The focus of this study is the prediction of trajectories of solid particles injected into either a cylindrically- shaped solid rocket motor (SRM) or a bidirectional vortex chamber (BV). The Lagrangian particle trajectory is assumed to be governed by drag, virtual mass, Magnus, Saffman lift, and gravity forces in a Stokes flow regime. For the conditions in a solid rocket motor, it is determined that either the drag or gravity forces will dominate depending on whether the sidewall injection velocity is high (drag) or low (gravity). Using a one-way coupling paradigm in a solid rocket motor, the effects of particle size, sidewall injection velocity, and particle-to-gas density ratio are examined. The particle size and sidewall injection velocity are found to have a greater impact on particle trajectories than the density ratio. Similarly, for conditions associated with a bidirectional vortex engine, it is determined that the drag force dominates. Using a one-way particle tracking Lagrangian model, the effects of particle size, geometric inlet parameter, particle-to-gas density ratio, and initial particle velocity are examined. All but the initial particle velocity are found to have a significant impact on particle trajectories. The proposed models can assist in reducing slag retention and identifying fuel injection configurations that will ensure proper confinement of combusting droplets to the inner vortex in solid rocket motors and bidirectional vortex engines, respectively.

No description available.

Photopolymerization is the basis of several multi-million dollar industries including films and coating, inks, adhesives, fiber optics, and biomaterials. The fundamentals of the photopolymerization process, however, are not well understood. As a result, spatial variations of photopolymerization impose significant limitations on applications in which a high spatial resolution is required. To address these issues, microrheology was implemented to study the spatial and temporal effects of free-radical photopolymerization. In this work a photosensitive, acrylate resin was exposed to ultraviolet light, while the Brownian motion of micron sized, inert fluorescent tracer particles was tracked using optical videomicroscopy. Statistical analysis of particle motion yielded data that could then be used to extract rheological information about the embedding medium as a function of time and space, thereby relating UV exposure to the polymerization and gelation of monomeric resins. The effects of varying depth, initiator concentration, inhibitor concentration, composition of the monomer, and light intensity on the gelation process were studied. The most striking result is the measured difference in gelation time observed as a function of UV penetration depth. The observed trend was found to be independent of UV light intensity and monomer composition. The intensity results were used to test the accuracy of energy threshold model, which is used to empirically predict photo-induced polymerization. The results of this research affirm the ability of microrheology to provide the high spatial and temporal resolution necessary to accurately monitor the photopolymerization process. The experimental data provide a better understanding of the photo-induced polymerization, which could lead to expanded use and improved industrial process optimization. The use of microrheology to monitor photopolymerization can also aid in the development of predictive models and offer the ability to perform in-situ quality control of the process.

Behavior of particles in buoyancy driven turbulent flow at Ra ≈ 10º is investigated experimentally. The volume fraction of the particles is low enough for the inter particle influence to be neglected, the mass loading of particle is low enough that the turbulence as not modified, and the particles Reynolds numbers (Re p ) st are small enough that the wake effect can be neglected. The buoyancy driven turbulent flow is created by maintaining an unstable density difference, using NaCl dissolved in water, across the ends of a long vertical tube. There is no mean flow and the turbulence is axially homogeneous. A method for uniform introduction of the particles was devised. Glass particles (S.G=2.4-2.5) of different diameter ranges (50-400 µm) are introduced into this flow. The sizes of particles considered are less than the Kolmogrov length scale corresponding to the turbulence level. The turbulence intensity level was varied in order to match its characteristic time and velocity scale to those of the particles. The ratio of the timescales, the Stokes number; is in the range (0.01-0.55); Stokes number is defined as a ratio of the viscous relaxation time of the particle and a turbulent time scale, and represents the effect of the particle inertia in the interaction with the turbulence, Stk =τp/τk. Another important non-dimensional parameter is the velocity ratio, the k ratio of the particle settling velocity in still fluid to a characteristic turbulence velocity. The flow field is illuminated by a continuous Argon-ion laser and a PHOTRON high- speed digital camera is used for imaging. The raw images are processed to evaluate particle centers followed by their velocity measurements. The objective of the experiment is to check for the effect of the turbulent flow on the sedimentation rate of the heavy particles. This sedimentation rate is compared with the settling velocity obtained in still water. It is expected that within a certain range of Stokes numbers and velocity ratios the sedimentation rate would be substantially changed, and the spatial concentration distribution of the particles may become patchy implying that turbulence may actually inhibit rather than enhance mixing of particles. By varying the turbulence level and particle mean diameter we achieved a set of values for the particle parameters, namely St k. ≈ 0.01, 0.1, 0.14, 0.55 and velocity ratios[[Wp ] St]]≈ 0.2, .0, 0.5, 2.25 respectively. The w rms velocity ratio [[Wp ] St /wf defined as a ratio between the article terminal velocity [Wp ] St and a suitable flow velocity scale; it is a measure of the residence time of the particle in an eddy, in eddy turnover time units. In this study we have considered the turbulence r.m.s velocity for the flow velocity scale.The particle Reynolds number (Re p)st corresponding to these 4 cases were 0.2, 31.5, 4.0, 31.5. Some preliminary quantitative measurements were made only for the 150-200 µm particles and turbulence level w rms ≈ 4.0 cm/s,corresponding to Stk ≈0.14 [[Wp ] St] = 0.5. A quantitative picture was obtained for the other cases. Streak pictures for these four different groups of particles revealed that Stk and the velocity ratio [[Wp ] St ] were important in influencing the particle- w rms turbulence interaction not the Stk alone. The r.m.s velocity fluctuations of particles in both the lateral (utp) and vertical direction (wtp) measured were found to be different from those obtained in still-water case.(For equations, pl see the pdf file)

In the numerical simulation of fluid flow and solute transport in porous media, finite element method (FEM) has long been utilized and has been proven to be efficient. In this work, an alternative approach called random walk particle tracking (RWPT) method is proposed. In this method, a finite number of particles represent the distribution of a solute mass. Each particle carries a certain fraction of the total mass and moves in the porous media according to the velocity field. The proposed RWPT model is established on a scientific software platform OpenGeoSys (OGS), which is an open source initiative for numerical simulation of thermo-hydro-mechanical-chemical (THMC) processes in porous media. The flow equation is solved using finite element method in OGS. The obtained hydraulic heads are numerically differentiated to obtain the velocity field. The particle tracking method does not solve the transport equation directly but deals with it in a physically stochastic manner by using the velocity field. Parallel computing concept is included in the model implementation to promote computational efficiency. Several benchmarks are developed for the particle tracking method in OGS to simulate solute transport in porous media and pore space. The simulation results are compared to analytical solutions and other numerical methods to test the presented method. The particle tracking method can accommodate Darcy flow as it is the main consideration in groundwater flow. Furthermore, other flow processes such as Forchheimer flow or Richards flow can be combined with as well. Two applications indicate the capability of the method to handle theoretical real-world problems. This method can be applied as a tool to elicit and discern the detailed structure of evolving contaminant plumes. / Bei der numerischen Simulation von Strömung und Stofftransport in porösen Medien hat die Nutzung der Finite-Elemente-Methode (FEM) eine lange Tradition und wird sich als effizient erweisen. In dieser Arbeit wird ein alternativer Ansatz, die random walk particle tracking (RWPT) Methode vorgeschlagen. Bei diesem Verfahren stellt eine endliche Anzahl von Partikeln die Verteilung eines gelösten Stoffes dar. Jedes Teilchen trägt einen bestimmten Bruchteil der Gesamtmasse und bewegt sich in den porösen Medien gemäß des Geschwindigkeitsfeldes. Das vorgeschlagene RWPT Modell basiert auf der wissenschaftlichen Softwareplattform OpenGeoSys (OGS), die eine Open-Source-Initiative für die numerische Simulation thermo-hydro-mechanisch-chemischen (THMC) in porösen Medien darstellt. Die Strömungsgleichung wird in OGS mit der Finite-Elemente-Methode gelöst. Der Grundwasserstand wird numerisch berechnet, um das Geschwindigkeitsfeld zu erhalten. Die Partikel-Tracking-Methode löst die Transportgleichung nicht direkt, sondern befasst sich mit ihr in einer physikalisch stochastische Weise unter Nutzung des Geschwindigkeitsfeldes. Zur Berücksichtigung der Recheneffizienz ist ein Parallel Computing-Konzept in der Modell-Implementierung enthalten. Zur Simulation des Stofftransports in porösen Medien und im Porenraum wurden mehrere Benchmarks für die Partikel-Tracking-Methode in OGS entwickelt. Die Simulationsergebnisse werden mit analytischen Lösungen und andere numerische Methoden verglichen, um die Aussagefähigkeit des vorgestellten Verfahrens zu bestätigen. Mit der Partikel-Tracking-Methode kann die Darcy-Strömung gelöst werden, die das wichtigste Kriterium in der Grundwasserströmung ist. Außerdem bewältigt die Methode auch andere Strömungsprozesse, wie die Forchheimer-Strömung und die Richards-Strömung. Zwei Anwendungen zeigen die Leistungsfähigkeit der Methode bei der prinzipiellen Handhabung von Problemen der realen Welt. Die Methode kann als ein Instrument zur Aufdeckung Erkennung der detaillierte Struktur von sich entwickelnden Schadstofffahnenangewendet werden.

abstract: Microfluidics is the study of fluid flow at very small scales (micro -- one millionth of a meter) and is prevalent in many areas of science and engineering. Typical applications include lab-on-a-chip devices, microfluidic fuel cells, and DNA separation technologies. Many of these microfluidic devices rely on micron-resolution velocimetry measurements to improve microchannel design and characterize existing devices. Methods such as micro particle imaging velocimetry (microPIV) and micro particle tracking velocimetry (microPTV) are mature and established methods for characterization of steady 2D flow fields. Increasingly complex microdevices require techniques that measure unsteady and/or three dimensional velocity fields. This dissertation presents a method for three-dimensional velocimetry of unsteady microflows based on spinning disk confocal microscopy and depth scanning of a microvolume. High-speed 2D unsteady velocity fields are resolved by acquiring images of particle motion using a high-speed CMOS camera and confocal microscope. The confocal microscope spatially filters out of focus light using a rotating disk of pinholes placed in the imaging path, improving the ability of the system to resolve unsteady microPIV measurements by improving the image and correlation signal to noise ratio. For 3D3C measurements, a piezo-actuated objective positioner quickly scans the depth of the microvolume and collects 2D image slices, which are stacked into 3D images. Super resolution microPIV interrogates these 3D images using microPIV as a predictor field for tracking individual particles with microPTV. The 3D3C diagnostic is demonstrated by measuring a pressure driven flow in a three-dimensional expanding microchannel. The experimental velocimetry data acquired at 30 Hz with instantaneous spatial resolution of 4.5 by 4.5 by 4.5 microns agrees well with a computational model of the flow field. The technique allows for isosurface visualization of time resolved 3D3C particle motion and high spatial resolution velocity measurements without requiring a calibration step or reconstruction algorithms. Several applications are investigated, including 3D quantitative fluorescence imaging of isotachophoresis plugs advecting through a microchannel and the dynamics of reaction induced colloidal crystal deposition. / Dissertation/Thesis / Ph.D. Mechanical Engineering 2011

No description available.

La plupart des simulations interactives ont besoin d'un modèle de détection de collisions. Cette détection nécessite d'une part d'effectuer des requêtes de proximité entre les entités concernées et d'autre part de calculer un comportement à appliquer. Afin d'effectuer ces requêtes, les entités présentes dans une scène sont soit hiérarchisées dans un arbre ou dans un graphe de proximité, soit plongées dans une grille d'enregistrement. Nous présentons un nouveau modèle de détection de collisions s'appuyant sur deux piliers : une représentation de l'environnement par des cartes combinatoires multirésolutions et un suivi en temps réel de particules plongées dans ces cartes. Ce modèle nous permet de représenter des environnements complexes tout en suivant en temps réel les entités évoluant dans cet environnement. Nous présentons des outils d'enregistrement et de maintien de l'enregistrement de particules, d'arêtes et de surfaces dans des cartes combinatoires volumiques multirésolutions. / Most interactive simulations need a collision detection system. First, this system requires the querying of the proximity between the objects and then the computing of the behaviour to be applied. In order to perform these queries, the objects present in a scene are either classified in a tree, in a proximity graph, or embedded inside a registration grid.Our work present a new collision detection model based on two main concepts: representing the environment with a combinatorial multiresolution map, and tracking in real-time particles embedded inside this map. This model allows us to simulate complex environments while following in real-time the entities that are evolving within it.We present our framework used to register and update the registration of particles, edges and surfaces in volumetric combinatorial multiresolution maps. Results have been validated first in 2D with a crowd simulation application and then in 3D, in the medical field, with a percutaneous surgery simulation.

Le travail de cette thèse a été consacré au développement d’un nouvelle technique SELFI (pour self-interferences, auto-interférences en anglais). Cette méthode permet d’obtenir une localisation tridimensionnelle d’émetteurs fluorescents individuels. Nous avons démontré que cela permet l'imagerie super-résolue en 3D et le suivie 3D de molécules uniques en profondeur dans des échantillons biologiques denses et complexes. La technique SELFI se base sur l'utilisation des interférences auto-référencées (également appelées « auto-interférences ») pour remonter à la localisation 3D d’un émetteur en une seule mesure. Ces interférences sont générées via l’utilisation d'un réseau de diffraction placé en sortie du microscope de fluorescence : le signal de fluorescence diffracte sur le réseau et les ordres interfèrent, après une courte propagation, sur le détecteur. Les interférences ainsi formées sont décodées numériquement pour remonter à la localisation 3D d'une molécule fluorescente au sein de l'échantillon. Une molécule unique peut ainsi être localisée avec une précision d'une dizaine de nanomètre, et cela jusqu'à une profondeur d'au moins 50µm au sein d'un échantillon biologique vivant épais (par exemple un tissu biologique).En combinant la méthode SELFI à différentes techniques de super-résolution (PALM, dSTORM et uPAINT), nous montrons que cette méthode de localisation tridimensionnelle permet de retrouver la hiérarchie et l'organisation de protéines dans des objets biologiques. En effectuant du SELFI-PALM, nous avons pu observer différentes protéines des points focaux d’adhésion (talin-C terminale et paxiline) et retrouver les différences de hauteur attendues, et ceux sur des échantillons de cellules vivantes. Ces résultats confirment la résolution accessible avec la technique SELFI (environ 25nm) même pour un faible nombre de photons collectés (environ 500 photons par molécule).Nous mettons en évidence la robustesse de la technique SELFI en reconstruisant des images de super-résolution 3D de structures denses en profondeur dans des échantillons tissulaires complexes. En effectuant du SELFI-dSTORM, nous avons observé le réseau d’actine sur des cellules cultivées en surface de la lamelle dans un premier temps, et à différentes profondeurs (25 et 50 microns) au sein de tissus artificiels dans un second temps.Du suivi 3D de particule unique a aussi été effectué sein de tissus biologiques vivants. Nous avons observé la diffusion libre de quantum dots à différentes profondeurs (jusqu’à 50 microns, limité par l’objectif utilisé) dans des tranches vivantes de cerveau.Nous avons appliqué la technique SELFI à la détection de récepteurs postsynaptiques NMDA. Cela nous a permis d'observer, sur des échantillons de neurones en culture primaire mais aussi au sein de tranches de cerveaux de rats, une différence d'organisation entre les deux sous-unités GluN2A et GluN2B de ce récepteur au glutamate.Enfin, nous avons démontré l'importance de suivre l'évolution de l'environnement des échantillons biologiques vivants lors des acquisitions permettant la détection de molécules individuelles. Grâce à l'utilisation additionnelle et simultanée de l'imagerie de phase quantitative, nous avons pu étudier la dynamique de la membrane cellulaire durant l’activation par un facteur de croissance. L'analyse corrélative entre les images de phase quantitative en lumière blanche et les détections de molécules fluorescentes uniques permet d'obtenir de nouvelles informations pertinentes sur l'échantillon étudié. / The work of this thesis was devoted to the development of a new technique SELFI (for self-interferences). This method unlocks the three-dimensional localization of individual fluorescent emitters. We have demonstrated that this allows 3D super-resolved imaging and 3D tracking of single molecules deep into dense and complex biological samples. The SELFI technique is based on the use of self-referenced interference to go back to the 3D location of a emitter in a single measurement. These interferences are generated using a diffraction grating placed at the exit of the fluorescence microscope: the fluorescence signal diffracts on the grating and, after a short propagation, the orders interfere on the detector. The formed interferences are digitally decoded to extract the 3D location of a fluorescent molecule within the sample. A single molecule can thus be localized with a precision of approximatively ten nanometers up to a depth of at least 50 µm in a thick living biological sample (for example a biological tissue).By combining the SELFI method with different super-resolution techniques (PALM, dSTORM and uPAINT), we show that this three-dimensional localization method grants the access to the hierarchy and organization of proteins in biological objects. By performing SELFI-PALM, we observed different proteins of the adhesion focal points (talin C-terminal and paxilin) and found the expected elevation differences, and those within living cell samples. These results confirm the resolution capability of the SELFI technique (about 25 nm) even for a small number of photons collected (about 500photons per molecule).We highlight the robustness of the SELFI technique by reconstructing 3D super-resolution images of dense structures at depth in complex tissue samples. By performing SELFI-dSTORM, we observed the actin network in cells grown on the surface of the coverslip at first, and at different depths (25 and 50 microns) within artificial tissues in a second time.3D single particle tracking has also been performed in living biological tissues. We observed the free diffusion of quantum dots at different depths (up to 50 microns) in living brain slices.We applied the SELFI technique to the detection of NMDA postsynaptic receptors. We observed, in primary culture of neurons but also within slices of rat brains, a difference in organization between the two subunits GluN2A and GluN2B of this glutamate receptor.Finally, we show the importance of following the evolution of the living biological sample environment during the acquisition of images leading to detections of single molecules. Thanks to the additional and simultaneous use of quantitative phase imaging, we were able to study cell membrane dynamics during the activation by a growth factor. The correlative analysis between white light quantitative phase images and single fluorescent molecule detections provides new relevant information on the sample under study.

<div>Particle Image Velocimetry (PIV) is a non-invasive measurement technique which resolves the flow velocity by taking instantaneous snapshots of tracer particle motion in the flow and uses digital image cross-correlation to estimate the particle shift up to subpixel accuracy. The measurement chain incorporates numerous sets of parameters, such as the particle displacements, the particle image size, the flow shear rate, the out-of-plane motion for planar PIV and image noise to name a few, and these parameters are interrelated and influence the final velocity estimate in a complicated way. In the last few decades, PIV has become widely popular by virtue of developments in both the hardware capabilities and correlation algorithms, especially with the scope of 3-component (3C) and 3-dimensional (3D) velocity measurements using stereo-PIV and tomographic-PIV techniques, respectively. The velocity field measurement not only leads to other quantities of interest such as Pressure, Reynold stresses, vorticity or even diffusion coefficient, but also provides a reference field for validating numerical simulations of complex flows. However, such a comparison with CFD or applicability of the measurement to industrial design requires one to quantify the uncertainty in the PIV estimated velocity field. Even though the PIV community had a strong impetus in minimizing the measurement error over the years, the problem of uncertainty estimation in local instantaneous PIV velocity vectors have been rather unnoticed. A typical norm had been to assign an uncertainty of 0.1 pixels for the whole field irrespective of local flow features and any variation in measurement noise. The first article on this subject was published in 2012 and since then there has been a concentrated effort to address this gap. The current dissertation is motivated by such a requirement and aims to compare the existing 2D PIV uncertainty methods, propose a new method to directly estimate the planar PIV uncertainty from the correlation plane and subsequently propose the first comprehensive methods to quantify the measurement uncertainty in stereo-PIV and 3D Particle Tracking Velocimetry (PTV) measurements.</div><div>The uncertainty quantification in a PIV measurement is, however, non-trivial due to the presence of multitude of error sources and their non-linear coupling through the measurement chain transfer function. In addition, the advanced algorithms apply iterative correction process to minimize the residual which increases the complexity of the process and hence, a simple data-reduction equation for uncertainty propagation does not exist. Furthermore, the calibration or a reconstruction process in a stereo or volumetric measurement makes the uncertainty estimation more challenging. Thus, current uncertainty quantification methods develop a-posterior models utilizing the evaluated displacement information and combine it with either image information, correlation plane information or even calibration “disparity map” information to find the desired uncertainties in the velocity estimates.</div><div><br></div>