Permeability is an important property of a porous medium and it controls the flow of fluid through the medium. Particle characteristics are known to affect the value of the permeability. However, experimental investigation of the effects of these particle characteristics on the value of permeability is time-consuming while analytical predictions have been reported to overestimate it leading to inefficient design. To overcome these challenges, there is the need for the development of new models that can predict permeability based on input variables and process conditions. In this research, data from experiments, Computational Fluid Dynamics (CFD) and literature were employed to develop new models using Multivariate Regression (MVR) and Artificial Neural Networks (ANNs). Experimental measurements of permeability were performed using high and low shear separation processes. Particles of talc, calcium carbonate and titanium dioxide (P25) were used in order to study porous media with different particle characteristics and feed concentrations. The effects of particle characteristics and initial stages of filtration as well as the reliability of filtration techniques (constant pressure filtration, CPF and constant rate filtration, CRF) were investigated. CFD simulations were also performed of porous media for different particle characteristics to generate additional data. The regression and ANN models also included permeability data taken from reliable literature sources. Particle cluster formation was only found in P25 leading to an increase of permeability especially in sedimentation. The constant rate filtration technique was found more suitable for permeability measurement than constant pressure. Analyses of data from the experiments, CFD and correlation showed that Sauter mean diameter (ranging from 0.2 to 168 μm), the fines ratio (x50/x10), particle shape (following Heywood s approach), and voidage of the porous medium (ranging from 98.5 to 37.2%) were the significant parameters for permeability prediction. Using these four parameters as inputs, performance of models based on linear and nonlinear MVR as well as ANN were investigated together with the existing analytical models (Kozeny-Carman, K-C and Happel-Brenner, H-B). The coefficient of correlation (R2), root mean square error (RMSE) and average absolute error (AAE) were used as performance criteria for the models. The K-C and H-B are two-variable models (Sauter mean diameter and voidage) and two variables ANN and MVR showed better predictive performance. Furthermore, four-variable (Sauter mean diameter, the x50/x10, particle shape, and voidage) models developed from the MVR and ANN exhibit excellent performance. The AAE was found with K-C and H-B models to be 35 and 40%, respectively while the results of using ANN2 model reduced the AAE to 14%. The ANN4 model further decreased the AAE to approximately 9% compared to the measured results. The main reason for this reduced error was the addition of a shape coefficient and particle spread (fine ratio) in the ANN4 model. These two parameters are absent in the analytical relations, such as K-C and H-B models. Furthermore, it was found that using the ANN4 (4-5-1) model led to increase in the R2 value from 0.90 to 0.99 and significant decrease in the RMSE value from 0.121 to 0.054. Finally, the investigations and findings of this work demonstrate that relationships between permeability and the particle characteristics of the porous medium are highly nonlinear and complex. The new models possess the capability to predict the permeability of porous media more accurately owing to the incorporation of additional particle characteristics that are missing in the existing models.

This thesis proposes a framework for controlling mobile robot networks based on a fluid dynamics paradigm. The approach is inspired by natural behaviors of fluids demonstrating desirable characteristics for collective robots. The underlying mathematical formalism is developed through establishing analogies between fluid bodies and multi-robot systems such that robots are modeled as fluid elements that constitute a fluid body. The governing equations of fluid dynamics are adapted to multi-robot systems and applied on control of robots. The model governs flow of a robot based on its local interactions with neighboring robots and surrounding environment. Therefore, it provides a layer of decentralized reactive control on low level behaviors, such as obstacle avoidance, deployment, and flow. These behaviors are inherent to the nature of fluids and provide emergent coordination among robots. The framework also introduces a high-level control layer that can be designed according to requirements of the particular task. Emergence of cooperation and collective behavior can be controlled in this layer via a set of parameters obtained from the mathematical description of the system in the lower layer. Validity and potential of the approach have been experimented through simulations primarily on two common collective robotic tasks / deployment and navigation. It is shown that gas-like mobile sensor networks can provide effective coverage in unknown, unstructured, and dynamically changing environments through self-spreading. On the other hand, robots can also demonstrate directional flow in navigation or path following tasks, showing that a wide range of multi-robot applications can potentially be developed using the framework.

In the field of physical chemistry, the relatively recently developed technique of velocity-map imaging has allowed chemical dynamics to be explored with a greater depth than could be previously achieved using other methods. Capturing the scattering image associated with the products resulting from fragmentation of a molecule allows the dissociative pathways and energy landscape to be investigated. In the study of particle physics, the neutron has become an irreplaceable spectroscopic tool due to the unique nature of the interaction with certain materials. Neutron spectroscopy is a non-destructive imaging technique that allows a number of properties to be discerned, including chemical identification, strain tensor measurements and the identification of beneath the sample surface using radiography and tomography. In both of these areas, as well as a multitude of other disciplines, a flight tube is used to separate particles, distinguishing them based upon their mass in the former case and their energy in the latter. The experiments can be vastly enhanced by the ability to record both the position and arrival time of the particle of interest. This thesis describes several new developments made in instrumentation for experiments involving time-of-flight particle imaging. The first development described is the construction of a new velocity-map imaging instrument that utilises electron ionisation to perform both steps of molecular fragmentation and ionisation. Data from CO2 is presented as an example of the ability of the instrument, and a preliminary analysis of the images is performed. The second presented project is the design of a time-resolved and position-resolved detector developed for ion imaging experiments. The hardware, software and firmware are described and presented alongside data from a variety of the experiments showcasing the breadth of investigations that are possible using the sensor. Finally, the modifications made to the detector to allow time-resolved neutron imaging are detailed, with an in-depth description of the various proof-of-concept experiments carried out as part of the development process.

Orientador: Luiz Otávio Saraiva Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-26T05:32:16Z (GMT). No. of bitstreams: 1 Horta_AgnusAzevedo_M.pdf: 4762311 bytes, checksum: 1bcc55cdb06dfc294f352fc1ac27c0ad (MD5) Previous issue date: 2014 / Resumo: Este trabalho tem como objetivo principal a implementação do motor de simulação de um framework de simulação com partículas, o motor de simulação utilizará o método DPD (Dissipative Particle Dynamics), baseando-se no paradigma de programação orientada a objeto (POO) e no uso de estruturas de dados otimizadas. O motor de simulação foi escrito em linguagem C++. A concepção do sistema foi realizada de forma a facilitar e promover a reutilização e manutenção do código. Buscou-se, também, a flexibilidade e generalização através do uso da linguagem Python na geração dos arquivos de entrada correspondentes a distribuição espacial das partículas, sendo utilizada a linguagem de marcação XML (eXtensible Markup Language) na estruturação dos arquivos resultantes da simulação. No final, o motor de simulação é avaliado aplicando o problema do fluxo de um fluído entre placas paralelas e o resultado comparado com os resultados obtidos no simulador Hoomd-Blue / Abstract: This work aims the implementation of the simulation of a simulation framework with particle engine, the simulation engine will use DPD (Dissipative Particle Dynamics) method, based on the paradigm of object oriented programming (OOP) and use optimized data structures. The simulation engine is written in C language ++. The system design was performed in order to facilitate and promote the reuse and maintainability of the code. Also, we sought flexibility and generalization through the use of Python in the generation of the input files corresponding to the spatial distribution of particles, (eXtensible Markup Language) XML markup being used in structuring the files resulting from the simulation. At the end, the simulation engine is the problem of applying rated flow of a fluid between parallel plates and the result compared with the results obtained in the simulation Hoomd-Blue / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

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Für die effektive numerische Berechnung von Strömungsphänomenen im frühen Entwicklungsprozess wurde methodisch das Zusammenwirken verschiedener Berechnungstools hinsichtlich der Berechnungsdauer und Güte auf handelsüblichen Desktoprechnern untersucht. Dabei ist die Skalierbarkeit der Berechnungsmodelle auf unterschiedliche geometrische Skalen sowie die Umsetzungsmöglichkeit der Berechnung kombinierter Fluidphasen essenziell. Für die Umsetzung wurden erste Grundlagenuntersuchungen an einfachen Geometrien experimentell und virtuell durchgeführt sowie die Ergebnisse gegenübergestellt. Daraus konnten Anforderungen abgeleitet und bereits im Ausschlussverfahren für das Projekt ungeeignete Software detektiert werden. Weiterhin wurden die Simulationsparameter, aufbauend auf den Grundlagenuntersuchungen, an die komplexen Geometrien von Bauteilen/Baugruppen bis hin zum Viertelfahrzeug angepasst. Somit konnten für die jeweiligen Berechnungstools die Stärken hinsichtlich der Anforderungen an die Geometrie und die physikalischen Randbedingungen abgeleitet werden. Anschließend wurde eine Software entwickelt, welche automatisiert Ergebnisse unterschiedlicher Softwarelösungen und geometrischer Skalen verarbeitet, interpoliert und anschließend auf gewählten Schnittstellen bereitstellt. Somit lassen sich die effektivsten Berechnungsmethoden miteinander verknüpfen. Zudem konnte im Laufe der Bearbeitungszeit eine Software (PreonLab) zum Einsatz kommen, welche hinreichend genau und dennoch zeitlich effektiv komplexe Strömungsphänomene berechnen kann. Im Zuge der Projektbearbeitung konnten somit Anforderungen als auch Randbedingungen für die numerische Berechnung komplexer Strömungsphänomene am Gesamtfahrzeug beschrieben werden.

Thermostated time evolutions are on a firm ground and widely used in classical molecular dynamics (MD) simulations. Hamilton´s equations of motion are supplemented by time-dependent pseudofriction terms that convert the microcanonical isoenergetic time evolution into a canonical isothermal time evolution, thus permitting the calculation of canonical ensemble averages by time averaging. However, similar methods for quantum MD schemes are still lacking. Given the rich dynamical behavior of ultracold trapped quantum gases depending on the value of the s-wave scattering length, it is timely to investigate how classical thermostating methods can be combined with powerful approximate quantum dynamics schemes to deal with interacting quantum systems at finite temperature. In this work, the popular method of Nose and Hoover to create canonically distributed positions and momenta in classical MD simulations is generalized to a genuine quantum system of infinite dimensionality. We show that for the quantum harmonic oscillator, the equations of motion in terms of coherent states may be modified in a Nose-Hoover manner to mimic the coupling of the system to a thermal bath and create a quantum canonical ensemble. The method is developed initially for a single particle and then generalized to the case of an arbitrary number of identical quantum particles, involving entangled distribution functions. The resulting isothermal equations of motion for bosons and fermions contain additional terms leading to Bose-attraction and Pauli-blocking, respectively. Questions of ergodicity are discussed for different coupling schemes. In the many-particle case, the superiority of the Nose-Hoover technique to a Langevin approach is demonstrated. In addition, the work contains an investigation of the Grilli-Tosatti thermostating method applied to the harmonic oscillator, and calculations for quantum wavefunctions moving with a time-invariant shape in a harmonic potential.