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Biomass is considered a biorenewable alternative energy resource that can potentially reduce the use of natural gas and provide low cost power production or process heating needs. Biomass hydrodynamics in a fluidized bed are extremely important to industries that are using biomass material in gasfication processes to yield high quality producer gas. However, biomass particles are typically difficult to fluidize due to their peculiar shape and a second inert material, such as sand, is typically added to the bed. The large differences in size and density between the biomass and inert particles lead to nonuniform distribution of the biomass within the fluidized bed, and particle interactions and mixing become major issues. The main goal of this research was to use CFD as a tool for modeling and analyzing the hydrodynamic behavior of biomassas a single material or as part of a mixture in a fluidized bed. The first part of this research focused on the characterization of biomass particles in a fluidized bed and validation of a numerical model with experimental results obtained from pressure measurements and CT and X-ray radiograph images. For a 2D fluidized bed of glass beads, the pressure drop, void fraction and mean bed height expansion were in quantitative agreement between the experiments and simulations using Syamlal-O'Brien and Gidaspow drag models. It was encouraging that the Gidaspow model predictions were in close agreement because the model does not require knowing the minimum fluidization as an input. Ground walnut shells were used to represent biomass because the material fluidizes uniformly and is classified as a Geldart type B particle. Two-dimensional simulations of ground walnut shells were analyzed to determine parameters that cannot easily be measured experimentally. The parametric study for ground walnut shell indicated that the material can be characterized with a medium sphericity (~0.6) and a relatively large coefficient of restitution (~0.85). In the second part of this work numerical simulations of a ground walnut shell fluidizing bed with side air injection were compared to CT data for the gas-solid distribution to demonstrate the quantitative agreement for bed fluidization. The findings showed that 2D simulations overpredicted the fluidized bed expansion and the results did not demonstrate a uniformly fluidizing bed. The 3D simulations compared well for all cases. This study demonstrates the importance of using a 3D model for a truly 3D flow in order to capture the hydrodynamics of the fluidized bed for a complicated flow and geometry. Finally, CFD modeling of pressure fluctuations was performed on sand and cotton-sand fluidized beds operating at inlet velocities ranging from 1.0-9.0Umf with the objective of predicting characteristic features of bubbling, slugging, and turbulent fluidization regimes. It was determined that the fluidized bed can be modeled using MUSCL discretization and the Ahmadi turbulence model. Three-dimensional sand fluidized beds were simulated for different fluidization regimes. Fluidized beds for all the regimes behaved as second-order dynamic systems. Bubbling fluidized beds showed one broad peak with a maximum at 2.6 Hz while slugging and turbulent showed two distinct peaks. It was observed that the peak at low frequency increased in magnitude as the flow transitioned from a slugging to a turbulent fluidization regime. CFD simulations of fluidized beds with the purpose of studying pressure fluctuations have demonstrated to be a useful tool to obtain hydrodynamic information that will help determine the fluidization regime. Prediction of slugging and turbulent fluidization regimes using CFD have not been reported to date. The work presented here is the first of its kind and can be an important advantage when designing a reactor and evaluating different operation conditions without the need to test them in a pilot plant or a prototype. / Ph. D.

Séries temporais financeiras, como índices de mercado e preços de ativos, são produzidas por interações complexas dos agentes que participam do mercado. As propriedades fractais e multifractais destas séries fornecem evidências para detectar com antecedência a ocorrência de movimentos bruscos de mercado (crashes). Tais evidências são obtidas ao aplicar o conceito de Calor Específico Análogo C(q), proveniente da equivalência entre a Multifractalidade e Termodinâmica. Na proximidade de um crash, C(q) apresenta um ombro anômalo à direita de sua curva, enquanto que na ausência de um crash, possui o formato parecido com uma distribuição gaussiana. Com base neste comportamento, o presente trabalho propõe um novo indicador temporal IA(i), definido como a taxa de variação da área sob a curva de C(q). O indicador foi construído por intermédio de uma janela temporal de tamanho s que se movimenta ao longo da série, simulando a entrada de dados na série ao longo do tempo. A análise de IA(i) permite detectar com antecedência a ocorrência de grandes movimentos, como os famosos crashes de 1929 e 1987 para os índices Dow Jones, S&P500 e Nasdaq. Além disso, a análise simultânea de medidas como a Energia Livre, a Dimensão Multifractal e o Espectro Multifractal, sugerem que um crash de mercado se assemelha a uma transição de fase. A robustez do método para diferentes ativos e diferentes períodos de tempo, demonstra a importância dos resultados. Além disso, modelos estatísticos não lineares para a volatilidade foram empregados no trabalho para estudar grandes flutuações causadas por crashes e crises financeiras ao longo do tempo. / Financial time series such as market index and asset prices, are produced by complex interactions of agents that trade in the market. The fractal and multifractal properties of these series provides evidence for early detection of the occurrence of sudden market movements (crashes). This evidence is obtained by applying the concept of Analog Specific Heat C(q), from the equivalence between the Multifractal Analysis and Thermodynamics. In the vicinity of a crash, C(q) exhibits a shoulder at the right side of its curve, while in the absence of a crash, C(q) presents a form similar to a Gaussian distribution curve. Based on this behavior, it is proposed in this work a new temporal indicator IA(i) defined here as the area variation rate over the Specific Heat function. We have constructed the mentioned indicator from a window of data with the first points (size s), that moves throughout the series, simulating the actual input of data over time. The indicator IA(i) allows one detecting in advance the occurrence of large financial market movements, such as those occurred in 1929 and 1987 for the marked indexes Dow Jones, Nasdaq and S&P500. Moreover, the simultaneous analysis of measures such as the Free Energy, Multifractal Dimension and Multifractal Spectrum suggest that a market crash resembles a phase transition. The robustness of the method for others assets and different periods of time demonstrates the importance of the results. Moreover, nonlinear statistical models for volatility have been employed in the work to study large fluctuations caused by crashes and financial crises over time.

Séries temporais financeiras, como índices de mercado e preços de ativos, são produzidas por interações complexas dos agentes que participam do mercado. As propriedades fractais e multifractais destas séries fornecem evidências para detectar com antecedência a ocorrência de movimentos bruscos de mercado (crashes). Tais evidências são obtidas ao aplicar o conceito de Calor Específico Análogo C(q), proveniente da equivalência entre a Multifractalidade e Termodinâmica. Na proximidade de um crash, C(q) apresenta um ombro anômalo à direita de sua curva, enquanto que na ausência de um crash, possui o formato parecido com uma distribuição gaussiana. Com base neste comportamento, o presente trabalho propõe um novo indicador temporal IA(i), definido como a taxa de variação da área sob a curva de C(q). O indicador foi construído por intermédio de uma janela temporal de tamanho s que se movimenta ao longo da série, simulando a entrada de dados na série ao longo do tempo. A análise de IA(i) permite detectar com antecedência a ocorrência de grandes movimentos, como os famosos crashes de 1929 e 1987 para os índices Dow Jones, S&P500 e Nasdaq. Além disso, a análise simultânea de medidas como a Energia Livre, a Dimensão Multifractal e o Espectro Multifractal, sugerem que um crash de mercado se assemelha a uma transição de fase. A robustez do método para diferentes ativos e diferentes períodos de tempo, demonstra a importância dos resultados. Além disso, modelos estatísticos não lineares para a volatilidade foram empregados no trabalho para estudar grandes flutuações causadas por crashes e crises financeiras ao longo do tempo. / Financial time series such as market index and asset prices, are produced by complex interactions of agents that trade in the market. The fractal and multifractal properties of these series provides evidence for early detection of the occurrence of sudden market movements (crashes). This evidence is obtained by applying the concept of Analog Specific Heat C(q), from the equivalence between the Multifractal Analysis and Thermodynamics. In the vicinity of a crash, C(q) exhibits a shoulder at the right side of its curve, while in the absence of a crash, C(q) presents a form similar to a Gaussian distribution curve. Based on this behavior, it is proposed in this work a new temporal indicator IA(i) defined here as the area variation rate over the Specific Heat function. We have constructed the mentioned indicator from a window of data with the first points (size s), that moves throughout the series, simulating the actual input of data over time. The indicator IA(i) allows one detecting in advance the occurrence of large financial market movements, such as those occurred in 1929 and 1987 for the marked indexes Dow Jones, Nasdaq and S&P500. Moreover, the simultaneous analysis of measures such as the Free Energy, Multifractal Dimension and Multifractal Spectrum suggest that a market crash resembles a phase transition. The robustness of the method for others assets and different periods of time demonstrates the importance of the results. Moreover, nonlinear statistical models for volatility have been employed in the work to study large fluctuations caused by crashes and financial crises over time.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-01-20T21:22:09Z No. of bitstreams: 1 RobivalAlvesRibeiro_TESE.pdf: 2206825 bytes, checksum: 2cb9e24716fd72830d590b545945791d (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-01-22T20:33:51Z (GMT) No. of bitstreams: 1 RobivalAlvesRibeiro_TESE.pdf: 2206825 bytes, checksum: 2cb9e24716fd72830d590b545945791d (MD5) / Made available in DSpace on 2016-01-22T20:33:51Z (GMT). No. of bitstreams: 1 RobivalAlvesRibeiro_TESE.pdf: 2206825 bytes, checksum: 2cb9e24716fd72830d590b545945791d (MD5) Previous issue date: 2014-12-17 / Esta pesquisa tem como objetivo verificar se ? poss?vel construir padr?es espaciais em reservat?rios de petr?leo, usando expoentes de DFA (Detrended Fluctuation Analysis) dos diferentes perfis geol?gicos como: s?nico, densidade, porosidade, resistividade e raios gama. Fizeram parte da amostra 54 po?os de petr?leo do campo de Namorado, localizados na bacia de Campos, no estado do Rio de Janeiro, Brasil. Com o intuito de verificar a correla??o linear, constru?ram-se matrizes de dist?ncias entre os po?os e matrizes de diferen?as entre os DFA dos po?os, comparadas duas a duas e utilizado como m?todo estat?stico o teste de Mantel. A hip?tese nula consiste em afirmar que n?o existe correla??o linear entre as estruturas espaciais formadas pelas matrizes de dist?ncias euclidianas e das diferen?as dos expoentes de DFA dos perfis geol?gicos. Os perfis s?nicos (p=0,18) e da densidade (p=0,26) foram os que revelaram uma tend?ncia ? correla??o ou correla??o fraca. Estudo complementar, utilizando o contour plot, mostra os padr?es s?nicos e da densidade compat?veis com presen?a de correla??o espacial, corroborando os revelados pelo teste de Mantel / This research aims to set whether is possible to build spatial patterns over oil fields using DFA (Detrended Fluctuation Analysis) of the following well logs: sonic, density, porosity, resistivity and gamma ray. It was employed in the analysis a set of 54 well logs from the oil field of Campos dos Namorados, RJ, Brazil. To check for spatial correlation, it was employed the Mantel test between the matrix of geographic distance and the matrix of the difference of DFA exponents of the well logs. The null hypothesis assumes the absence of spatial structures that means no correlation between the matrix of Euclidean distance and the matrix of DFA differences. Our analysis indicate that the sonic (p=0.18) and the density (p=0.26) were the profiles that show tendency to correlation, or weak correlation. A complementary analysis using contour plot also has suggested that the sonic and the density are the most suitable with geophysical quantities for the construction of spatial structures corroborating the results of Mantel test

Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T11:52:38Z No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 13129069 bytes, checksum: b427ff42ec7918c3d0cf7f63798ed648 (MD5) / Made available in DSpace on 2016-07-07T11:52:38Z (GMT). No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 13129069 bytes, checksum: b427ff42ec7918c3d0cf7f63798ed648 (MD5) Previous issue date: 2014-09-19 / The objective of this study was to uncloak the dynamic of climate of Brazil, seeking to measure the regularity and the long range autocorrelation of daily climate series of temperature of air (average, maximum, minimum, and temperature range), relative humidity of air average and wind speed average. The data were obtained by Instituto Nacional de Meteorologia (INMET), at 264 meteorological stations, in the period from January 1990 to December 2012. We use the Detrended Fluctuation Analysis to realize the estimation of the Hurst exponent, the Multiscale Sample Entropy to estimating the entropy of series and the Kriging to interpolate the estimates made. We observed that higher latitudes tend to attenuate the mean of temperatures of air maximum, minimum and average, but increase the variability of the same. This inversion of the magnitudes of the mean and standard deviation is also observed in the relative humidity of air. The means of the estimated Hurst exponents estimated for Brazil were 0.81, 0.79, 0.81, 0.77, 0.83 and 0.64, and the estimated Sample Entropy, 1.39, 1.78, 1.46, 1.41, 1.56 and 1.66, respectively for average, maximum and minimum temperatures of air, temperature range, relative humidity of air average and wind speed average. The values of the estimated Hurst exponents showed a positive correlation with latitude in the temperature variables studied. Such a correlation was not observed in other variables. This a correlation was not observed in other variables. The regularities of climate series in Brazil were medians. Spatially, the greatest changes occurred in estimates of entropies in the scale 1 to 2 of , in the Multiscale Sample Entropy. As from ≥2 the changes observed were more subtle. We observe the influence of the Equatorial Continental air mass in entropy of temperatures daily average and maximum of air. The climatic factor of altitude influenced with more frequently in the observed results, mainly on temperature variables. In some cases, the continentality and the air masses were also identified as important factors in characterizing the spatial distribution of estimates made. / O objetivo deste estudo foi desvendar a dinâmica climática do Brasil, buscando mensurar a regularidade e a autocorrelação de longo alcance em séries climáticas diárias de temperatura do ar (média, máxima, mínima, e amplitude térmica), umidade relativa média do ar e velocidade média diária do vento. Os dados foram obtidos pelo Instituto Nacional de Meteorologia, em 264 estações meteorológicas, no período de janeiro de 1990 a dezembro de 2012. Utilizamos o Detrended Fluctuation Analysis para realizar a estimativa do expoente de Hurst, o Multiscale Sample Entropy para as estimativas da entropia das séries e o Kriging para a interpolação das estimativas realizadas. Observamos que maiores latitudes tendem a atenuar as médias das temperaturas máxima, mínima e média do ar, porém aumentam a variabilidade das mesmas. Esta inversão entre as magnitudes da média e do desvio padrão também é observado na umidade relativa média do ar. As médias dos expoentes de Hurst estimados para todo o Brasil foram 0,81; 0,79; 0,81; 0,77; 0,83 e 0,64; e do Sample Entropy estimado, 1,39; 1,78; 1,46; 1,41; 1,56 e 1,66, respectivamente para séries diárias de temperatura média, máxima e mínima do ar, amplitude térmica do ar, umidade relativa média do ar e velocidade média do vento. Os valores do expoentes de Hurst estimados apresentaram uma correlação positiva com a latitude nas variáveis de temperatura do ar estudadas. Tal correlação não foi observada nas demais variáveis. As regularidades das séries climáticas no Brasil foram medianas. Espacialmente, as maiores alterações nas estimativas das entropias ocorreram na escala 1 para a 2 de , no Multiscale Sample Entropy. A partir de ≥2 as mudanças observadas foram mais sutis. Observamos influência da massa de ar Equatorial Continental na entropia das temperaturas do ar média e máxima diárias. O fator climático da altitude atuou com maior frequência sob os resultados observados, principalmente nas variáveis de temperatura. Em alguns casos, a continentalidade e as massas de ar também foram apontados como fatores importantes na caracterização da distribuição espacial das estimativas realizadas.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-04T14:28:53Z No. of bitstreams: 1 Maira de Oliveira Santos.pdf: 1516572 bytes, checksum: ea3508c7d99ef42591a6b17f459901e0 (MD5) / Made available in DSpace on 2016-08-04T14:28:53Z (GMT). No. of bitstreams: 1 Maira de Oliveira Santos.pdf: 1516572 bytes, checksum: ea3508c7d99ef42591a6b17f459901e0 (MD5) Previous issue date: 2010-06-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The study of climate has great economic end environmental importance, given that a single large and unexpected variation of a climatic element may devastate plantations or cities, and thus affect the economy of a region and life of the inhabitants. Climate can be influenced by diverse factors, such as latitude, altitude, air mass, proximity to sea, sea currents, terrain topology, vegetation, etc. The most important climate elements are temperature, humidity, atmospheric pressure, solar radiation, precipitation and wind. The wind is generated by atmospheric air mass movement, and may influence various phenomena such as soil erosion, pollutant dispersal, transport of pollen and seeds, propagation of diseases, as well as generation of eolic energy. Surface wind velocity is natural example of the phenomenon of turbulence, which represents a stochastic process characterized by temporal and spatial scale invariance. In this work we study long range correlations in temporal series of wind speed and direction registered at four meteorological stations in the cities of Arcoverde, Cabrobro, Garanhuns and Petrolina, in the state of Pernambuco, Brazil. To this end we apply Detrended Fluctuation Analysis (DFA) which was developed for quantification of long range correlations in non-stationary temporal series. We analyze the original wind speed series together with volatility (absolute value of increments) of the wind direction. All the analyzed series exhibit persistent long range correlations with the scale exponent above 0.5. In all cases the exponent values were found to be lower for wind direction then those for wind speed, indicating weaker persistence. No correlation was detected between the exponent values and the geographic parameters: longitutde, latitude and altitude of the station. The results of these analyses contribute to a better understanding of the nature of stochastic processes governing wind dynamics, necessary for development of more realistic theoretical and computational models as a base for modeling diverse phenomena influenced by climatic conditions. / O estudo do clima e dos seus elementos tem grande importância econômica e ambiental, visto que uma grande e inesperada variação em ao menos um dos elementos do clima pode devastar plantações, cidades, e assim mudar a economia de uma região e a vida das pessoas que ali habitam. O clima pode ser influenciado por diversos fatores, tais como latitude, altitude, massas do ar, continentalidade, maritmidade, correntes marítimas, relevo, vegetação, etc. Os elementos mais importantes do clima são temperatura, umidade, pressão atmosférica, radiação solar, precipitação e vento. O vento é gerado pelo movimento de massas do ar na atmosfera e pode influenciar vários fenômenos, como erosão do solo, dispersão de poluentes, transporte de pólen e sementes, propagação de doenças e geração da energia eólica. A velocidade do vento na superfície é um exemplo natural do fenômeno de turbulência, que representa um processo estocástico caracterizado pela invariância de escala temporal e espacial. Neste trabalho foram estudadas as correlações de longo alcance em séries temporais da velocidade e direção do vento registradas em quatro estações meteorológicas, nas cidades Arcoverde, Cabrobó, Garanhuns e Petrolina em Pernambuco. Foi utilizado o método Detrended fluctuation analysis (DFA), desenvolvido para quantificar as correlações de longo alcance em séries temporais não estacionárias. Foram analisadas as séries originais da velocidade do vento e as séries dos valores absolutos dos incrementos (volatilidade) da direção do vento. Todas as séries analisadas possuem as correlações de longo alcance persistentes, com expoente de escala acima de 0,5. Em todos os casos os valores dos expoentes são menores para a direção do que para a velocidade do vento, indicando que a persistência é mais fraca para a direção do vento. Não foi detectada a correlação entre os valores dos expoentes de escala e os parâmetros geográficos: longitude, latitude e altitude da estação. Os resultados destas análises vão ajudar a entender melhor a natureza dos processos estocásticos geradores da dinâmica do vento. Este entendimento é necessário para desenvolvimento dos modelos teóricos e computacionais mais precisos cujos resultados servirão como base para modelagem dos vários fenômenos influenciados pelas condições climáticas.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-12T14:38:54Z No. of bitstreams: 1 Rosilda Benecio de Souza.pdf: 1979743 bytes, checksum: 6e2df03119bd441a84297bc73d2befeb (MD5) / Made available in DSpace on 2016-08-12T14:38:54Z (GMT). No. of bitstreams: 1 Rosilda Benecio de Souza.pdf: 1979743 bytes, checksum: 6e2df03119bd441a84297bc73d2befeb (MD5) Previous issue date: 2011-02-17 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Vegetation and forest fires affect millions of hectares of Brazilian land and have severe ecological, social and economic consequences, including emissions of green house gases, loss of biodiversity, soil erosion etc. To establish efficient methods for prevention and suppression of fires, which is crucial for preservation of environment, it is necessarily to know the spatial location and time of occurrence of fires, burned area, why they occur, and how they initiate and propagate. Several satellite systems are currently available for monitoring different fire characteristics: dry areas that are susceptible to fire, actively flaming fires, burned area and smoke, and trace gas emissions. Hot pixels are satellite image pixels with infrared intensity corresponding to burning vegetation. Depending of image resolution, a hot pixel may represent one fire, or a part of a larger fire. Together with other satellite data, the number of hot pixels can be used to estimate the burned area and predict environmental and economics consequences. In this work we study the dynamics of hot pixels detected in Brazil by satellite NOAA-12 during the period 1998-2007, using the method Multifractal Detrended Fluctuation Analysis, which serves to detect and quantify multifractal properties of non-stationary temporal series. We calculate the generalized Hurst exponent h(q), Renyi exponent (q) and singularity spectrum f( ). The results show the existence of power-law long-term correlations that are described by a hierarchy of scaling exponents, which is the consequence of an underlying multifractal stochastic process. Based on this empirical result we also show that the Multifractal Cascade Model can be used to produce synthetic data for hot pixels dynamics. The observed multifractal property of temporal series of hot pixels should be incorporated in theoretical models and computer simulations of the fire dynamics and related phenomena. / Queimadas e incêndios florestais atingem milhões de hectares de terras brasileiras, causando graves consequências ecológicas, sociais e econômicas, incluindo emissões de gases do efeito estufa, perda de biodiversidade, erosão do solo, etc. Para estabelecer métodos eficientes para prevenção e supressão do fogo, importantes para proteção do meio ambiente, é necessário conhecer onde, quando e porquê os incêndios ocorrem, a área queimada e como se iniciam e se propagam. Atualmente, vários satélites são disponibilizados para monitoramento das características do fogo: áreas de risco, incêndios atualmente ativos, área queimada, fumaça e emissão de gases. Focos de calor são pixels na imagem de satélite, com intensidade infravermelha correspondente a vegetação queimada. Dependendo da resolução, um foco pode representar uma queimada ou parte de um incêndio maior. O número de focos combinado a outras informações fornecidas pelos satélites pode ser usado para estimar a área queimada, e prever as consequências ecológicas e econômicas. Neste trabalho, foi estudada a dinâmica de focos de calor detectados no Brasil pelo satélite NOAA-12, durante o período 1998-2007, utilizando o método Multifractal Detrended Fluctuation Analysis, desenvolvido para detecção e quantificação das propriedades mutifractais das séries temporais não estacionárias. Foram calculados o expoente generalizado de Hurst h(q), o expoente Renyi (q) e o espectro de singularidade f( ). Os resultados mostraram a existência de correlações de longo alcance, caracterizadas por uma hierarquia dos expoentes de escala, conseqüência de um processo estocástico multifractal. Baseado nos resultados empíricos, também foram mostrados que o Multifractal Cascade Model pode ser usado para gerar séries artificiais dos focos de calor. A propriedade multifractal da dinâmica dos focos de calor poderá ser incorporada em modelos teóricos e simulações computacionais de dinâmica de incêndios e fenômenos relacionados.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-12T15:41:19Z No. of bitstreams: 1 Silvio Fernando Alves Xavier Junior.pdf: 1969278 bytes, checksum: 716d98cbb2937cd01be0ff1272ed5033 (MD5) / Made available in DSpace on 2016-08-12T15:41:19Z (GMT). No. of bitstreams: 1 Silvio Fernando Alves Xavier Junior.pdf: 1969278 bytes, checksum: 716d98cbb2937cd01be0ff1272ed5033 (MD5) Previous issue date: 2011-06-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Rainfall can be understood as an end product of a number of complex atmospheric processes, which vary in space and time, and it may be considered one of most important dominant factor of the meteorological-climatic features of an specified investigated area. In this study, we observed if the dynamics of rain in Piracicaba, São Paulo - Brazil is generated by a multifractal process and / or belongs to classes of Self-Organized Criticality systems. To detect long-term correlations and multifractal behavior, we apply MF-DFA method that systematically detect nonstationarities and overcome trends in the data at all timescales. We calculated the generalized Hurst exponent, h(q), and Renyi exponent, (q). The results showed the existence of power-law long-term correlations which are described by a hierarchy of scaling exponents, that is the consequence of an underlying multifractal stochastic process. For smaller scales of about 8 months, the dynamics of rain is generated by a multifractal process (the generalized Hurst exponent, h(q), decreases with the increase in order (q) meaning it can be modeled using the cascade models. For larger scales, the value of h(q) is between 0:35 �� 0:55 indicating a weaker multifractality. The hypothesis that rainfall may be a case of Self-Organized Criticality is assessed. We analyze two events: the daily amount of rain and drought events (days without rain), both are weather phenomena that are strongly linked to rainfall. It appears that the distribution of the daily amount of rain displays two different scaling regimes for small and large intensities. The value of the ratio of these exponents confirms the results that were obtained in regions with tropical and subtropical climates. However, for the distribution of drought events we find two distinct scaling exponents with values that are closer than those observed in the daily amount of rain. The multifractal properties and self-organized criticality should be incorporated into theoretical models and computer simulations of the dynamics of rainfall and related phenomena.Rainfall can be understood as an end product of a number of complex atmospheric processes, which vary in space and time, and it may be considered one of most important dominant factor of the meteorological-climatic features of an specified investigated area. In this study, we observed if the dynamics of rain in Piracicaba, São Paulo - Brazil is generated by a multifractal process and / or belongs to classes of Self-Organized Criticality systems. To detect long-term correlations and multifractal behavior, we apply MF-DFA method that systematically detect nonstationarities and overcome trends in the data at all timescales. We calculated the generalized Hurst exponent, h(q), and Renyi exponent, (q). The results showed the existence of power-law long-term correlations which are described by a hierarchy of scaling exponents, that is the consequence of an underlying multifractal stochastic process. For smaller scales of about 8 months, the dynamics of rain is generated by a multifractal process (the generalized Hurst exponent, h(q), decreases with the increase in order (q) meaning it can be modeled using the cascade models. For larger scales, the value of h(q) is between 0:35 �� 0:55 indicating a weaker multifractality. The hypothesis that rainfall may be a case of Self-Organized Criticality is assessed. We analyze two events: the daily amount of rain and drought events (days without rain), both are weather phenomena that are strongly linked to rainfall. It appears that the distribution of the daily amount of rain displays two different scaling regimes for small and large intensities. The value of the ratio of these exponents confirms the results that were obtained in regions with tropical and subtropical climates. However, for the distribution of drought events we find two distinct scaling exponents with values that are closer than those observed in the daily amount of rain. The multifractal properties and self-organized criticality should be incorporated into theoretical models and computer simulations of the dynamics of rainfall and related phenomena. / A precipitação pode ser entendida como um produto final de processos atmosféricos complexos, os quais variam no tempo e espaço, e pode ser considerada um dos mais importantes fatores dominante das características meteorológicas-climáticas de uma determinada área investigada. Neste trabalho, verificamos se a dinâmica da chuva em Piracicaba, São Paulo - Brasil é gerada por um processo multifractal e/ou pertence as classes dos sistemas com propriedade da criticalidade auto-organizada. Para detectar a correlação de longo alcance e o comportamento multifractal, aplicamos o método MF-DFA que sistematicamente detecta não-estacionariedades e tendências nos dados para todas escalas de tempo. Calculamos o expoente generalizado de Hurst, h(q), e o expoente de Renyi, (q). Os resultados mostraram a existência de correlações de longo alcance, caracterizadas por uma hierarquia dos expoentes de escala, consequência de um processo estocástico multifractal. Para as escalas menores, aproximadamente 8 meses, a dinâmica de chuva é gerada por um processo multifractal (o expoente de Hurst generalizado, h(q), diminui com o aumento de ordem q) significando que pode ser modelada utilizando os modelos de cascata. Para as escalas maiores, o valor de h(q) está entre 0,35-0,55 o que indica a multifractalidade mais fraca. A hipótese de que a precipitação pode ser um caso de Self- Organized Criticality é avaliada. Analisamos dois eventos: a quantidade diária de chuva e eventos de seca (dias sem chuva), ambos são fenômenos metereológicos os quais são fortemente ligados à precipitação. Verifica-se que a distribuição da quantidade diária de chuva exibe dois regimes de escala distintos para pequenas e grandes quantidades. O valor da razão desses expoentes encontrados confirmam os resultados que foram obtidos nas regiões com climas tropical e subtropical. No entanto, para a distribuição de eventos de seca encontramos dois expoentes de escala distintos com valores bem mais próximos comparados com os observados na quantidade diária de chuva. As propriedades multifractais e criticalidade auto-organizada deverão ser incorporados em modelos teóricos e simulações computacionais da dinâmica das chuvas e fenômenos relacionados.