Simulação computacional como ferramenta de auxílio ao projeto: aplicação em edifícios naturalmente ventilados no clima de Natal/RN

Natural air ventilation is the most import passive strategy to provide thermal comfort in hot and humid climates and a significant low energy strategy. However, the natural ventilated building requires more attention with the architectural design than a conventional building with air conditioning systems, and the results are less reliable. Therefore, this thesis focuses on softwares and methods to predict the natural ventilation performance from the point of view of the architect, with limited resource and knowledge of fluid mechanics. A typical prefabricated building was modelled due to its simplified geometry, low cost and occurrence at the local campus. Firstly, the study emphasized the use of computational fluid dynamics (CFD) software, to simulate the air flow outside and inside the building. A series of approaches were developed to make the simulations possible, compromising the results fidelity. Secondly, the results of CFD simulations were used as the input of an energy tool, to simulate the thermal performance under different rates of air renew. Thirdly, the results of temperature were assessed in terms of thermal comfort. Complementary simulations were carried out to detail the analyses. The results show the potentialities of these tools. However the discussions concerning the simplifications of the approaches, the limitations of the tools and the level of knowledge of the average architect are the major contribution of this study

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Por uma moradia termicamente confortável: proposta de habitação de interesse social com ênfase no conforto térmico

This research covers the topic of social housing and its relation to thermal comfort, so applied to an architectural and urban intervention in land situated in central urban area of Macaíba/RN, Brazil. Reflecting on the role of design and use of alternative building materials in the search for better performance is one of its main goals. The hypothesis is that by changing design parameters and choice of materials, it is possible to achieve better thermal performance results. Thus, we performed computer simulations of thermal performance and natural ventilation using computational fluid dynamics or CFD (Computational Fluid Dynamics). The presentation of the thermal simulation followed the methodology proposed in the dissertation Negreiros (2010), which aims to find the percentage of the amount of hours of comfort obtained throughout the year, while data analysis was made of natural ventilation from images generated by the images extracted from the CFD. From model building designed, was fitted an analytical framework that results in a comparison between three different proposals for dwellings housing model, which is evaluated the question of the thermal performance of buildings, and also deals with the spatial variables design, construction materials and costs. It is concluded that the final report confirmed the general hypotheses set at the start of the study, it was possible to quantify the results and identify the importance of design and construction materials are equivalent, and that, if combined, lead to gains in thermal performance potential.