motor vehicles and pedestrians. The problems discussed for each given conflict situation, has a great potential in increasing the knowledge needed to prevent accidents and minimise any pedestrian-vehicle conflict in this area and to aid in the development and planning of safer bus stops.

Orientador: Luiz Otávio Saraiva Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T02:13:59Z (GMT). No. of bitstreams: 1 Oliveira_FabiolaMartinsCamposde_M.pdf: 13513306 bytes, checksum: fd54d4a28df68e3217dbd8316b6aeadd (MD5) Previous issue date: 2015 / Resumo: Recentes avanços na tecnologia de processadores multinúcleos e vários-núcleos popularizaram a computação paralela, acelerando a execução de programas e possibilitando a simulação de domínios maiores. Dentre os problemas complexos que requerem alta velocidade de processamento, os problemas de fluidodinâmica computacional se destacam, já que suas simulações tendem a ter um alto custo computacional e exigem grandes domínios de simulação. O método baseado nas Redes de Boltzmann é um método de fluidodinâmica computacional apropriado para o uso de paralelismo que vem ganhando destaque na comunidade científica. Embora haja trabalhos que explorem o paralelismo em GPU nesse método, um simulador eficiente na execução e visualização interativa ainda não foi explorado adequadamente. Assim, a proposta deste trabalho é implementar em GPU um simulador interativo de fluidodinâmica com o método das Redes de Boltzmann. Inicialmente, o simulador foi desenvolvido em linguagem C e foi paralelizado em CPU usando MPI. Em seguida, foi paralelizado em GPU usando CUDA e convertido para linguagem orientada a objetos em C++. Depois, a visualização interativa foi acrescentada utilizando técnicas como interoperabilidade entre CUDA e OpenGL, texturização 3D, fluxo programável da GPU, além de funções de interação com o usuário. O simulador foi validado para casos 2D e 3D em fluxos monocomponentes monofásicos. Além disso, para demonstrar o ganho de desempenho em velocidade de processamento de problemas paralelizados em relação a execuções sequenciais, um conjunto de testes com tamanhos crescentes de domínio foi desenvolvido. O resultado dessa comparação indicou que o simulador implementado em GPU com visualização interativa teve desempenho 71.3 vezes maior em relação à versão sequencial em CPU sem visualização interativa. Dessa forma, a abordagem de paralelização em GPU com visualização interativa mostrou-se muito adequada à execução de simulações fluidodinâmicas, sendo uma ferramenta útil no estudo de escoamentos fluídicos, capaz de executar inúmeros cálculos e lidar com a grande quantidade de memória exigida por simulações fluídicas / Abstract: Recent advances on multicore and many-core processor technology have popularized the parallel computing, accelerating program execution and enabling the simulation of larger domains. Within the complex problems that require a high processing speed, the computational fluid dynamics problems stand out, since their simulations tend to have high computational cost and demand large simulation domains. The method based on the Lattice Boltzmann is an appropriate computational fluid dynamics algorithm to explore parallelism that has been noteworthy in scientific community. Although there are several works that approach GPU parallelism in this method, an efficient simulator implementation and interactive visualization have not been explored adequately. Thus, the purpose of this work is to implement in GPU an interactive fluid dynamics simulator based on the Lattice Boltzmann method. First, the simulator was developed in C language and was parallelized in CPU using MPI. Next, it was parallelized in GPU using CUDA and converted into C++ object-oriented language. Then, the interactive visualization was added using techniques such as CUDA-OpenGL interoperability, 3D texturing, GPU programmable pipeline, and interaction features. The simulator was validated for 2D and 3D cases in single component, single phase flows. Besides that, to show the performance gain in processing velocity of parallelized problems in relation to sequential executions, a test set with increasing domain sizes was developed. This comparison result indicated the GPU-implemented interactive simulator was 71.3 times faster in relation to sequential CPU version without interactive visualization. Thereby, the GPU parallelization approach with interactive visualization showed to be very adequate to fluid dynamics simulations, being a useful tool in fluid flow study, capable of simulating numerous calculations and dealing with the large amount of memory required in fluidic simulations / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestra em Engenharia Mecânica

Advances in technology have given rise to the widespread use of small unmanned aerial systems (sUAS), more commonly known as ‘drones.’ The sUAS market is expected to continue to increase at a rapid pace, with the FAA forecasting around 8,000 registrations monthly (FAA, 2019). High profile drone incidents include use in an attack on the Venezuelan president, an undetected landing on the property of the White House, and use in dropping crude explosives on troops in the Middle East (Gramer, 2017; Grossman, 2018; Wallace & Loffi, 2015). The rate of proliferation and high-performance characteristics of these drones has raised serious concerns for safety in high-density outdoor events. Counter-unmanned aerial systems are currently illegal for all but a few Federal entities within the U.S., leaving private and public entities at risk. This exploratory research investigates several legal facility and patron behavioral interventions to reduce possible casualties during a drone attack by using AnyLogic simulation modeling in an amusement park scenario. Data from this experiment suggest that behavioral interventions implemented 30 seconds before a drone attack can reduce casualties by more than 55%, and up to 62% casualty reductions can be realized with a 60-second implementation time. Testing suggests that venue design considerations, such as a reduction in hard corners, covered high-density areas, and smoother area transitions can synergistically reduce casualties when used in conjunction with a warning system. While casualty mitigation did occur throughout the study, active threat interdiction methods would be necessary to design a system that may prevent casualties overall.

Game balance has been a challenge for game developers since the time games have become more complex. There have been a handful of proposals for game balancing processes outside the manual labor-intensive play testing methods, which most game developers often are forced to use simply due to the lack of better methods. Simple solutions, like restrictive game play, are limited because of their inability to provide insight on interdependencies among the mechanisms in the game. Complex techniques framed around the potential of AI algorithms are limited by computational budgets or cognition inability to assess human actions. In order to find a middle ground we investigate Game Theory and Mechanism Design concepts. Both have proven to be effective tools to analyse strategic situations among interacting participants, or in this case `players'. We test the feasibility of using these techniques in an Real Time Strategy (RTS) game domain to understand game balance. MicroRTS, a small and simple execution of an RTS game is employed as our model. The results provide promising insight on the effectiveness of the method in detecting imbalances and further inspection to find the cause. An additional benefit out of this technique, besides detecting for game imbalances, the approach can be leveraged to create imbalances. This is useful when the designer or player desires to do so.

<div>For decades, the response of high-energy (HE) density materials at extreme conditions of pressure and temperature from strong insults like burning or impact have been studied in depth by the shock community. Shock physicists aim to develop a fundamental understanding for coupled chemical and physical processes across orders of magnitude spatial and temporal regimes. In order to succeed, this requires extensive collaboration between experiments and simulations, ranging from the electronic to the engineering scales. The end goals would be to develop predictive multiscale models capable of explaining ignition and initiation of HE systems and composites. The collected works in this thesis detail my contributions to the field of HE materials, specifically addressing the chemical reactivity at the atomistic level using reactive molecular dynamics (MD) simulations.</div><div><div>Through this endeavor, we aim to develop a critical understanding for the decomposition processes of HE materials. We begin with a validation the reactive force field, ReaxFF, by addressing the very strong anisotropic shock sensitivity in 2,2-Bis[(nitrooxy)methyl]propane-1,3-diyl dinitrate (PETN) through direct comparison of time-evolved spectra between experiments and simulations. Such strong orientation dependence is thought to relate to the initial decomposition events. Therefore we compare spectra at three different shock pressures, where we observe similar timescales for the disappearance of the NO2 symmetric and antisymmetric stretch modes. A more detailed chemical species analysis indicates that the NO2 molecular species could be considered the primary intermediate which initiates the decomposition process. Furthermore, these results suggest that the combination of explicit MD simulations and ultrafast spectroscopy will be key to the development of a detailed understanding of chemistry at extreme conditions.</div></div><div><div>Following the validation study, we further our understanding of reactivity in HE systems by investigating the differences in kinetics between an ordered and disordered system. It has been shown that shocked material is often severely strained, causing a loss in crystalline order. This in turn results in the disordered materials, such as amorphous solids, having</div><div>faster reactivity due to their higher internal energy and/or lower thermal conductivity. Our results indicate that extra energy is required to break the long-range order in bulk crystalline systems, thus resulting in slower decomposition rates. Further analyses of thermal hotspots point towards slightly faster chemical propagation in the amorphous samples due to lower thermal conductivity. These results provide an understanding for how molecular disorder can be attributed to increased reactivity.</div></div><div><div>After developing an understanding for the initial decomposition processes of HE materials, we turn our attention to a growing interest in the community which is the developing reduced order chemistry models for use in multiscale efforts. Many schemes report mechanisms that are obtained from experiments, which can have large error bars depending on the apparatus and/or extraction technique, or from gas phase simulations, which may not be relevant at shock conditions. To circumvent these issues, we develop a coarse-grained chemical kinetics model from all-atom reactive MD simulations by taking advantage of an unsupervised dimensionality reduction machine learning technique called non-negative matrix factorization. Doing so allows us to represent the overall decomposition chemistry as latent concentrations akin to reactants, intermediates, and products, which we then use to extract kinetics parameters and heats of reaction. These values are implemented into a continuum model, where we could simulate the criticality of thermal hotspots at regimes beyond the reach of MD, as well as verify how uncertainties in the parameters vary as a function of hotspot sizes.</div></div><div><div>Finally, we close with significant progress made towards on-going and future work, where we address two of the most challenging ideas in the field of HE materials: 1) developing definitive chemistry models at extreme conditions, and 2) improving coarse-grained descriptions for multiscale modeling.</div></div>

<p>The dissertation offers a Multiphysics perspective in analyzing emerging metallurgical techniques. Heat transfer, structural deformation, and fluid flow associate with one another in phase-changing materials processing methods. To comprehensively analyze these aspects for an optimized final product, the authors have proposed a numerical mathematical model describing the thermal and geometric progression of the binary alloy casting process. The model is further executed in COMSOL Multiphysics, adapted in two metal manufacturing applications, resistance spot welding (RSW) and binder jet printing (BJP). </p><p>Resistance spot welding is a well-adapted metal sheeting joining technique with comparably limited modeling and simulating research. The heat transfer module and geometric deformation module are applied to the simulation of RSW to discuss the thermal gradient development of the welding zone. The model was further calculated and verified through a case study with Python. </p><p>BJP is a rapidly developing additive manufacturing method. The novel 3D printing technique brings challenges in post-processing geometric control and material selection limitations. Multiphysics simulation serves as an excellent tool in process parameters analysis and quality control. This dissertation focuses on the sintering process of BJP of binary alloy powders. Melting and solidification mathematical models were implemented in COMSOL, where the sintering shrinkage rate could be calculated. The shrinkage rate was further verified through experimental analysis of binder jet printed samples. </p><p>Microstructural analysis on sintered binder jet printed parts was performed to assess the validity of BJP to substitute the die casting method for manufacturing of valvebody. Sintering shrinkage and metallurgical analysis have been performed on the green and sintered BJP samples. After sintering, the final part achieved 98% density, and the integrity of the designed channels was preserved. The shrinkage analysis has indicated the effect of printing orientation and sintering orientation on the geometry and metallurgy of the final products. Microstructure analysis on the cross-sections of the sintered products also indicates the various defects induced from biner jet 3D printing. </p><p>The research aims to provide a systematic rheology analysis of the phase transformation process of binary alloys. The dissertation has connected the physical, mathematical modeling with 15 </p><p><br></p><p>simulative modeling through the rheological evaluation of phase-changing manufacturing techniques. The connections were conclusively verified through empirical studies, including case assessment and experimentation. The research aims to offer universally applicable models that can be applied to phase-changing metal processing techniques. </p>

<div>This thesis combines human and machine intelligence for consensus decision-making, and it contains four interrelated research areas. Before presenting the four research areas, this thesis presents a literature review on decision-making using two criteria: trust and risk. The analysis involves studying the individual and the multi-stakeholder decision-making. Also, it explores the relationship between trust and risk to provide insight on how to apply them when making any decision. This thesis presents a grouping procedure of the existing trust-based multi-stakeholder decision-making schemes by considering the group decision-making process and models. In the first research area, this thesis presents the foundation of building multi-stakeholder consensus decision-making (MSCDM). This thesis describes trust-based multi-stakeholder decision-making for water allocation to help the participants select a solution that comes from the best model. Several criteria are involved when deciding on a solution such as trust, damage, and benefit. This thesis considers Jain's fairness index as an indicator of reaching balance or equality for the stakeholder's needs. The preferred scenario is when having a high trust, low damages and high benefits. The worst scenario involves having low trust, high damage, and low benefit. The model is dynamic by adapting to the changes over time. The decision to select is the solution that is fair for almost everyone. In the second research area, this thesis presents a MSCDM, which is a generic framework that coordinates the decision-making rounds among stakeholders based on their influence toward each other, as represented by the trust relationship among them. This thesis describes the MSCDM framework that helps to find a decision the stakeholders can agree upon. Reaching a consensus decision might require several rounds where stakeholders negotiate by rating each other. This thesis presents the results of implementing MSCDM and evaluates the effect of trust on the consensus achievement and the reduction in the number of rounds needed to reach the final decision. This thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in the stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the distance of the choices made by the stakeholders. Trust is useful in decreasing the distances. In the third research area, this thesis presents Rating Convergence in the implemented MSCDM framework, and such convergence is a result of changes in stakeholders' rating behavior in each round. This thesis evaluates the effect of trust on the rating changes by measuring the perturbation in the rating matrix. Trust is useful in increasing the rating matrix perturbation. Such perturbation helps to decrease the number of rounds. Therefore, trust helps to increase the speed of agreeing upon the same decision through the influence. In the fourth research area, this thesis presents Rating Aggregation operators in the implemented MSCDM framework. This thesis addresses the need for aggregating the stakeholders' ratings while they negotiate on the round of decisions to compute the consensus achievement. This thesis presents four aggregation operators: weighted sum (WS), weighted product (WP), weighted product similarity measure (WPSM), and weighted exponent similarity measure (WESM). This thesis studies the performance of those aggregation operators in terms of consensus achievement and the number of rounds needed. The consensus threshold controls the performance of these operators. The contribution of this thesis lays the foundation for developing a framework for MSCDM that facilitates reaching the consensus decision by accounting for the stakeholders' influences toward one another. Trust represents the influence.</div>

Topographic depressions are naturally occurring low land areas surrounded by areas of high elevations, also known as “pits” or “sinks”, on terrain surfaces. Traditional watershed modeling often neglects the potential effects of depressions by implementing removal (mostly filling) procedures on the digital elevation model (DEM) prior to the simulation of physical processes. The assumption is that all the depressions are either spurious in the DEM or of negligible importance for modeling results. However, studies suggested that naturally occurring depressions can change runoff response and connectivity in a watershed based on storage conditions and their spatial arrangement, e.g., shift active contributing areas and soil moisture distributions, and timing and magnitude of flow discharge at the watershed outlet. In addition, recent advances in remote sensing techniques, such as LiDAR, allow us to examine this modeling assumption because naturally occurring depressions can be represented using high-resolution DEM. This dissertation provides insights on the effects of depressions on overland flow processes at multiple spatial scales, from internal depression areas to the watershed scale, based on hydrologic connectivity metrics. Connectivity describes flow pathway connectedness and is assessed using geostatistical measures of heterogeneity in overland flow patterns, i.e., connectivity function and integral connectivity scale lengths. A new algorithm is introduced here to upscale connectivity metrics to large gridded patterns (i.e., with > 1,000,000 cells) using GPU-accelerated computing. This new algorithm is sensitive to changes of connectivity directions and magnitudes in spatial patterns and is robust for large DEM grids with depressions. Implementation of the connectivity metrics to overland flow patterns generated from original and depression filled DEMs for a study watershed indicates that depressions typically decrease overland flow connectivity. A series of macro connectivity stages based on spatial distances are identified, which represent changes in the interaction mechanisms between overland flow and depressions, i.e., the relative dominance of fill and spill, and the relative speed of fill and formation of connected pathways. In addition, to study the role of spatial resolutions on such interaction mechanisms at watershed scale, two revised functional connectivity metrics are also introduced, based on depressions that are hydraulically connected to the watershed outlet and runoff response to rainfall. These two functional connectivity metrics are sensitive to connectivity changes in overland flow patterns because of depression removal (filling) for DEMs at different grid resolutions. Results show that these two metrics indicate the spatial and statistical characteristics of depressions and their implications on overland flow connectivity, and may also relate to storage and infiltration conditions. In addition, grid resolutions have a more significant impact on overland flow connectivity than depression removal (filling).

Generating ideas and creating prototypes of physical products is a highly non-linear and iterative process. Current tools divide this process into multiple discrete steps with different tools to support each of these steps such as CAD modelling, simulation and fabrication. We believe, design interfaces that combine different steps of the process and create different layers of abstraction depending on the type of the user and where they are in the process can support users in generating more creative ideas and creating better functioning prototypes more efficiently. In order to validate this, we developed three interfaces- a sketch-based ideation tool, a live programming interface to create IoT devices and a design tool to support design and fabrication of hand wearables. The foundation of these design interfaces is the layer of abstraction that allows users to focus on idea generation and converting it into a tangible prototype with little or no technical knowledge, and a continuous visual feedback that guides the user to make necessary changes to improve their design. The three tools were evaluated through user testing for supporting creation of different ideas and converting them into functional prototypes.

<div><div><div><p>Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.</p></div></div></div>