Cavitation can be defined as the breakdown of a liquid (either static or in motion) medium under very low pressure. The hydrodynamic happened in high-speed flow, where local pressure in liquid falls under the saturating pressure thus the liquid vaporizes to form the cavity. During the evolution and collapsing of cavitation bubbles, extreme physical conditions like high-temperature, high-pressure, shock-wave, and high-speed micro-jets can be generated. Such a phenomenon shall be prevented in hydraulic or astronautical machinery due to the induced erosion and noise, while it can be utilized to intensify some treatment processes of chemical, food, and pharmaceutical industries, to shorten sterilization times and lower energy consumption. Advances in the understanding of the physical processes of cavitating flows are challenging, mainly due to the lack of quantitative experimental data on the two-phase structures and dynamics inside the opaque cavitation areas. This dissertation is aimed at finding out the physical mechanisms governing the cavitation instabilities and making contributions in controlling hydraulic cavitation for engineering applications. In this thesis, cavitation developed in various convergent-divergent (Venturi) channels was studied experimentally using the ultra-fast synchrotron X-ray imaging, LIF Particle Image Velocimetry, and high-speed photography techniques, to (1) investigate the internal structures and evolution of bubble dynamics in cavitating flows, with velocity information obtained for two phases; (2) measure the slip velocity between the liquid and the vapor to provide the validation data for the numerical cavitation models; (3) consider the thermodynamic effects of cavitation to establish the relation between the cavitation extent and the fluid temperature, then and optimize the cavitation working condition in water; (4) seek the coherent structures of the complicated high-turbulent cavitating flow to reduce its randomness using data-driven methods. / Doctor of Philosophy / When the pressure of a liquid is below its saturation pressure, the liquid will be vaporized into vapor bubbles which can be called cavitation. In many hydraulic machines like pumps, propulsion systems, internal combustion engines, and rocket engines, this phenomenon is quite common and could induce damages to the mechanical systems. To understand the mechanisms and further control cavitation, investigation of the bubble inception, deformation, collapse, and flow regime change is mandatory. Here, we performed the fluid mechanics experiment to study the unsteady cavitating flow underlying physics as it occurs past the throat of a Venturi nozzle. Due to the opaqueness of this two-phase flow, an X-ray imaging technique is applied to visualize the internal flow structures in micrometer scales with minor beam scattering. Finally, we provided the latest physical model to explain the different regimes that appear in cavitation. The relationship between the cavitation length and its shedding regimes, and the dominant mechanism governing the transition of regimes are described. A combined suppression parameter is developed and can be used to enhance or suppress the cavitation intensity considering the influence of temperature.

Most thin-walled metallic structural members experience some extent of interactive buckling that corrodes the load carrying capacity. Current design methods predict the strength of thin-walled metallic structural members based on individual buckling limit-states and limited case of interactive buckling limit state. In order to develop design methods for most coupled buckling limit states, the interaction of buckling modes needs to be studied. This dissertation first introduces a generally applicable methodology for Generalized Beam Theory (GBT) elastic buckling analysis on members with holes, where the buckling modes of gross cross-section interact with those of net cross-section. The approach treats member with holes as a structural system consisting of prismatic sub-members. These sub-members are connected by enforcing nodal compatibility conditions for the GBT discretization points at the interfaces. To represent the shear lag effect and nonlinear normal stress distribution in the vicinity of a hole, GBT shear modes with nonlinear warping are included. Modifications are made to the GBT geometric stiffness because of the influence from shear lag effect caused by holes. In the following sections, the GBT formulation for a prismatic bar is reviewed and the GBT formulation for members with holes is introduced. Special aspects of analyzing members with holes are defined, namely the compatibility conditions to connect sub-members and the geometric stiffness for members with holes. Validation and three examples are provided. The second topic of this dissertation involves a buckling mode decomposition method of normalized displacement field, bending stresses and strain energy for thin-walled member displacement field (point clouds or finite element results) based on generalized beam theory (GBT). The method provides quantitative modal participation information regarding eigen-buckling displacement fields, stress components and elastic strain energy, that can be used to inform future design approaches. In the method, GBT modal amplitudes are retrieved at discrete cross-sections, and the modal amplitude field is reconstructed assuming it can be piece-wisely approximated by polynomials. The unit displacement field, stress components and strain energy are all retrieved by using reconstructed GBT modal amplitude field and GBT constitutive laws. Theory and examples are provided, and potential applications are discussed including cold-formed steel member design and post-disaster evaluation of thin-walled structural members. In the third part, post-buckling modal decomposition is made possible by development of a geometrically nonlinear GBT software. This tool can be used to assist understanding couple-buckling limit-states. Lastly, the load-deformation response considering any one GBT mode is derived analytically for fast computation and interpretation of structural post-buckling behavior. / Ph. D. / Here I present novel analytical methods to quantitatively decompose interactive buckling in the thin-walled structures. Interactive buckling, where multiple buckling modes are present to initiate structure failure, often controls the load-carrying capacity of thin-walled structures, e.g., the amount of load a column can withstand or the maximum acceleration a space shuttle can experience. In this research, based on Generalized Beam Theory, I describe in detail the analytical methods revealing how buckling modes are coupled and contribute to key quantities related to the structural failure, namely, displacement, stress, and strain energy. I obtain the algorithms by performing rigorous mathematical derivations based on thin-walled mechanics. The research not only facilitates better building design according to the simplified method in the current design standard, but also enables advanced, nonlinear modal decomposition analysis using the custom-made Finite Element program. These studies aim to provide the quantitative understanding of the coupled buckling mechanism and further the development of more powerful strength prediction methods for thin-walled structures.

Em escalas interanuais a advecção de calor, sal e nutrientes pode afetar a produtividade primária. Pode-se citar a Circulação de Revolvimento Meridional (MOC) no impacto da concentração de clorofila. Neste estudo, a partir do método Multidimensional Empirical Mode Decomposition foram obtidos tendências decadais de PAR, nitrato integrado na coluna d\'água, concentração de clorofila e fluxo de volume da MOC. Em variações de baixa frequência há uma relação entre o transporte de volume da MOC com o nitrato integrado na coluna d\'água no Atlântico Sul, e consequentemente, na concentração de clorofila. Dois possíveis cenários foram identificados sobre o efeito da MOC no nitrato integrado e na concentração de clorofila. O cenário 1 apresenta uma relação direta no sistema MOC-nitrato integrado-concentração de clorofila, enquanto o efeito do cenário 2 é indireto, com a MOC afetando outras variáveis que perturbam o sistema. Neste estudo também foi analisado a influência de teleconexões atmosféricas na MOC do Atlântico Sul. Foi achado indícios da influência da Oscilação Antártica na MOC do Atlântico Sul em escalas decadais. Conclui-se que variações de baixa frequência no fluxo de volume da MOC alteram o padrão espaço-temporal da concentração de clorofila no Atlântico Sul. / On interannual timescales the advection of heat, salt and nutrients can affect the primary production. The influence of the Meridional Overturning Circulation (MOC) over the chlorophyll concentration can be mentioned. In this study, decadal trends of PAR, integrated nitrate over the water column, chlorophyll and volume flux of the MOC were obtained using the Multidimensional Empirical Mode Decomposition method. On low frequency variations there is a relationship between the MOC volume transport with integrated nitrate over the water column in the South Atlantic, and therefore, on chlorophyll concentration. Two scenarios were identified as possible mechanisms of influence of the MOC over integrated nitrate and chlorophyll concentrations. Scenario 1 presents a direct relationship on the system MOC-integrated nitrate-chlorophyll concentration, while scenario 2 has an indirect effect, with the MOC affecting other variables that disturb the system. In this study the influence of atmospheric teleconnections on the South Atlantic MOC were also investigated. It was found evidence that the Antarctic Oscillation can affect the South Atlantic MOC on decadal timescales. It was concluded that low frequency variability on the volume flux of the MOC can alter the spatiotemporal pattern of the chlorophyll concentration on the South Atlantic.

No description available.

More than one million fetal deaths occur in the United States every year [1]. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate. All the existing technologies have either complex instrumentation or need a trained professional at all times or both. The existing technologies are proven to be impractical for continuous monitoring [2]. Hence, there is an increased interest towards noninvasive, continuous monitoring, and less expensive technologies like fetal phonocardiography. Fetal Phonocardiography (FPCG) signal is obtained by placing an acoustic transducer on the abdomen of the mother. FPCG is rich in physiological bio-signals and can continuously monitor the fetal heart rate non-invasively. Despite its high diagnostic potential, it is still not being used as the secondary point of care. There are two challenges as to why it is still being considered as the secondary point of care; in the data acquisition system and the signal processing methodologies. The challenges pertaining to data acquisition systems are but not limited to sensor placement, maternal obesity and multiple heart rates. While, the challenges in the signal processing methodologies are dynamic nature of FPCG signal, multiple known and unknown signal components and SNR of the signal. Hence, to improve the FPCG based care, challenges in FPCG signal processing methodologies have been addressed in this study. A comparative evaluation was presented on various advanced signal processing techniques to extract the bio-signals with fidelity. Advanced signal processing approaches, namely empirical mode decomposition, spectral subtraction, wavelet decomposition and adaptive filtering were used to extract the vital bio-signals. However, extracting these bio-signals with fidelity is a challenging task in the context of FPCG as all the bio signals and the unwanted artifacts overlap in both time and frequency. Additionally, the signal is corrupted by noise induced from the fetal and maternal movements as well the background and the sensor. Empirical mode decomposition algorithm was efficient to denoise and extract the maternal and fetal heart sounds in a single step. Whereas, spectral subtraction was used to denoise the signal which was later subjected to wavelet decomposition to extract the signal of interest. On the other hand, adaptive filtering was used to estimate the fetal heart sound from a noisy FPCG where maternal heart sound was the reference input. The extracted signals were validated by obtaining the frequency ranges computed by the Short Time Fourier Transform (STFT). It was observed that the bandwidths of extracted fetal heart sounds and maternal heart sounds were consistent with the existing gold standards. Furthermore, as a means of additional validation, the heart rates were calculated. Finally, the results obtained from all these methods were compared and contrasted qualitatively and quantitatively.

In this work the methods of mode decomposition and Fourier analysis of quantum fields on curved spacetimes previously available mainly for the scalar fields on Friedman-Robertson-Walker spacetimes are extended to arbitrary vector fields on general spatially homogeneous spacetimes. This is done by developing a rigorous unified framework which incorporates mode decomposition, harmonic analysis and Fourier analysis. Explicit constructions are performed for a variety of situations arising in homogeneous cosmology. A number of results concerning classical and quantum fields known for very restricted situations are generalized to cover almost all cosmological models.

Στα πλαίσια της διπλωματικής αυτής εργασίας στόχος μας είναι η εφαρμογή τεχνικών ψηφιακής επεξεργασίας σήματος σε γεωφυσικά σήματα και συγκεκριμένα κατά κύριο λόγο σε σήματα γεωηλεκτρικού δυναμικού μακράς διάρκειας και σε σήματα σεισμικών κυμάτων. Έχουμε εστιάσει την επεξεργασία μας στην διερεύνηση της εφαρμογής δυο τεχνικών επεξεργασίας ξεχωριστά και σε συνδυασμό. Η πρώτη αφορά την εκτίμηση της στιγμιαίας συχνότητας ενός σήματος μέσω του τελεστή Teager-Kaiser [Maragos_1991] στοχεύοντας σε μια εναλλακτική στα προβλήματα συμβιβασμού της διακριτικής ικανότητας, στην υπολογιστική πολυπλοκότητα, στην ανίχνευση αρνητικών συχνοτήτων και στην εμφάνιση cross-terms άλλων κλασικών μεθόδων. Η δεύτερη που είναι η Empirical Mode Decomposition (EMD) [Huang_1998] αφορά την ανάλυση των δεδομένων σε συνιστώσες AM-FM μορφής καθοδηγούμενη από τα δεδομένα, για την ανάδειξη χαρακτηριστικών δεδομένων μη-στάσιμων και μη-γραμμικών διαδικασιών. Ο συνδυασμός των μεθόδων είναι η εύρεση του συχνοτικού περιεχομένου ενός σήματος με την χρήση του τελεστή Teager- Kaiser από τις επιμέρους συνιστώσες που έχουν προκύψει με την μέθοδο (EMD) και ονομάζεται μετασχηματισμός Teager-Huang και αποτελεί μια εναλλακτική δυνατότητα στην εύρεση του φάσματος ενός σήματος. Στα επόμενα κεφάλαια περιγράφουμε τα υπό εξέταση σήματα, τους μηχανισμούς καταγραφής τους, μια ανασκόπηση των βασικών επεξεργασιών που έχουν υποστεί και την ανάλυση στην οποία υποβλήθηκαν στα πλαίσια της συγκεκριμένης εργασίας. Έτσι στο κεφάλαιο 2 παρουσιάζουμε τα υπό ανάλυση σήματα, αναφέροντας τις διαδικασίες με τις οποίες προκύπτουν και τις διατάξεις μέτρησής τους. Στο κεφάλαιο 3 κάνουμε μια ανασκόπηση των τεχνικών επεξεργασίας των σημάτων γεωηλεκτρικού δυναμικού μέχρι σήμερα. Στα κεφάλαια 4, 5 και 6 αναφέρουμε το θεωρητικό υπόβαθρο, την πειραματική διαδικασία επεξεργασίας που ακολουθήθηκε και τα συμπεράσματα με την εφαρμογή του τελεστή Teager, της μεθόδου EMD και του μετασχηματισμού Teager-Huang αντίστοιχα που αποτελούν πρωτότυπη εφαρμογή στον χώρο των γεωφυσικών σημάτων. Στο κεφάλαιο 7 αναφερόμαστε σε επεξεργασία που έχει γίνει υποβοηθητικά ή συμπληρωματικά της επεξεργασίας των προηγούμενων κεφαλαίων. Για τα πειράματα των κεφαλαίων 4-7, αναφέρουμε το όνομα του script αρχείου που τα υλοποιεί εντός παρενθέσεων δίπλα στο όνομα του πειράματος. Ο σχετικός κώδικας μπορεί να βρεθεί στο συνοδευτικό CD της διπλωματικής εργασίας. Στο κεφάλαιο 8 καταλήγουμε με τα κύρια συμπεράσματα και ορισμένες κατευθύνσεις περαιτέρω επεξεργασίας ως επέκταση της τρέχουσας διπλωματικής. / This work concerns the study and implementation of the Teager-Kaiser Operator, the Empirical Mode Decomposition method, and the combination of them known as Teager-Huang transformation.

Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-26T22:26:10Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) / Made available in DSpace on 2018-06-26T22:26:10Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) Previous issue date: 2017-02-21 / CAPES / The useful life time of equipment is an important variable related to reliability and maintenance. The knowledge about the useful remaining life of operation system by means of a prognostic and health monitoring could lead to competitive advantage to the corporations. There are numbers of models trying to predict the reliability’s variable behavior, such as the remaining useful life, from different types of signal (e.g. vibration signal), however several could not be realistic due to the imposed simplifications. An alternative to those models are the learning methods, used when exist many observations about the variable. A well-known method is Support Vector Machine (SVM), with the advantage that is not necessary previous knowledge about neither the function’s behavior nor the relation between input and output. In order to achieve the best SVM’s parameters, a Particle Swarm Optimization (PSO) algorithm is coupled to enhance the solution. Empirical Mode Decomposition (EMD) and Wavelets rise as two preprocessing methods seeking to improve the input data analysis. In this paper, EMD and wavelets are used coupled with PSO+SVM to predict the rolling bearing Remaining Useful Life (RUL) from a vibration signal and compare with the prediction without any preprocessing technique. As conclusion, EMD models presented accurate predictions and outperformed the other models tested. / O tempo de vida útil de um equipamento é uma importante variável relacionada à confiabilidade e à manutenção, e o conhecimento sobre o tempo útil remanescente de um sistema em operação, por meio de um monitoramento do prognóstico de saúde, pode gerar vantagens competitivas para as corporações. Existem diversos modelos utilizados na tentativa de prever o comportamento de variáveis de confiabilidade, tal como a vida útil remanescente, a partir de diferentes tipos de sinais (e.g. sinal de vibração), porém alguns podem não ser realistas, devido às simplificações impostas. Uma alternativa a esses modelos são os métodos de aprendizado, utilizados quando se dispõe de diversas observações da variável. Um conhecido método de aprendizado supervisionado é o Support Vector Machine (SVM), que gera um mapeamento de funções de entrada-saída a partir de um conjunto de treinamento. Para encontrar os melhores parâmetros do SVM, o algoritmo de Particle Swarm Optimization (PSO) é acoplado para melhorar a solução. Empirical Mode Decomposition (EMD) e Wavelets são usados como métodos pré-processamento que buscam melhorar a qualidade dos dados de entrada para PSO+SVM. Neste trabalho, EMD e Wavelets foram usadas juntamente com PSO+SVM para estimar o tempo de vida útil remanescente de rolamentos a partir de sinais de vibração. Os resultados obtidos com e sem as técnicas de pré-processamento foram comparados. Ao final, é mostrado que modelos baseados em EMD apresentaram boa acurácia e superaram o desempenho dos outros modelos testados.

I applied empirical mode decomposition (EMD) and the Hilbert-Herbert transforms, as tools to analyze streamflow data. I used the EMD method to extract and analyze periodic processes and trends in several environmental datasets including daily stream flow, daily precipitation, and daily temperature on data from the watersheds of two rivers in the Upper Colorado River Basin, the Yampa and the Upper-Green rivers. I used these data to identify forcing functions governing streamflow. Forcing functions include environmental factors such as temperature and precipitation and anthropogenic factors such as dams or diversions. The Green and Yampa Rivers have similar headwaters, but the Yampa has minimal diversions or controls while Flaming George Dam on the Green river significantly affects flow. This provides two different flow regimes with similar large watersheds. In addition to flow data, I analyzed several time series data sets, including temperature and precipitation from Northeast Utah, North Western Colorado, and Southern Wyoming. These data are from the area that defines the Yampa River and Green River watersheds, which stretch from Flaming Gorge Dam to Ouray Colorado. The EMD method is a relatively new technique that allows any time series data set, including non-linear and non-stationary datasets that are common in earth observation data, to be decomposed into a small quantity of composite finite data series, called intrinsic mode functions (IMFs). The EMD method can decompose any complicated data into several IMFs that represent independent signals in the original data. These IMFs may represent periodic forcing functions, such as environmental conditions or dam operations, or they may be artifacts of the decomposition method and not have an associated physical meaning. This study attempts to assign physical meaning to some IMFs resulting from the decomposition of the Green and Yampa flows where possible. To assign physical meaning to the IMFs, I analyzed frequencies of each IMF using the Hilbert-Hung transform, part of the Empirical Mode Decomposition method, and then compared frequencies of the IMFs with the known frequencies of physical processes. I performed these calculations on both flow, temperature, and precipitation. I found significant correlation between IMF components of flow, precipitation, and temperature data with El Niño Southern Oscillation (ENSO) events. The EMD process also extracts the long-term trend in non-linear data sets that can provide insights into the effects of climate change on the flow system. Though in preliminary stages of research, these analysis methods may lead to further understanding the availability of water within the upper Yampa and Green River Watersheds.

The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.