Les poudres propulsives pour armes sont des matériaux énergétiques dont la combustion permet l’accélération de projectiles jusqu’à des vitesses importantes. Ces matériaux énergétiques sensibles peuvent être soumis à de fortes contraintes (chocs, impacts et incendies) lors de leur utilisation. Le remplacement de certaines substances entrant dans leur formulation permet de diminuer leur vulnérabilité. En conséquence, ces poudres propulsives présentent une dynamique d’allumage différente. Ce travail de recherches est consacré à l’allumage des poudres propulsives pour armes et se présente en quatre chapitres. Un état de l’art sur le sujet est réalisé. Il porte en particulier sur la phénoménologie de l’allumage, la caractérisation de poudres propulsives, et la modélisation de l’allumage. Deux poudres propulsives sont expérimentalement analysées par thermogravimétrie, calorimétrie et spectrométrie de masse. Cette analyse permet de caractériser les différentes étapes cinétiques de la dégradation thermique de ces poudres propulsives. Un code de modélisation biphasique 2D est développé pour servir de support à la comparaison de modèles d’allumage. Le modèle implémenté décrit la chambre de combustion du canon dans les premières phases du coup de canon, lorsque le lit de poudre propulsive est compacté et que les gaz issus du dispositif pyrotechnique d’allumage le parcourent. Un modèle d’allumage est développé à partir des résultats expérimentaux obtenus au deuxième chapitre. Le code de calcul précédemment évoqué permet de comparer l’influence de différents modèles sur la propagation de l’allumage. / Gun Propellants are energetic materials whose combustion can accelerate projectiles to high speeds. These sensitive energetic materials can be subjected to high stresses (shocks, impacts and fires), potentially able to ignite them. The modification of their chemical formulation reduces such vulnerability. Consequently, these propellants have different ignition dynamics. This work focuses on the ignition of gun propellants and comes in four chapters. A state of the art on the subject is firstly made. It focuses on the phenomenology of the ignition and on energetic materials experimental characterization, and modeling of their ignition. Two gun propellants are experimentally analyzed by thermogravimetry, calorimetry and mass spectrometry. This analysis allows characterizing the various stages of the degradation kinetics of these propellants. A 2D biphasic modeling code was developed to provide support for the comparison of ignition models. It describes the combustion chamber in the early stages of the gun firing, when the propellant bed is compacted and the gases from the pyrotechnic igniter are flowing through it. An ignition model is developed from the experimental data obtained in the second chapter. The previously mentioned modeling code allows comparing the influence of different ignition models on the spreading speed of the ignition signal through the packed bed of propellant.

Made available in DSpace on 2017-07-10T18:07:58Z (GMT). No. of bitstreams: 1 Lucas Maycon Hoff Zeni.pdf: 1421377 bytes, checksum: 75c6a9407a955e26c7fd4db2939b1b79 (MD5) Previous issue date: 2012-02-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Configuration of fixed bed that operates with biphasic flow is used in industrial operations such as the Fischer-Tropsch, hydrogenation, and residual water treatments. Vital information for the project and operation of this type of bed is in its characteristics fluid-dynamic and among these characteristics the flow regime because these have a direct influence transferring heat and mass present in the bed. In the two-phase flow with ascendant flow through fixed bed, three distinct regimes can be identified: the bubble regime, for low gas flow; pulsating regime, for moderate liquid and gas flow; and spray regime; for low flow of liquid and high flow rates of gas. Although there are different techniques to determine flow regimes, the most used is the visual identification. Thus, this research aims to develop, by using artificial neural networks (ANNs) a way to determine, for a given set of liquid-gas flow what out-flow regime the bed presents. To do so, firstly, the out-flow regime were identified by using water and air, respectively flux mass flowing varying from 2 to 16.5 kg.m-2.s-1 and from 0 to 0.6 kg.m-2.s-1, flowing up-words through a fixed bed packed with glass spheres measuring from 2.7 to 3.5 mm of diameter. The network proposed to identify the regimes contains Multiple Layers Perceptron architecture (PML) trained by the back propagation algorithm put together by applying the Multiple Back-Propagation (MBP) software, version 2.2.3 consistently with two input neurons, two intermediate layers, and four output neurons. The number of neurons of the intermediate layers was assorted to find out the best configuration. As activation of function, logistic, tangent, hyperbolic, and Gaussian were tested. Observed results showed that it is possible the identification of regimes through neural networks and among those tested the one that showed the best performance was the one that used the hyperbolic-tangent activation function; 10 neurons in the first hidden layer, and 12 neurons in the second hidden layer. / A configuração de leito fixo que opera com escoamento bifásico é muito utilizada em operações industriais, tais como síntese de Fischer-Tropsch, hidrogenação e tratamento de águas residuais. Uma informação vital para projeto e operação deste tipo de leito está nas características fluidodinâmicas, e dentre estas características podem ser citados os regimes de escoamento, pois estes influenciam diretamente nas transferências de calor e massa presentes no leito. No escoamento bifásico com fluxo ascendente através de leito fixo podem ser identificados três regimes distintos: regime bolha, para baixas vazões de gás; regime pulsante, para vazões moderadas de líquido e gás; e regime spray, para baixas vazões de líquidos e altas vazões de gás. Apesar de haver diferentes técnicas para a determinação dos regimes de escoamento, a mais empregada é a identificação visual. Sendo assim, esta pesquisa tem por objetivo desenvolver, por meio da utilização de redes neurais artificiais (RNA s), uma maneira de determinar, para um dado conjunto de vazões gás-líquido, qual regime de escoamento o leito apresenta. Para isto, os regimes de escoamento primeiramente foram identificados utilizando água e ar, respectivamente com fluxo mássico variando de 2 a 16,5 kg.m-2.s-1 e de 0 a 0,6 kg.m-2.s-1, escoando em fluxo ascendente por meio de um leito fixo recheado com esferas de vidro de diâmetro entre 2,7 e 3,5 mm. A rede proposta para a identificação dos regimes possui arquitetura perceptron de múltiplas camadas (MLP) treinada pelo algoritmo backpropagation e foi montada utilizando o programa freeware Multiple Back-Propagation (MBP) versão 2.2.3 sempre com dois neurônios de entrada, duas camadas intermediárias e quatro neurônios de saída. O número de neurônios das camadas intermediárias foi variado a fim de descobrir a melhor configuração. Como função de ativação, foram testadas as funções logística, tangente hiperbólica e gaussiana. Os resultados observados mostram que é possível a identificação dos regimes por meio de redes neurais e dentre as configurações testadas, a que apresentou melhor desempenho foi a rede que utilizou a função de ativação tangente hiperbólica, 10 neurônios na primeira camada oculta e 12 neurônios na segunda camada oculta.

<p dir="ltr">Decades of semiconductor technology scaling and breakthroughs in communication technology have miniaturized computing, embedding it everywhere, enabling the development of smart things connected to the internet, forming the Internet of Things. Further miniaturization of devices has led to an exponential increase in the number of devices in and around the body in the last decade, forming a subset of IoT which is increasingly becoming popular as the Internet of Bodies (IoB). The gradual shift from the current form of human-electronics coexistence to human-electronics cooperation, is the vision of Internet of Bodies (IoB). This vision of a connected future with devices in and around our body talking to each other to assist their day-to-day functions demands energy efficient means of communication. Electro-Quasistatic Human Body Communication (EQS-HBC) has been proposed as an exciting alternative to traditional Radio Frequency based methodologies for communicating data around the body. In this dissertation, we expand the boundaries of wearable and implantable IoB nodes using Electro-Quasistatic Human Body Communication and Powering by developing advanced channel models and demonstrating novel applications.</p><p dir="ltr">Leveraging the advanced channel models developed for wearable EQS-HBC, we demonstrate wearable applications like ToSCom which extend the use cases of touchscreens to beyond touch detection and location to enable high-speed communication strictly through touch. We further demonstrate an application of EQS Resonant Human Body Powering to demonstrate Step-to-Charge, allowing mW-scale wireless power transfer to wearable devices. With increasing connected implanted healthcare devices becoming a part of the IoB space, we benchmark RF-based technologies for In-Body to Out-of-Body (IBOB) communication using novel in-vivo experiments. We then explore EQS-HBC in the realm of IBOB communication using advanced channel modeling, revealing its potential for low-power and physically secure data transfer from implantable devices to wearable nodes on the body, demonstrating its potential in extending the battery life span of implantable nodes. Finally, an overview of the potential of IoB devices is analyzed with the use of EQS-HBC where we propose a human-inspired distributed network of IoB nodes which brings us a step closer to the potential for perpetually operable devices in and around the body.</p>