Vertical structure of the low frequency horizontal currents at the northern edge of the Loop Current during eddy shedding events is observed using concurrent hydrographic, moored, and satellite altimetry data from 2005. Dynamic modes are calculated at three deep (~3000 m), full water-column moorings in the eastern Gulf of Mexico. Time-series of the barotropic and first two baroclinic modes are found using a least squares minimization that fits theoretically derived modes to observed moored velocity data. EOF analyses show that the majority of observed variance is explained by a surface-trapped mode that is highly coherent with the temporal amplitudes of the first baroclinic mode, and a lower, but significant percentage of variance is captured in bottom-intensified modes. Amplitudes of the second empirical mode indicate that currents are more coherent in the ocean interior approaching the Loop Current, as more variance is explained by this mode at the southernmost mooring near the Loop Current. A dynamic mode decomposition of the horizontal currents reveals that the barotropic and first baroclinic modes exhibit low frequency variability and eddy time scales of 10 – 40 days. Second baroclinic mode amplitudes show higher frequency variability and shorter time scales. A model utility test for the least squares fit of modeled to observed velocity shows that the second baroclinic mode is useful to the statistical model during 50 – 85 % of the mooring deployment, and is particularly necessary to the model when cyclonic features are present in the study area. The importance of the second baroclinic mode to the model increases significantly closer to the Loop Current. High-speed currents associated with the Loop Current and anticyclones stimulate a strong first baroclinic response, but the second baroclinic mode amplitudes are found to be similar in magnitude to the first baroclinic mode amplitudes at times. This happens episodically and could be an indication of higher order dynamics related to frontal eddies or Loop Current eddy shedding.

"As water covers over 75% surface area of the earth, humans have an innate desire to explore the underwater environment for various aims. Physiological responses are induced in humans and animals to adapt to different stresses imposed by the hyperbaric environment. When these stresses become overwhelming, certain hazards can occur to individuals in underwater or in similar hyperbaric environments, and they may include nitrogen narcosis, oxygen toxicity and decompression sickness (DCS). There are evidences showing that the autonomic nervous system (ANS) plays an important role in diving reflex and physiological responses to diving hazards. However, the assessment of the autonomic nervous activity during SCUBA dives and diving-related hazards are mostly absent from the literature. Thus, in order to evaluate the autonomic nervous alterations that may occur during diving, especially during DCS, the following three experiments were performed in this study: the simulated dives of human subjects in a hyperbaric chamber, the SCUBA diving performed in seawater and induced decompression sickness in a swine model. A novel algorithm developed in our lab, principal dynamic mode (PDM) analysis, is applied to the above data. It has been shown that the PDM is able to accurately separate the sympathetic and parasympathetic dynamics of the ANS, and subsequently it is able to obtain a better quantification of the autonomic nervous activity than a current golden-standard approach. Through the study, dominance of the parasympathetic modulation was found in both hyperbaric chamber and SCUBA diving conditions. And more stresses were present in real dives, compared to simulated dives in chamber. In the swine DCS model, we found neurological DCS and cardiopulmonary DCS resulted in different alterations in the ANS. Furthermore, tracking dynamics of the parasympathetic modulations via the PDM method may allow discrimination between cardiopulmonary DCS and neurological DCS, and has potential use as a marker for early diagnosis of cardiopulmonary DCS. "

Detonation is a supersonic mode of combustion that is modeled by a system of conservation laws of compressible fluid mechanics coupled with the equations describing thermodynamic and chemical properties of the fluid. Mathematically, these governing equations admit steady-state travelling-wave solutions consisting of a leading shock wave followed by a reaction zone. However, such solutions are often unstable to perturbations and rarely observed in laboratory experiments. The goal of this work is to study the stability of travelling-wave solutions of detonation models by the following novel approach. We linearize the governing equations about a base travelling-wave solution and solve the resultant linearized problem using high-order numerical methods. The results of these computations are postprocessed using dynamic mode decomposition to extract growth rates and frequencies of the perturbations and predict stability of travelling-wave solutions to infinitesimal perturbations. We apply this approach to two models based on the reactive Euler equations for perfect gases. For the first model with a one-step reaction mechanism, we find agreement of our results with the results of normal-mode analysis. For the second model with a two-step mechanism, we find that both types of admissible travelling-wave solutions exhibit the same stability spectra. Then we investigate the Fickett’s detonation analogue coupled with a particular reaction-rate expression. In addition to the linear stability analysis of this model, we demonstrate that it exhibits rich nonlinear dynamics with multiple bifurcations and chaotic behavior.

A research-scale solid-fuel ramjet test article has been developed to study the secondary combustion zone of solid fuel ramjets. Tests were performed at a constant core air mass flowrate of 0.77 kg/s with 0%, 15%, and 30% bypass ratios. The propulsive performance analysis results indicate that the 0% bypass case had the highest regression rate and fuel mass flowrate. The regression rate and fuel mass flowrate of fuel without carbon black was the lowest. The specific impulse with air mass flowrate included was highest for the 0% bypass case reaching 130 s and lowest for the 30% bypass case reaching 110 s. For specific impulse with air mass flowrate excluded, the 30% bypass case achieved 2,800 s while the 0% bypass case achieved 1,800 s. The characteristic velocity was greatest for 0% bypass reaching 1,025 m/s and lowest for 30% bypass reaching 900 m/s. The combustion efficiency was highest for the 15% bypass case with carbon black addition approaching 0.82. 50 kHz and 75 kHz CH* chemiluminescence imaging was performed. Analyzing thin slivers of the images over 40,001 frames with frequency-domain techniques showed that most of the high amplitude content occurred below 1-5kHz with small peaks near 20 kHz and 30 kHz. Dynamic mode decomposition (DMD) was performed on sets of 10,001 spatially-calibrated images and their corresponding uncalibrated, uncropped images. Most of the tests exhibited low-frequency axial pumping, transverse modes, and other mode shapes indicative of the secondary injection. The prominence of transverse and other jet-related modes over axial modes appeared to be related to increasing bypass ratio. High-frequency axial modes also appeared in a case thought to have high core-flow momentum that did not appear at these high frequencies for other cases. The DMD modes for 0% bypass were indiscernible due to high soot content. Most of the modes corresponding to the calibrated images also appeared in the uncalibrated images, however, with different mode amplitude rankings. PIV was performed at 5 kHz for one test at 15% bypass. The instantaneous vector fields for these tests displayed local velocities up to 600 m/s. The mean images showed velocities up to 250 m/s. The two-dimensional turbulent kinetic energies reached 200 m2/s2 in several regions throughout the flowfield. The turbulence intensity exceeded 0.20 near the bottom of the flowfield.

We examine the related nature of two identification algorithms, subspace identification (SID) and Dynamic Mode Decomposition (DMD), and their correctness properties over a broad range of problems. This investigation begins by noting the strong relationship between the two algorithms, both drawing significantly on the pseudoinverse calculation using singular value decomposition, and ultimately revealing that DMD can be viewed as a substep of SID. We then perform extensive computational studies, characterizing the performance of SID on problems of various model orders and noise levels. Specifically, we generate 10,000 random systems for each model order and noise level, calculating the average identification error for each case, and then repeat the entire experiment to ensure the results are, in fact, consistent. The results both quantify the intrinsic algorithmic error at zero-noise, monotonically increasing with model complexity, as well as demonstrate an asymptotically linear degradation to noise intensity, at least for the range under study. Finally, we close by demonstrating DMD's ability to recover system matrices, because its access to full state measurements makes them identifiable. SID, on the other hand, can't possibly hope to recover the original system matrices, due to their fundamental unidentifiability from input-output data. This is true even when SID delivers excellent performance identifying a correct set of equivalent system matrices.

No description available.

Data-driven intelligent transportation systems (ITS) are increasingly playing a critical role in improving the efficiency of the existing transportation network and addressing traffic challenges in large cities, such as safety and road congestion. This dissertation employs data dimensionality reduction, reinforcement learning, and discrete outcome models to improve traffic flow and transportation safety. First, we propose a novel data-driven technique based on Koopman operator theory and dynamic mode decomposition (DMD) to address the complex nonlinear dynamics of signalized intersections. This approach not only provides a better understanding of intersection behavior but also offers faster computation times, making it a valuable tool for system identification and controller design. It represents a significant step towards more efficient and effective traffic management solutions. Second, we propose an innovative phase-switching approach for traffic light control using deep reinforcement learning, enhancing the efficiency of signalized intersections. The novel reward function, based on speed, waiting time, deceleration, and time to collision (TTC) for each vehicle, maximizes traffic flow and safety through real-time optimization. Finally, we introduce a mixed spline indicator pooled model, an approach for multivariate crash severity prediction, addressing the limitations of previous models by capturing temporal instability. It carefully incorporates additional independent variables to measure parameter slope changes over time, enhancing data fit and predictive accuracy. The developed models are estimated and validated using data from the Central Florida region.

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.

No description available.

In axial compressors with a relatively large blade tip clearance, an unsteady phenomenon denoted as rotating instability (RI) can be detected when the compressor is throttled to the operating points near the stability limit. In the frequency domain, RIs are shown as a hump lower than the blade passing frequency. This indicates an increase in noise level and might cause blade vibration and other undesirable structural issues. In this thesis, a comprehensive study on RIs is performed based on an axial compressor rotor row of the Low Speed Research Compressor at Technische Universität Dresden. Three blade tip clearances are investigated, and a groove casing treatment is mounted over the shroud for flow control. Methods of numerical modeling are evaluated, and zonal large eddy simulation is selected as the numerical model. By analyzing the flow properties and applying the dynamic mode decomposition, the coherent flow structure corresponding to the dominant frequency of RIs is extracted and visualized as the waves located in the blade tip region. The criteria for the appearance of RIs in the investigated research object are concluded.