This master's thesis project investigates the impact of hydrophobicity and topology on dry and wet macroscopic particle accumulation on surfaces. The report begins by outlining the challenges associated with this phenomenon and the existing theories and methods used to study it, including adhesion, surface engineering, the Lotus effect, and humidity. The method chapter presents the case study of grass accumulation for robotic lawn mowing application and describes experiments conducted to simulate the accumulation of grass during robotic lawn mowing. These experiments explore two different phenomena: the accumulation of grass that is already stuck to a surface and the process by which grass initially sticks to a surface. Experiments have been done to change the hydrophobicity level of certain specimens and investigate how hydrophobicity and humidity affect the stickiness of grass when already accumulated on a surface. In addition, experiments are conducted by throwing grass towards surfaces with different hydrophobicity and topology to determine the influence of these variables on accumulation. Finally, a physical field test is conducted. The findings indicate that hydrophobicity up to 108,2° water contact angle has a low impact on both static and dynamic grass accumulation. Increased hydrophobicity does not tend to decrease grass accumulation, even showing increased accumulation in some experiments. Humidity and the wetness of the grass are more important factors. Experiments have shown that a textured surface can decrease the accumulation, showing significant results in a controlled environment. However, these results did not transfer over in a significant way to a real robotic lawn mowing situation.

Recent natural disasters have highlighted the need to evacuate people as quickly as possible. During hurricane Rita in 2005, people were stuck in queue buildups and large scale congestions, due to improper use of capacity, planning and inadequate response to vehicle breakdown, flooding and accidents. Every minute is precious in situation of such disaster scenarios. Understanding evacuation demand loading is an essential part of any evacuation planning. One of the factors often understood to effect evacuation, but not modeled has been the effect of a previous hurricane. This has also been termed as the 'Katrina Effect', where, due to the devastation caused by hurricane Katrina, large number of people decided to evacuate during Hurricane Rita, which hit Texas three weeks after Katrina hit Louisiana. An important aspect influencing the rate of evacuation loading is Evacuation Preparation Time also referred to as 'Mobilization time' in literature. A methodology to model the effect of a recent past hurricane on the mobilization times for evacuees in an evacuation has been presented utilizing simultaneous estimation techniques. The errors for the two simultaneously estimated models were significantly correlated, confirming the idea that a previous hurricane does significantly affect evacuation during a subsequent hurricane. The results show that the home ownership, number of individuals in the household, income levels, and level/risk of surge were significant in the model explaining the mobilization times for the households. Pet ownership and number of kids in the households, known to increase the mobilization times during isolated hurricanes, were not found to be significant in the model. Evacuation operations are marred by unexpected blockages, breakdown of vehicles and sudden flooding of transportation infrastructure. A fast and accurate simulation model to incorporate flexibility into the evacuation planning procedure is required to react to such situations. Presently evacuation guidelines are prepared by the local emergency management, by testing various scenarios utilizing micro-simulation, which is extremely time consuming and do not provide flexibility to evacuation plans. To gain computational speed there is a need to move away from the level of detail of a micro-simulation to more aggregated simulation models. The Cell Transmission Model which is a mesoscopic simulation model is considered, and compared with VISSIM a microscopic simulation model. It was observed that the Cell Transmission Model was significantly faster compared to VISSIM, and was found to be accurate. The Cell Transmission model has a nice linear structure, which is utilized to construct Linear Programming Problems to determine optimal strategies. Optimization models were developed to determine strategies for optimal scheduling of evacuation orders and optimal crossover locations for contraflow operations on freeways. A new strategy termed as 'Dynamic Crossovers Strategy' is proposed to alleviate congestion due to lane blockages (due to vehicle breakdowns, incidents etc.). This research finds that the strategy of implementing dynamic crossovers in the event of lane blockages does improve evacuation operations. The optimization model provides a framework within which optimal strategies are determined quickly, without the need to test multiple scenarios using simulation. Destination networks are the cause of the main bottlenecks for evacuation routes, such aspects of transportation networks are rarely studied as part of evacuation operations. This research studies destination networks from a macroscopic perspective. Various relationships between network level macroscopic variables (Average Flow, Average Density and Average speed) over the network were studied. Utilizing these relationships, a "Network Breathing Strategy" was proposed to improve dissipation of evacuating traffic into the destination networks. The network breathing strategy is a cyclic process of allowing vehicles to enter the network till the network reaches congestion, which is followed by closure of their entry into the network until the network reaches an acceptable state. After which entrance into the network is allowed again. The intuitive motivation behind this methodology is to ensure that the network does not remain in congested conditions. The term 'Network Breathing' was coined due to the analogy seen between this strategy to the process of breathing, where vehicles are inhaled by the network (vehicles allowed in) and dissipated by the network (vehicles are not allowed in). It is shown that the network breathing improves the dissipation of vehicle into the destination network. Evacuation operations can be divided into three main levels: at the origin (region at risk), routes and destination. This research encompasses all the three aspects and proposes a framework to assess the whole system in its entirety. At the Origin the demand dictates when to schedule evacuation orders, it also dictates the capacity required on different routes. These breakthroughs will provide a framework for a real time Decision Support System which will help emergency management official make decisions faster and on the fly.

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The dissertation studies traffic dispersion modeling in four parts. In the first part, the dissertation focuses on the Robertson platoon dispersion model which is the most widely used platoon dispersion model. The dissertation demonstrates the importance of the Yu and Van Aerde calibration procedure for the commonly accepted Robertson platoon dispersion model, which is implemented in the TRANSYT software. It demonstrates that the formulation results in an estimated downstream cyclic profile with a margin of error that increases as the size of the time step increases. In an attempt to address this shortcoming, the thesis proposes the use of three enhanced geometric distribution formulations that explicitly account for the time-step size within the modeling process. The proposed models are validated against field and simulated data. The second part focuses on implementation of the Robertson model inside the popular TRANSYT software. The dissertation first shows the importance of calibrating the recurrence platoon dispersion model. It is then demonstrated that the value of the travel time factor β is critical in estimating appropriate signal-timing plans. Alternatively, the dissertation demonstrates that the value of the platoon dispersion factor α does not significantly affect the estimated downstream cyclic flow profile; therefore, a unique value of α provides the necessary precision. Unfortunately, the TRANSYT software only allows the user to calibrate the platoon dispersion factor but does not allow the user to calibrate the travel time factor. In an attempt to address this shortcoming, the document proposes a formulation using the basic properties of the recurrence relationship to enable the user to control the travel time factor indirectly by altering the link average travel time. In the third part of the dissertation, a more general study of platoon dispersion models is presented. The main objective of this part is to evaluate the effect of the underlying travel time distribution on the accuracy and efficiency of platoon dispersion models, through qualitative and quantitative analyses. Since the data used in this study are generated by the INTEGRATION microsimulator, the document first describes the ability of INTEGRATION in generating realistic traffic dispersion effects. The dissertation then uses the microsimulator generated data to evaluate the prediction precision and performance of seven different platoon dispersion models, as well as the effect of different traffic control characteristics on the important efficiency measures used in traffic engineering. The results demonstrate that in terms of prediction accuracy the resulting flow profiles from all the models are very close, and only the geometric distribution of travel times gives higher fit error than others. It also indicates that for all the models the prediction accuracy declines as the travel distance increases, with the flow profiles approaching normality. In terms of efficiency, the travel time distribution has minimum effect on the offset selection and resulting delay. The study also demonstrates that the efficiency is affected more by the distance of travel than the travel time distribution. Finally, in the fourth part of the dissertation, platoon dispersion is studied from a microscopic standpoint. From this perspective traffic dispersion is modeled as differences in desired speed selection, or speed variability. The dissertation first investigates the corresponding steady-state behavior of the car-following models used in popular commercially available traffic microsimulation software and classifies them based on their steady-state characteristics in the uncongested regime. It is illustrated that with one exception, INTEGRATION which uses the Van Aerde car-following model, all the software assume that the desired speed in the uncongested regime is insensitive to traffic conditions. The document then addresses the effect of speed variability on the steady-state characteristics of the car-following models. It is shown that speed variability has significant influence on the speed-at-capacity and alters the behavior of the model in the uncongested regime. A method is proposed to effectively consider the influence of speed variability in the calibration process in order to control the steady-state behavior of the model. Finally, the effectiveness and validity of the proposed method is demonstrated through an example application. / Ph. D.

Thesis (MComm (Business Management))--University of Stellenbosch, 2007. / ENGLISH ABSTRACT: his study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations to explain the behaviour of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study general approaches are reviewed that may be used to model financial markets and the volatility of price processes in particular. Special emphasis is placed on the modelling of highfrequency data sets and exchange rate data. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings of this thesis widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics in terms of the formation of discrete states, the detection of the near zone effect as proclaimed by Shnoll et al., the periodic recurrence of histogram shapes as well as the synchronous variation in data sets measured in the same time intervals.

In this thesis we investigate strongly-correlated states of ultracold bosonic atoms in rotating ring lattices and arrays of double-well potentials. In the first part of the thesis, we study the tunneling dynamics of ultracold bosons in double-well potentials. In the non-interacting limit single-particle transitions dominate, while in the interaction-dominated regime correlated tunneling of all particles prevails. At intermediate times of the many-particle flopping process correlated states occur, but the timescales of these processes increase dramatically with the number of particles. Using an array of double-well potentials, a large number of such few-particle superposition states can be produced in parallel. In the second part of the thesis, we study the effects of rotation on ultracold bosons confined to one-dimensional ring lattices. We find that at commensurate filling there exists a critical rotation frequency, at which the ground state of the weakly-interacting gas is fragmented into a macroscopic superposition of different quasi-momentum states. We demonstrate that the generation of such superposition states using slightly non-uniform ring lattices has several practical advantages. Moreover, we show that different quasi-momentum states can be distinguished in time-of-flight absorption imaging and propose to probe correlations via the many-body oscillations induced by a sudden change in the rotation frequency. Finally, we compare these macroscopic superposition states to those occurring in superconducting quantum interference devices. In the third part of the thesis, we demonstrate the creation of entangled states with ultracold bosonic atoms by dynamical manipulation of the shape of the lattice potential. To this end, we consider an optical superlattice that allows both the splitting of each site into a double-well potential and the variation of the height of the potential barrier between the sites. We show how to use this array of double-well potentials to perform entangling operations between neighboring qubits encoded on the Zeeman levels of the atoms. As one possible application, we present a method of realizing a resource state for measurement-based quantum computation via Bell-pair measurements. In the final part of the thesis, we study ultracold bosons on a two-dimensional square lattice in the presence of an effective magnetic field and point out a couple of features this system has in common with ultracold bosons in one-dimensional rotating ring lattices.

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.

Movité par des questions posées par F. Santambrogio, cette thèse est dédiée à l'étude de jeux à champ moyen et des modèles impliquant le transport optimal avec contraintes de densité. A fin d'étudier des modèles de MFG d'ordre deux dans l'esprit des travaux de F. Santambrogio, on introduit en tant que brique élementaire un modèle diffusif de mouvement de foule avec contraintes de densité (en généralisant dans une sense les travaux de Maury et al.). Le modèle est décrit par l'évolutions de la densité de la foule, qui peut être vu comme une courbe dans l'espace de Wasserstein. Du point de vu EDP, ça correspond à une équation de Fokker-Planck modifiée, avec un terme supplémentaire, le gradient d'une pression (seulement dans la zone saturée) dans le drift. En passant par l'équation duale et en utilisant des estimations paraboliques bien connues, on démontre l'unicité du pair densité et pression. Motivé initialement par l'algorithm de splitting (utilisé dans le résultat d'existence ci-dessus), on étudie des propriétés fines de la projection de Wasserstein en dessous d'un seuil donné. Intégrant cette question dans une classe plus grande de problèmes impliquant le transport optimal, on démontre des estimations BV pour les optimiseurs. D'autres applications possibles (en transport partiel, optimisation de forme et problèmes paraboliques dégénérés) de ces estimations BV sont également discutées.En changeant le point de vu, on étudie également des modèles de MFG variationnels avec contraintes de densité. Dans ce sens, les systèmes de MFG sont obtenus comme conditions d'optimalité de premier ordre pour deux problèmes convexes en dualité. Dans ces systèmes un terme additionnel apparaît, interpreté comme un prix à payer quand les agents passent dans des zones saturées. Premièrement, en profitant des résultats de régularité elliptique, on montre l'existence et la caractérisation de solutions des MFG de deuxième ordre stationnaires avec contraintes de densité. Comme résultat additionnel, on caractérise le sous-différentiel d'une fonctionnelle introduite par Benamou-Brenier pour donner une formulation dynamique du problème de transport optimal. Deuxièmement, (basé sur une technique de pénalisation) on montre qu'une classe de systèmes de MFG de premier ordre avec contraintes de densité est bien posée. Une connexion inattendu avec les équations d'Euler incompressible à la Brenier est égalment donnée. / Motivated by some questions raised by F. Santambrogio, this thesis is devoted to the study of Mean Field Games and models involving optimal transport with density constraints. To study second order MFG models in the spirit of the work of F. Santambrogio, as a possible first step we introduce and show the well-posedness of a diffusive crowd motion model with density constraints (generalizing in some sense the works by B. Maury et al.). The model is described by the evolution of the people's density, that can be seen as a curve in the Wasserstein space. From the PDE point of view, this corresponds to a modified Fokker-Planck equation, with an additional gradient of a pressure (only living in the saturated zone) in the drift. We provide a uniqueness result for the pair density and pressure by passing through the dual equation and using some well-known parabolic estimates. Initially motivated by the splitting algorithm (used for the above existence result), we study some fine properties of the Wasserstein projection below a given threshold. Embedding this question into a larger class of variational problems involving optimal transport, we show BV estimates for the optimizers. Other possible applications (for partial optimal transport, shape optimization and degenerate parabolic problems) of these BV estimates are also discussed.Changing the point of view, we also study variational Mean Field Game models with density constraints. In this sense, the MFG systems are obtained as first order optimality conditions of two convex problems in duality. In these systems an additional term appears, interpreted as a price to be paid when agents pass through saturated zones. Firstly, profiting from the regularity results of elliptic PDEs, we give the existence and characterization of the solutions of stationary second order MFGs with density constraints. As a byproduct we characterize the subdifferential of a convex functional introduced initially by Benamou-Brenier to give a dynamic formulation of the optimal transport problem. Secondly, (based on a penalization technique) we prove the well-posedness of a class of first order evolutive MFG systems with density constraints. An unexpected connection with the incompressible Euler's equations à la Brenier is also given

This thesis, submitted to the University of Manchester, covers a range of topics related to current research in two-dimensional materials under the title: 'Investigations into the interfacial interaction of graphene with hexagonal boron nitride.'In the last decade, two-dimensional materials have become a rich source of original research and potential applications. The main advantage lies in the ability to produce novel composite structures, so-called 'layered heterostructures', which are only a few atomic layers thick. One can utilise the unique properties of several species of crystal separately, or how they interact to realise a diverse range of uses. Two such crystals are graphene and hexagonal boron nitride. Hexagonal boron nitride has, so far, been used primarily as a substrate for graphene, allowing researchers to get the most out of graphene's impressive individual properties. However, in this thesis, the non-trivial van der Waals interaction between graphene and hexagonal boron nitride is examined. The interface potential reveals itself as a relatively large-scale, orientation-dependant superlattice, which is described in chapters 1 and 2.I In Chapter 4, the effect of this superlattice is examined by measurement of its effect upon the electrons in graphene, where its modulation leads to the creation of second and third generation Dirac points, revealing Hofstadter's Butterfly. As well as an excellent example of the physics possible with graphene, it also presents a new tool with which to create novel devices possessing tailored electronic properties. II In chapter 5, the consequential effect of the superlattice potential on the structure of graphene is studied. Results are discussed within the framework of the Frenkel-Kontorova model for a chain of atoms on a static background potential. Results are consistent with relaxation of the graphene structure leading to the formation of a commensurate ground state. This has exciting consequences for the production of heterostructures by demonstrating that alignment angle can have large effects upon the physical properties of the crystals. III In chapter 6, the van der Waals potential is shown to be responsible for the self-alignment of the two crystals. This effect is important for the fabrication of perfectly aligned devices and may lead to new applications based on nanoscale motion.

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.