A new state-of-the-art CFD solver capable of simulating a broad range of complex engineering flows at real-life Reynolds numbers is developed. The method solves the three-dimensional incompressible unsteady Reynolds-averaged Navier-Stokes (URANS) equations closed with statistical turbulence models. Three such models are incorporated in the solver: the standard k - e model with wall functions, the Spalart-Allmaras model and the detached-eddy simulation (DES) model. The numerical solver employs domain decomposition with structured Chimera overset grids to handle complex, multi-connected geometries. The governing equations are discretized with second order accuracy schemes both in space and time. The capabilities and versatility of the numerical method are demonstrated by applying it to simulate two widely different flow problems: a) flow past a geometrical complex array of multiple bridge piers mounted both on a natural river reach and on a flat bed experimental flume; and b) flow in mechanical, bileaflet, prosthetic heart valve with the leaflets fixed in the fully-open position. Overset grid systems with several millions of grid nodes are used and grid-refinement and other numerical dependency studies are carried out to explore the sensitivity of the computed solutions to various numerical parameters. For all simulated cases, large-scale unsteadiness appears naturally as a result of excited mean-flow instabilities and the computed mean flowfields are shown to be in good quantitative agreement with experimental measurements. By analyzing the instantaneous flowfields numerous novel insights into the physics of both flow cases are obtained and discussed extensively. The results of this thesis demonstrate the potential of the new method as a powerful simulation tool for a broad range of cross-disciplinary engineering flow problems and underscore the need for physics-based numerical modeling by integrating CFD with laboratory experimentation.

Bridge scour is the term which describes the erosion of soil surrounding a bridge foundation due to water. Bridge scour can cause the reduction of the load carrying capacity of bridge foundations, excessive foundation settlements, and damage to bridge abutments. Bridges with foundations that are unstable for calculated and/or observed scour conditions are termed scour critical bridges. Approximately 25,000 bridges in the United States are classified as scour critical and about 600 of them are in Texas. This designation comes in part from the use of over-conservative methods that predict excessive scour depths in erosion resistant materials. Other methods have been developed to eliminate this over-conservatism but are uneconomical because they require site-specific erosion testing. The major contribution of this dissertation is a new method to assess a bridge for scour and erosion classification charts which categorizes the erodibility of geomaterials according to conventional engineering properties. The new method is a three level Bridge Scour Assessment (BSA) procedure which is relatively simple and economical. It does not require site-specific erosion testing and eliminates the over-conservatism in current methods. The first level, BSA 1, uses charts that extrapolate the maximum scour depth recorded during the life of the bridge to obtain the scour depth corresponding to a specified future flood event. The second level, BSA 2, determines the maximum scour depth and is carried out if BSA 1 does not conclude with a specific plan of action for the bridge. The third level, BSA 3, determines the time dependent scour depth and is carried out if BSA 2 does not conclude with a specific plan of action. The scour vulnerability depends on the comparison between the predicted and allowable scour depths. The 11 case histories used to validate the new method showed good agreement between predicted values and field measurements. BSA 1 was then applied to 16 bridges. In this process, 6 out of 10 bridges classified as scour critical by current methods were found to be stable. These results show that the new method allows for more realistic evaluation of bridges for scour while not requiring site-specific erosion testing.

Bridge scour is the leading cause of bridge damage nationwide. Successfully mitigating bridge scour problems depends on our ability to reliably estimate scour potential, design safe and economical foundation elements that account for scour potential, identify vulnerabilities related to extreme events, and recognize changes to the environmental setting that increase risk at existing bridges. This study leverages available information, gathered from several statewide resources, and adds watershed metrics to create a comprehensive, georeferenced dataset to identify parameters that correlate to bridges damaged in an extreme flood event. Understanding the underlying relationships between existing bridge condition, fluvial stresses, and geomorphological changes is key to identifying vulnerabilities in both existing and future bridge infrastructure. In creating this comprehensive database of bridge inspection records and associated damage characterization, features were identified that correlate to and discriminate between levels of bridge damage. Stream geomorphic assessment features were spatially joined to every bridge, marking the first time that geomorphic assessments have been broadly used for estimating bridge vulnerability. Stream power assessments and watershed delineations for every bridge and stream reach were generated to supplement the comprehensive database. Individual features were tested for their significance to discriminate bridge damage, and then used to create empirical fragility curves and probabilistic predictions maps to aid in future bridge vulnerability detection. Damage to over 300 Vermont bridges from a single extreme flood event, the August 28, 2011 Tropical Storm Irene, was used as the basis for this study. Damage to historic bridges was also summarized and tabulated. In some areas of Vermont, the storm rainfall recurrence interval exceeded 500 years, causing widespread flooding and damaging over 300 bridges. With a dataset of over 330 features for more than 2,000 observations to bridges that were damaged as well as not damaged in the storm, an advanced evolutionary algorithm performed multivariate feature selection to overcome the shortfalls of traditional logistic regression analysis. The analysis identified distinct combinations of variables that correlate to the observed bridge damage under extreme food events.

This thesis presents an experimental study both in the laboratory and field to develop and test a method for continuously measuring and monitoring scour using an automated identification technology known as Radio Frequency Identification (RFID). RFID systems consist of three main components, namely (a) the reader which controls the system, (b) the transponder (derived from transmitter/responder) that transmits data to the reader and (c) the excitation antenna that allows the communication between the reader and the transponder. The study provides an insight into the RFID technology and develops the framework for using this technology to eventually address two central themes in river mechanics and sediment transport; (a) the determination of the active layer thickness and (b) the scour/deposition depth around a hydraulic structure. In particular, this study develops the methodology for relating the signal strength of a radio frequency (RF) device with the distance between an excitation antenna and the RF device. The experiments presented herein are classified into two main groups, (1) the laboratory and (2) the RF signal vs. the detection distance experiments (field experiments). The laboratory experiments were designed to understand the effect of key RFID parameters (e.g., transponder orientation with respect to the excitation antenna plane, maximum antenna-transponder detection distance), measured in terms of the transponder return RF signal strength for various antenna-transponder distances, transponder orientations with respect to the excitation antenna plane and different mediums in between the excitation antenna and the transponder, on the overall performance of the RFID system. On the other hand, the RF signal vs. the detection distance experiments were based on the results obtained during the laboratory experiments and focused on developing calibration curves by relating the transponder return RF signal strength with the distance between the excitation antenna and a transponder. The laboratory results show that the dominant RFID parameters affecting the system performance are (a) the transponder orientation towards the excitation antenna plane and (b) the medium type in between the excitation antenna and the transponder. The differences in reading distances were attributed to the transponder inner antenna type, while the effect of the medium was related with the void ratio, where higher porosity materials have, less RF signal strength decay. The parameter that governs the RF signal strength decay was found to be the distance between the excitation antenna and the transponder (erosion process experiments). The RF signal strength decays almost linearly with distance, while the rate of the RF signal strength decay is controlled by the material type in between the excitation antenna and the transponder (deposition process experiments). The RF signal vs. the detection distance experiments demonstrate that the reading distance of the RFID system can be significantly increased by using a custom made excitation antenna. The custom made excitation antenna not only increases the reading distance between the antenna and the transponder to nearly 20 ft., but also allows the user to manipulate the excitation antenna's shape and size to meet the specific landscape requirements at the monitoring site.

Samples collected from 5 bridge sites from around the state of Georgia are analyzed to determine their erosion and rheologic behavior. Most sites were subject to large amounts of local scour due to flood events resulting from Tropical Storm Alberto in 1994. According to the Federal Highway Administration's Hydraulic Engineering Circular No. 18 by Richardson and Davis (2001), scouring of bridge foundations is the most common cause of bridge failure resulting from floods. The erosion rates of the soils are measured in a rectangular tilting flume capable of applying up to 21 Pa of shear stress at the bed. Samples from Shelby tubes are extruded into the flow from below the bed using a hydraulic piston. The displacement is measured as a function of time using a cable-pull potentiometer. The soils are also subject to extensive geotechnical analysis. Sieve and hydrometer analyses are performed to obtain the particle size distribution for each sample. Atterberg Limits and other standard geotechnical measures are also found. Additionally, insight into the shear strength and cohesive nature of the fine (<0.75 micrometers) particles is gained using a stress controlled rheometer to measure the rheological characteristics of the slurry. These results are used to improve and extend a relationship for the critical shear stress of soils developed in previous research that can be used in bridge scour prediction formulae as affected by soil parameters. In addition, the rheologic properties of the soil in terms of a dimensionless yield stress are related to the critical value of the Shields parameter for estimating critical shear stress for erosion.

abstract: There is a concerted effort in developing robust systems health monitoring/management (SHM) technology as a means to reduce the life cycle costs, improve availability, extend life and minimize downtime of various platforms including aerospace and civil infrastructure. The implementation of a robust SHM system requires a collaborative effort in a variety of areas such as sensor development, damage detection and localization, physics based models, and prognosis models for residual useful life (RUL) estimation. Damage localization and prediction is further complicated by geometric, material, loading, and environmental variabilities. Therefore, it is essential to develop robust SHM methodologies by taking into account such uncertainties. In this research, damage localization and RUL estimation of two different physical systems are addressed: (i) fatigue crack propagation in metallic materials under complex multiaxial loading and (ii) temporal scour prediction near bridge piers. With little modifications, the methodologies developed can be applied to other systems. Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature. For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2016

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Junction flows originate from the interaction between a fluid moving over a wall with an obstacle mounted on the same surface. Understanding the physics of such flows is of great interest to engineers responsible for the design of systems consisting of wall-body junctions. From aerodynamics to turbomachinery and electronics to bridge hydraulics, a number of phenomena (drag, heat transfer, scouring) are driven by the behavior of the most prominent feature of junction flows: the horseshoe vortex system (HVS). Focusing on turbulent flows, the complex dynamics of the HVS is established through its unsteadiness and non-uniformity. The fundamentals of this dynamically-rich phenomenon have been described within the body of a rapidly-expanding literature. Nevertheless, important aspects remain inadequately understood and call for further scrutiny. This study emphasized three of them, by investigating the effects of: model scale, wall roughness, and bed geometry. High-resolution experiments were carried out using Particle Image Velocimetry (PIV). Statistical analyses, vortex identification schemes, and Proper Orthogonal decomposition were employed to extract additional information from the large PIV datasets. The time-averaged topology of junction flows developing over a smooth and impermeable wall was independent of the flow Reynolds number, Re (parameter that expresses the effects of scale). On the contrary, time-resolved analysis revealed a trend of increasing vorticity, momentum, and eruptions of near-wall fluid with Re. New insights on the modal dynamics of the HVS were also documented in a modified flow mechanism. Wall roughness (modeled with a permeable layer of crushed stones) diffused turbulence and vorticity throughout the domain. This effect manifested with high levels of intermittency and spatial irregularity for the HVS. Energetic flow structures were also identified away from the typical footprint of the HVS. Finally, a novel implementation of PIV allowed for unique velocity measurements over an erodible bed. It was demonstrated that, during the initial stages of scouring, the downflow at the face of the obstacle becomes the dominant flow characteristic in the absence of the HVS. Notwithstanding modeling limitations, the physical insight contributed here could be used to enhance the design of systems with similar flow and geometrical characteristics. / Ph. D.

This study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quantification for one-dimensional diffusion processes. ANNs were successfully implemented in this study to develop nonlinear models for the mechanical properties of stabilized organic soils. ANN models were able to learn from the training examples and then generalize the trend to make predictions for the stiffness and strength of stabilized organic soils. A stepwise parameter selection and a sensitivity analysis method were implemented to identify the most relevant factors for the prediction of the stiffness and strength. Also, the variations of the stiffness and strength with respect to each factor were investigated. A deterministic and a probabilistic approach were proposed to evaluate the characteristics of unknown foundations of bridges subjected to scour. The proposed methods were successfully implemented and validated by collecting data for bridges in the Bryan District. ANN models were developed and trained using the database of bridges to predict the foundation type and embedment depth. The probabilistic Bayesian approach generated probability distributions for the foundation and soil characteristics and was able to capture the uncertainty in the predictions. The parametric and numerical uncertainties in the one-dimensional diffusion process were evaluated under varying observation conditions. The inverse problem was solved using Bayesian inference formulated by both the analytical and numerical solutions of the ordinary differential equation of diffusion. The numerical uncertainty was evaluated by comparing the mean and standard deviation of the posterior realizations of the process corresponding to the analytical and numerical solutions of the forward problem. It was shown that higher correlation in the structure of the observations increased both parametric and numerical uncertainties, whereas increasing the number of data dramatically decreased the uncertainties in the diffusion process.