Numerica Corporation (United States)

O transporte rodoviário de cargas é uma prática expressiva no Brasil, exigindo uma análise aprofundada do comportamento das placas de concreto em pavimentos rígidos devido à sua alta capacidade de suportar cargas. No entanto, essa perspectiva carece de normas e métodos de dimensionamento mais sofisticados, que permitam uma análise detalhada do comportamento estrutural da placa e otimização do projeto, principalmente quanto à fadiga, que não é propriamente tratada nos métodos tradicionais, tendendo a superdimensionar o pavimento. A pesquisa realizada tem como objetivo verificar a resposta e resistência dessas placas utilizando a análise numérica como uma ferramenta e avaliando o desempenho em termos de fadiga em diferentes condições de carga. Um estudo de caso aplicou métodos empíricos tradicionais no dimensionamento de pavimentos rígidos (PCA 1966, PCA 1984, Guia AASHTO 1993 e 1998) comparando-os com a análise numérica. Os resultados indicaram a tendência dos métodos empíricos em superestimar a espessura da placa em média 47%, enquanto a análise numérica tende a oferecer maior precisão quando o critério de dimensionamento utilizado é a resistência à fadiga. A análise numérica é uma ferramenta sofisticada que parece conduzir a um dimensionamento preciso e otimizado, podendo reduzir custos e garantir um desempenho estrutural adequado. A representação da interação entre as placas de concreto e o subleito apresenta desafios, já que as condições do solo podem variar significativamente com o tempo e as condições climáticas, influenciando a resposta do pavimento. Essas limitações, somadas a outros efeitos não contemplados no presente estudo, como a erosão, exigem cautela ao interpretar os resultados da modelagem numérica.

<div class="section abstract"><div class="htmlview paragraph">The difficulties of testing a bluff automotive body of sufficient scale to match the on-road vehicle Reynolds number in a closed wall wind tunnel has led to many approaches being taken to adjust the resulting data for the inherent interference effects. But it has been impractical if not impossible to experimentally analyze the effects that are occurring on and around the vehicle when these blockage interferences are taking place. The present study is an extension of earlier work by the author and similarly to that study uses the CFD (computational fluid dynamics) analysis of several bodies of differing configurations to examine the interference phenomena in solid wall wind tunnels and the effects that they have on the pressures, forces and force increments experienced by the vehicle model. This is accomplished by executing a series of CFD configurations with varying sized cross sections from 0.2% to 16% blockage enabling an approximation of free air conditions as a reference. The earlier work is extended by using additional body configurations which include reference bodies from the Technical University of Munich, the University of Stuttgart, and Ford Motor Company to complement the simple shapes used in that study along with the use of a SAE Notchback model simple shape and a newly generated Utility Van configuration utilizing chassis components from the Ford GTU pickup. Examination is made of the physical phenomena occurring around the vehicle as the proximity to the walls and ceiling is changed holding the test section aspect ratio and length constant. Static Pressure distortions, skin friction changes, velocity distortions and the distribution of forces on the vehicle body are examined. It is intended that this dataset be used by the SAE Road Vehicle Aerodynamics Forum Committee (RVAC) and the Subsonic Aerodynamic Testing Association combined activity, Commonized Automotive Aerodynamic Test Standards, or CAATS, to evaluate and/or develop wind tunnel blockage techniques for automotive bluff bodies. The report has corrected errors contained in the original paper while revising and updating it.</div></div>

Abstract. In this paper we introduce a new software framework for the offline calculation of tracer transport in the ocean. The Fast Equilibration of Ocean Tracers Software (FEOTS) is an end-to-end set of tools to efficiently calculate tracer distributions on a global or regional sub-domain using transport operators diagnosed from a comprehensive ocean model. To the best of our knowledge, this is the first application of a Transport Matrix Model to an eddying ocean state. We demonstrate the capabilities of FEOTS in an application focused on the Argentine Basin, where intense eddy activity and the Zapiola Anticyclone lead to strong mixing of water masses. The demonstration clearly shows the benefits of the approach, while at the same time highlighting the limitations of the Impulse Response Functions approach in capturing tracer transports by a non-linear advection scheme. Our future work will focus on improving the computational efficiency of the code to reduce time-to-solution, and on using different basis functions to better represent non-linear advection operators.

A probabilistic Markov Chain (MC) surrogate model for a two-dimensional system of interacting particles within a square domain having inherent symmetries is developed. Particles are assumed to be circular and identical, colliding with each other and with rigid domain walls via perfectly elastic collisions. Simulation results over many realizations are used to develop and evaluate the surrogate MC model. The stationary quantity of interest (QoI) is the number of particles within each of nine coarser subdomains, delineated via three internal geometric symmetries. The surrogate model is used to quantify QoI mean properties and uncertainty as the particle radius is varied.

As Habilidades Sociais são consideradas essenciais no ambiente de trabalho.Jovens que possuem um bom reportório dessas habilidades têm maior probabilidadede sucesso no início da vida profissional. Objetivo. Avaliar as Habilidades Sociaisde alunos participantes do último ano do programa para jovens com inserção nomercado de trabalho, da Guarda mirim de Foz do Iguaçu, e desenvolver oficinas parapromover estas habilidades a fim de refletir no repertório destes em sua vida pessoal eprofissional. Metodologia. Pesquisa de campo de caráter descritivo, de natureza qualitativae quantitativa com 24 alunos com idades de 16 a 18 anos. Para levantamentode dados foram realizadas entrevistas com as psicólogas da instituição e aplicação doInventário de Habilidades Sociais (IHS) nos alunos. Foram realizados 9 encontros,constituídos por oficinas abordando vivência grupal, dinâmicas, palestra, vídeo, rodade conversa, jogos de cartas da autoestima e roleta das habilidades sociais. Resultados.Constatou-se através do IHS um baixo repertório de HS com um maior déficit nofator que avalia a autoexposição. Os comportamentos analisados durante as oficinasconfirmaram um alto grau de resistência nas atividades que requeriam exposição,confirmando os resultados da análise quantitativa do Inventário de Habilidades Sociais.Conclusão. O desenvolvimento das oficinas buscou contribuir para melhorar essahabilidade nos alunos e refletir de forma positiva no comportamento pessoal e profissional.Através dos relatos verbais foi possível observar a eficácia das oficinas bemcomo uma melhora inclusive na interação da turma.

ABSTRACTThe neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the object’s reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.

Inferring ego position by recognizing previously seen places in the world is an essential capability for autonomous mobile systems. Recent advances have addressed increasingly challenging recognition problems, e.g. long-term vision-based localization despite severe appearance changes induced by changing illumination, weather or season. Since robots typically move continuously through an environment, there is high correlation within consecutive sensory inputs and across similar trajectories. Exploiting this sequential information is a key element of some of the most successful approaches for place recognition in changing environments. We present a novel, neurally inspired approach that uses sequences for mobile robot localization. It builds upon Hierarchical Temporal Memory (HTM), an established neuroscientific model of working principles of the human neocortex. HTM features two properties that are interesting for place recognition applications: (1) It relies on sparse distributed representations, which are known to have high representational capacity and high robustness towards noise. (2) It heavily exploits the sequential structure of incoming sensory data. In this paper, we discuss the importance of sequence information for mobile robot localization, we provide an introduction to HTM, and discuss theoretical analogies between the problem of place recognition and HTM. We then present a novel approach, applying a modified version of HTM’s higher order sequence memory to mobile robot localization. Finally we demonstrate the capabilities of the proposed approach on a set of simulation-based experiments.

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

1.AbstractHierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.