A brief review of three-dimensional turbulent boundary layer mean velocity profile models was presented, with emphasis on the applicability of these to predict cross flow profiles when skewing existed in any single profile. A recurving or s-shaped rectangular channel was used to experimentally investigate the possible existence of such a turbulent boundary layer flow. The time average velocity profiles along the centerline of this channel were obtained with a hot film anemometer. The resultant profiles indicated that a turbulent boundary layer can exist with cross flow in two lateral directions simultaneously in the same profile and this phenomenon can occur over a relatively long flow distance. Several attempts were made to fit the models of Eichelbrenner and Shanebrook to the measured cross flow profiles, but with only limited success. A test of the three-dimensional wall-wake formulation proposed by Coles was made for each profile. A shear velocity was inferred by a modification of the two-dimensional Ludwieg and Tillman skin friction equation, and by a modified form of the two-dimensional Clauser skin friction chart. A linear semi-logarithmic region was judged distinguishable for profiles with skewing in one lateral direction and with the limiting wall streamline angle less than approximately 30 degrees. Additionally in some instances a linear semi-logarithmic region was judged to exist when when simultaneous lateral skewing occurred in two directions. Generally, the constructed wake profiles did not resemble the universal form tentatively proposed by Coles, but rather resembled the characteristic preasymptotic form as discussed by Pierce. / Master of Science

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Subclinical atherosclerosis is an important area of research to evaluate stroke risk and predict localization of plaque. The current methods for detecting atherosclerosis risk are insufficient because it is based on The Framingham Risk Score and carotid intima media thickness, therefore an engineering detection model based on quantifiable data is needed. Laminar and turbulent flow, dictated by Reynolds number and relative roughness, was modeled through the carotid artery bifurcation to compare shear stress and shear rate. Computer-aided design and fluid flow software were used to model hemodynamics through the carotid artery. Data from the model was derived from governing equations programmed in COMSOL for both laminar and turbulent flow. A carotid artery model is accurate enough to describe how relative roughness, flow profiles, and shear rate can be a good prediction of subclinical atherosclerosis.

Introdução: A obstrução nasal é um sintoma presente em várias doenças nasais. Este projeto propõe desenvolver uma metodologia para o cálculo da resistência nasal ao fluxo aerífero por meio de fluidodinâmica computacional e comparar os resultados dessa técnica com os da rinomanometria. Métodos: a resistência nasal ao fluxo aerífero foi medida por rinomanometria, experimentalmente e por fluidodinâmica computacional. A influência da segmentação da tomografia computadorizada nas variáveis de fluidodinâmica computacional foi investigada. O modelo computacional de escoamento laminar foi comparado ao modelo de turbulência k-w padrão. Foram analisadas a acurácia, correlação e concordância entre a resistência nasal calculada por fluidodinâmica computacional com aquela obtida por experimento e rinomanometria. Resultados: A resistência nasal provida por fluidodinâmica computacional pode variar até 50% de acordo com os critérios de segmentação da tomografia computadorizada. O modelo de turbulência k-w padrão apresentou acurácia de 93,1%, demonstrando melhor desempenho que o modelo laminar para prever a resistência da cavidade nasal. A correlação entre a vazão em 75Pa obtida por rinomanometria e fluidodinâmica computacional foi alta para ambas as cavidades, Pearson r = 0,75 p < 0,001. Não houve concordância entre a resistência nasal fornecida pelos dois métodos. A resistência nasal por fluidodinâmica computacional é, em média, 65% da resistência por rinomanometria. Conclusão: os critérios para segmentação da cavidade nasal interferem na resistência calculada por fluidodinâmica computacional. A metodologia de fluidodinâmica computacional para calcular a resistência nasal foi validada experimentalmente. O modelo de escoamento turbulento é melhor que o modelo laminar para calcular a resistência nasal. A resistência nasal calculada por fluidodinâmica computacional apresentou alta correlação com a medida por rinomanometria anterior ativa, mas o nível de concordância entre os métodos não permite comparação direta entre os valores obtidos por cada um / Introduction: Nasal obstruction is a symptom present in various nasal diseases. This project proposes to develop a methodology for the calculation of nasal resistance to airflow through computational fluid dynamics and, to compare the results of this technique with those of rhinomanometry. Methods: nasal airflow resistance was measured by rhinomanometry, experimentally and computational fluid dynamics. We investigated the influence of computed tomography segmentation on the computational fluid dynamics variables. The computational model of laminar flow was compared to the kw turbulence model. The accuracy, correlation and agreement between the nasal resistance calculated by computational fluid dynamics was analyzed comparing it with nasal resistance obtained through experiment and rhinomanometry. Results: The nasal resistance provided by computational fluid dynamics can vary up to 50% according to the computed tomography segmentation criteria. The k-w turbulence model showed accuracy of 93.1%, presenting a better performance than the laminar model to predict nasal cavity resistance. The correlation between the flow in 75Pa obtained by rhinomanometry and computational fluid dynamics was high for both cavities, Pearson r >= 0.75 p < 0.001. There was no agreement between nasal resistance provided by the two methods. Nasal resistance due to computational fluid dynamics is, on average, 65% of rhinomanometric resistance. Conclusion: the criteria used for nasal cavity segmentation interfere with the resistance calculated by computational fluid dynamics. The methodology of computational fluid dynamics to calculate nasal resistance was validated experimentally. The turbulent flow model is better than the laminar model to calculate nasal resistance. The nasal resistance calculated by computational fluid dynamics showed a high correlation with the measurement by active rhinomanometry, but the level of agreement between the methods does not allow a direct comparison between the values obtained by each one

Introdução: A obstrução nasal é um sintoma presente em várias doenças nasais. Este projeto propõe desenvolver uma metodologia para o cálculo da resistência nasal ao fluxo aerífero por meio de fluidodinâmica computacional e comparar os resultados dessa técnica com os da rinomanometria. Métodos: a resistência nasal ao fluxo aerífero foi medida por rinomanometria, experimentalmente e por fluidodinâmica computacional. A influência da segmentação da tomografia computadorizada nas variáveis de fluidodinâmica computacional foi investigada. O modelo computacional de escoamento laminar foi comparado ao modelo de turbulência k-w padrão. Foram analisadas a acurácia, correlação e concordância entre a resistência nasal calculada por fluidodinâmica computacional com aquela obtida por experimento e rinomanometria. Resultados: A resistência nasal provida por fluidodinâmica computacional pode variar até 50% de acordo com os critérios de segmentação da tomografia computadorizada. O modelo de turbulência k-w padrão apresentou acurácia de 93,1%, demonstrando melhor desempenho que o modelo laminar para prever a resistência da cavidade nasal. A correlação entre a vazão em 75Pa obtida por rinomanometria e fluidodinâmica computacional foi alta para ambas as cavidades, Pearson r = 0,75 p < 0,001. Não houve concordância entre a resistência nasal fornecida pelos dois métodos. A resistência nasal por fluidodinâmica computacional é, em média, 65% da resistência por rinomanometria. Conclusão: os critérios para segmentação da cavidade nasal interferem na resistência calculada por fluidodinâmica computacional. A metodologia de fluidodinâmica computacional para calcular a resistência nasal foi validada experimentalmente. O modelo de escoamento turbulento é melhor que o modelo laminar para calcular a resistência nasal. A resistência nasal calculada por fluidodinâmica computacional apresentou alta correlação com a medida por rinomanometria anterior ativa, mas o nível de concordância entre os métodos não permite comparação direta entre os valores obtidos por cada um / Introduction: Nasal obstruction is a symptom present in various nasal diseases. This project proposes to develop a methodology for the calculation of nasal resistance to airflow through computational fluid dynamics and, to compare the results of this technique with those of rhinomanometry. Methods: nasal airflow resistance was measured by rhinomanometry, experimentally and computational fluid dynamics. We investigated the influence of computed tomography segmentation on the computational fluid dynamics variables. The computational model of laminar flow was compared to the kw turbulence model. The accuracy, correlation and agreement between the nasal resistance calculated by computational fluid dynamics was analyzed comparing it with nasal resistance obtained through experiment and rhinomanometry. Results: The nasal resistance provided by computational fluid dynamics can vary up to 50% according to the computed tomography segmentation criteria. The k-w turbulence model showed accuracy of 93.1%, presenting a better performance than the laminar model to predict nasal cavity resistance. The correlation between the flow in 75Pa obtained by rhinomanometry and computational fluid dynamics was high for both cavities, Pearson r >= 0.75 p < 0.001. There was no agreement between nasal resistance provided by the two methods. Nasal resistance due to computational fluid dynamics is, on average, 65% of rhinomanometric resistance. Conclusion: the criteria used for nasal cavity segmentation interfere with the resistance calculated by computational fluid dynamics. The methodology of computational fluid dynamics to calculate nasal resistance was validated experimentally. The turbulent flow model is better than the laminar model to calculate nasal resistance. The nasal resistance calculated by computational fluid dynamics showed a high correlation with the measurement by active rhinomanometry, but the level of agreement between the methods does not allow a direct comparison between the values obtained by each one

Profiling techniques to detect performance bugs in applications are usually customized to detect a specific bug pattern and involve significant engineering effort. In spite of this effort, many techniques either suffer from high runtime overheads or are imprecise. This necessitates the design of a common and efficient instrumentation substrate that profiles the flow of objects during an execution. Designing such a substrate which enables profile generation precisely with low overhead is non-trivial due to the number of objects created, accessed and paths traversed by them in an execution. In this thesis, we design and implement an efficient instrumentation substrate that efficiently generates object flow profiles for Java programs, without requiring any modifications to the underlying virtual machine. We achieve this by applying Ball-Larus numbering on a specialized hy-brid ow graph (hfg). The hfg path profiles that are collected during runtime are post-processed o ine to derive the object flow profiles. We extend the design to handle inter-procedural objec flows by constructing flow summaries for each method and incorporating them appropriately. We have implemented the substrate and validated its efficacy by applying it on programs from popular benchmark suites including dacapo and java-grande. The results demonstrate the scalability of our approach, which handles 0.2M to 0.55B object accesses with an average runtime overhead of 8x. We also demonstrate the effectiveness of the generated profiles by implementing three client analyses that consume the profiles to detect performance bugs. The analyses are able to detect 38 performance bugs which when refactored result in signi cant performance gains (up to 30%) in running times.