Infiltration facilities are source control mechanisms that are implemented in urban developments with reduced natural permeable surfaces. Despite the development of design criteria for infiltration facilities, these systems continue to fail due to headloss development, overflow, or chemical breakthrough. The limited research on the long-term performance of these systems has emphasized the role of physical filtration mechanisms within porous media filters to address concerns surrounding system failure, namely filter clogging. A continuous macroscopic depth filtration model was developed to investigate the clogging potential of the underlying sand filter. This continuous model furthers the understanding of temporal and spatial changes in system performance for the development of more appropriate design criteria and more suitable maintenance regimes. The characterization of long-term system performance by defining three different failure modes and a probabilistic approach comprises a comprehensive methodology by considering several performance criteria rather than assuming that one criterion dictates the overall system performance.

Infiltration facilities are source control mechanisms that are implemented in urban developments with reduced natural permeable surfaces. Despite the development of design criteria for infiltration facilities, these systems continue to fail due to headloss development, overflow, or chemical breakthrough. The limited research on the long-term performance of these systems has emphasized the role of physical filtration mechanisms within porous media filters to address concerns surrounding system failure, namely filter clogging. A continuous macroscopic depth filtration model was developed to investigate the clogging potential of the underlying sand filter. This continuous model furthers the understanding of temporal and spatial changes in system performance for the development of more appropriate design criteria and more suitable maintenance regimes. The characterization of long-term system performance by defining three different failure modes and a probabilistic approach comprises a comprehensive methodology by considering several performance criteria rather than assuming that one criterion dictates the overall system performance.

The purpose with this report is to develop a network based insertion algorithm and evaluate it on a real-case timetable. The aim of the algorithm is to minimize the effect that that train implementation cause on the other, already scheduled traffic. We meet this purpose by choosing an objective function that maximizes the minimum distance to a conflicting train path. This ensures that the inserted train receives the best possible bottleneck robustness. We construct a graph problem, which solve with a modified version of Dijkstra’s algorithm. The complexity of the algorithm is Ο(s^2 t log⁡(s^2 t). We applied the algorithm on a Swedish timetable, containing 76 stations. The algorithm performs well and manage to obtain the optimal solution for a range of scenarios, which we have evaluated in various experiments. Increased congestion seemed to reduce the problem size. The case also show that a solution’s robustness decreases with increasing total number of departures. One disadvantage with the algorithm is that it cannot detect the best solution among those using the same bottleneck. We propose a solution to this that we hope can be implemented in further studies.

No description available.

In the past, the study of Emergency Vehicle (EV) signal preemption has been mostly done using field studies. None of the simulation models that are currently commercially available have the capability to model the presence of EVs and simulate the traffic dynamics of the vehicles surrounding them. This study presents a macroscopic traffic model for examining the effect of signal preemption for EVs on traffic control measures, roadway capacity, and delays incurred to the vehicles on the side streets. The model is based on the cell transmission model, which is consistent with the hydrodynamic theory of traffic flow. A special component, in the form of a moving bottleneck that handles the traffic dynamics associated with the presence of EVs, was developed in the model. Several test scenarios were constructed to demonstrate the capabilities of the model for studying the impact of signal preemption on an arterial with multiple intersections under various traffic demand levels and varying frequencies of the arrival of EVs. Performance measures, such as average vehicle delay, maximum delay, and standard deviation of delay to traffic on all approaches, were obtained. An additional advantage of the model, apart from the capability to model EVs, is that the state-space equations used in the model can be easily incorporated into a mathematical programming problem. By coupling with a desired objective function, the model can be solved analytically. Optimal solutions can be generated to obtain insights into the development of traffic control strategies in the presence of EVs. / Master of Science

Doctor of Philosophy / Department of Computing and Information Sciences / David Gustafson / We use probabilistic graphs to predict the location of swarms over 100 steps in simulations in grid worlds. One graph can be used to make predictions for worlds of different dimensions. The worlds are constructed from a single 5x5 square pattern, each square of which may be either unoccupied or occupied by an obstacle or a target. Simulated robots move through the worlds avoiding the obstacles and tagging the targets. The interactions between the robots and the robots and the environment lead to behavior that, even in deterministic simulations, can be difficult to anticipate. The graphs capture the local rate and direction of swarm movement through the pattern. The graphs are used to create a transition matrix, which along with an occupancy matrix, can be used to predict the occupancy in the patterns in the 100 steps using 100 matrix multiplications. In the future, the graphs could be used to predict the movement of physical swarms though patterned environments such as city blocks in applications such as disaster response search and rescue. The predictions could assist in the design and deployment of such swarms and help rule out undesirable behavior.

Ce travail porte sur la modélisation d'écoulements turbulents anisothermes dans des milieux macroporeux. Ce problème intéresse de nombreux domaines : échangeurs de chaleur, réacteurs nucléaires, canopées... Notre objectif est de modéliser des écoulements traversant une structure solide selon un approche multi-échelle. L'utilisation d'un opérateur de moyenne spatiale permet ainsi d'obtenir une description homogénéisée des écoulements, tandis que l'aspect turbulent est traité grâce à un opérateur de moyenne statistique. Au cours du processus de moyenne, une partie des informations sur l'état microscopique est perdue. Cela se traduit, à l'échelle macroscopique, par l'apparition de termes inconnus liés à la turbulence (contraintes de Reynolds) et à la présence de la matrice solide (dispersion). C'est sur ces termes de dispersion présents dans les équations macroscopiques de quantité de mouvement et de la température que porte notre travail. Nous proposons un modèle de dispersion thermique qui permet de prédire de façon satisfaisante l'évolution de la température moyenne du fluide pour des écoulements à l'équilibre hydraulique présentant de forts gradients de température ou de flux thermique à la paroi. De plus, un modèle macroscopique de température de paroi basé sur le modèle de température moyenne est dérivé. Il permet de prédire avec précision l'évolution de la température de paroi pour des écoulements hors équilibre thermique. Afin de pouvoir traiter aussi des cas hors équilibre hydraulique, un modèle macroscopique de turbulence est proposé. Une analyse physique détaillée des transferts énergétiques a montré que c'est l'énergie dispersive qui permet de caractériser le déséquilibre hydraulique. Un modèle de turbulence prenant en compte les déséquilibres d'énergie dispersive a donc été dérivé. Il permet de prédire de façon satisfaisante la dynamique d'établissement d'écoulements entrant dans des canaux et de fournir des conditions aux limites précises à la sortie des canaux. Enfin, nous proposons un modèle dynamique pour le tenseur de dispersion basé sur l'énergie dispersive et la dissipation associée. / This works deals with the modelling if anisothermal turbulent flows in macroporous media. This topic concerns many practical applications such as heat exchangers, nuclear reactors, canopies... Our aim is to model flows through porous matrices by means of a multi-scale approach. A macroscopic description of the flows is obtained thanks to a spatial average operator, while a statistical average operator is used to handle turbulence. The successive application of both filters leads to a loss of information. Therefore, at macroscopic scale, unknown contributions linked to turbulence (Reynolds stresses) and the presence of the solid matrix (dispersion) appear. We focus on dispersion terms. We propose a thermal dispersion model for hydrodynamically established flows. Mean temperature predictions obtained with this model are very accurate for channel flows with strong temperature and wall heat flux gradients. We also derive a wall temperature model based the mean temperature model. It gives good macroscopic results for thermally developping flows. In order to be able to simulate hydrodynamically developping flows, a turbulence model is needed. A two-scale analysis of energy transfers within the flow shows that the dynamic behaviour of unbalanced flows can be described using the dispersive kinetic energy. A turbulence model that accounts for dispersive energy is derived. It predicts very well the dynamics of a flows near a channel inlet and provides accurate boundary conditions for exit flows. Finally, a dynamic model based on the dispersive energy and its dissipation rate is proposed for the dispersion tensor.

Ma thèse porte sur la relation entre la microstructure des tissus et le signal macroscopique d'imagerie par résonance magnétique de diffusion (IRMd). Les estimations des paramètres de tissus provenant de signaux mesurées expérimentalement est très important dans l'IRMd. En dépit d'une histoire de la recherche intensive dans ce domaine depuis longtemps, de nombreux aspects de ce problème inverse restent mal compris. Nous avons proposé et testé une solution approchée à ce problème, dans lequel le signal d'IRMd est d'abord approché par un modèle macroscopique appropriée, puis le paramètres effectifs de ce modèle sont estimés.Nous avons étudié deux modèles macroscopiques du signal d'IRMd. Le premier est le modèle Karger qui suppose une certaine forme de (macroscopique) diffusion de compartiments multiples et les échanges inter-compartiment, mais est soumis à la restriction d'impulsion étroite sur les impulsions de gradient de champ magnétique diffusion codant. Le deuxième est un modèle ODE de plusieurs aimantations compartiment obtenus à partir de l'homogénéisation mathématique de l'équation de Bloch-Torrey, qui n'est pas soumis à la restriction d'impulsion étroite.Tout d'abord, nous avons étudié la validité de ces modèles macroscopiques en comparant le signal d'IRMd proposée par le modèle Karger et le modèle ODE avec le signal d'IRMd de diffusion simulé sur certaines geometries de tissu relativement complexes en résolvant l'équation de Bloch-Torrey en cas de membranes semi-perméables cellule biologique. Nous avons conclu que la validité de ces deux modèles macroscopiques est limitée au cas où la diffusion dans chaque compartiment est effectivement gaussien et où l'échange inter-compartimentale peut être représenté par des termes cinétiques de premier ordre standard.Deuxièmement, en supposant que les conditions ci-dessus sur la diffusion compartimentale et l'échange inter-compartiment sont satisfaits, nous avons résolu le problème des moindres carrés associée à monter les paramètres du modèle Karger et du modèle ODE au signal simulé d'IRMd obtenu en résolvant l'équation de Bloch-Torrey microscopique. Parmi divers paramètres efficaces, nous avons examiné les fractions volumiques des compartiments intra-cellulaires et extra-cellulaires, la perméabilité de la membrane, la taille moyenne des cellules, la distance inter-cellulaire, ainsi que des coefficients de diffusion apparents. Nous avons commencé par étudier la faisabilité de la méthod des moindres carrés pour les deux groupes de geometries de tissu relativement simples. Pour le premier groupe, dans lequel les domaines sont constitués de cellules identiques ou sphériques de taille variable noyées dans l'espace extra-cellulaire, nous avons conclu que problème d'estimation de paramètres peut être résolu robuste, même en présence de bruit. Dans le second groupe, on a considéré les cellules cylindriques parallèles, qui peuvent être couverts par une couche de membrane d'épaisseur, et noyés dans l'espace extra-cellulaire. Dans ce cas, la qualité de l'estimation des paramètres dépendant fortement de la quantité de la structure cellulaire est allongée dans la direction du gradient. Dans la pratique, l'orientation des cellules allongées n'est pas de priori connue, de plus, les tissus biologiques peuvent contenir des structures allongées orientées de manière aléatoire et également en mélange avec d'autres éléments compacts (par exemple, les axones et les cellules gliales). Cette situation a été étudiée numériquement sur notre domaine le plus complexe dans lequel les couches de cellules cylindriques dans différentes directions sont mélangés avec des couches de cellules sphériques. Nous avons vérifié que certains paramètres peuvent encore être estimés assez fidèlement tandis que l'autre reste inaccessible. Dans tous les cas considérés, le modèle ODE a fourni des estimations plus précises que le modèle Karger. / My thesis focused on the relationship between the tissue microstructure and the macroscopic dMRI signal. Inferring tissue parameters from experimentally measured signals is very important in diffusion MRI. In spite of a long standing history of intensive research in this field, many aspects of this inverse problem remain poorly understood. We proposed and tested an approximate solution to this problem, in which the dMRI signal is first approximated by an appropriate macrosopic model and then the effective parameters of this model are estimated.We investigated two macroscopic models of the dMRI signal. The first is the Kärger model that assumes a certain form of (macroscopic) multiple compartmental diffusion and intercompartment exchange, but is subject to the narrow pulse restriction on the diffusion-encoding magnetic field gradient pulses. The second is an ODE model of the multiple compartment magnetizations obtained from mathematical homogenization of the Bloch-Torrey equation, that is not subject to the narrow pulse restriction.First, we investigated the validity of these macroscopic models by comparing the dMRI signal given by the Kärger and the ODE models with the dMRI signal simulated on some relatively complex tissue geometries by solving the Bloch-Torrey equation in case of semi-permeable biological cell membranes. We concluded that the validity of both macroscopic models is limited to the case where diffusion in each compartment is effectively Gaussian and where the inter-compartmental exchange can be accounted for by standard first-order kinetic terms.Second, assuming that the above conditions on the compartmental diffusion and intercompartment exchange are satisfied, we solved the least squares problem associated with fitting the Kärger and the ODE model parameters to the simulated dMRI signal obtained by solving the microscopic Bloch-Torrey equation. Among various effective parameters, we considered the volume fractions of the intra-cellular and extra-cellular compartments, membrane permeability, average size of cells, inter-cellular distance, as well as apparent diffusion coefficients. We started by studying the feasibility of the least squares solution for two groups of relatively simple tissue geometries. For the first group, in which domains consist of identical or variably-sized spherical cells embedded in the extra-cellular space, we concluded that parameters estimation problem can be robustly solved, even in the presence of noise. In the second group, we considered parallel cylindrical cells, which may be covered by a thick membrane layer, and embedded in the extra-cellular space. In this case, the quality of parameter estimation strongly depends on how much the cellular structure is elongated in the gradient direction. In practice, the orientation of elongated cells is not known a priori; moreover, biological tissues may contain elongated structures randomly oriented and also mixed with other compact elements (e.g., axons and glial cells). This situation has been numerically investigated on our most complicated domain in which layers of cylindrical cells in various directions are mixed with layers of spherical cells. We checked that certain parameters can still be estimated rather accurately while the other remains inaccessible. In all considered cases, the ODE model provided more accurate estimates than the Kärger model.

Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of bi-linear speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. This research explores the limitations of the simplified theory of kinematic waves. First this research documents a relaxation of several key constraints. In the original theory, priority was given to on-ramp merging vehicles so that they can bypass any queue at the merge. This research proposes to relax this constraint using a capacity-based weighted fair queuing (CBWFQ) merge model. In the original theory, downstream queue affects upstream traffic as a whole and exiting traffic can always be able to leave as long as it gets to the diverge. This research proposes that this diverge constraint be replaced with a contribution-based weighted splitting (CBWS) diverge model. This research proposes a revised notation system, permitting the solution techniques to be extended to freeway networks with multiple freeways and their ramps. This research proposes a generalization to permit application of the revised theory to general transportation networks. A generalized CBWFQ merge model and a generalized CBWS diverge model are formulated to deal with merging and diverging traffic. Finally, this research presents computational procedure for solving the new system of equations. Comparisons of model predictions with field observations are conducted on GA 400 in Atlanta. Investigations into the performance of the proposed CBWFQ and CBWS models are conducted. Results are quite encouraging, quantitative measures suggest satisfactory accuracy with narrow confidence interval.

Les travaux présentés dans cette thèse visent à proposer des méthodes de routage d’une population de masse à travers un réseau perturbé dont les données varient dans le temps pour l’aide à la conception de plan d’évacuation. Ce problème s’illustre parfaitement en cas de catastrophe d’origine humaine ou naturelle où les populations (potentiellement) impactées par ces sinistres doivent quitter leur lieux de vie pour une période pouvant aller d’un à plusieurs jours. Dans la littérature, ces routages de masse sont souvent modélisés comme des problèmes de flots dynamiques dont l’objectif est de minimiser la durée globale du transfert des individus depuis un certain nombre de points de départs dangereux vers des points d’arrivé sûrs. Toutefois, peu de travaux prennent en compte la notion de sécurité durant ce routage et encore moins le déploiement d’agents (policiers, sapeur-pompiers, ambulanciers,...) pouvant sécuriser et/ou faciliter le déplacement des personnes. / The work presented in this thesis aims to propose methods for routing a mass population through a disturbed network whose data vary over time. This problem can be illustrated by disasters due to humans or natural events where people (potentially) affected have to leave their living places for a period of one to several days. In the literature, mass routing are often modeled as dynamic flow problems whose objective is to minimize the overall duration of the evacuation process from a set of gathering points towards another set of shelter locations. However few papers take into account the concept of safety during this routing nor deploying task forces that can secure or facilitate this process. In this context, the safety security can be seen as a danger affecting the quality of life of people we organize the trip. In this context, the safety can be seen as a danger that influence the health of the people we are trying to evacuate. Indeed, this hazardous event can be related to a radioactive cloud, a fire, a tsunami, an earthquake or a flooding which make some of paths becoming dangerous or less usable by evacuees.