Large-scale testing of low-rise buildings or components of tall buildings is essential as it provides more representative information about the realistic wind effects than the typical small scale studies, but as the model size increases, relatively less large-scale turbulence in the upcoming flow can be generated. This results in a turbulence power spectrum lacking low-frequency turbulence content. This deficiency is known to have significant effects on the estimated peak wind loads. To overcome these limitations, the method of Partial Turbulence Simulation (PTS) has been developed recently in the FIU Wall of Wind lab to analytically compensate for the effects of the missing low-frequency content of the spectrum. This method requires post-test analysis procedures and is based on the quasi-steady assumptions. The current study was an effort to enhance that technique by investigating the effect of scaling and the range of applicability of the method by considering the limitations risen from the underlying theory, and to simplify the 2DPTS (includes both in-plane components of the turbulence) by proposing a weighted average method. Investigating the effect of Reynolds number on peak aerodynamic pressures was another objective of the study. The results from five tested building models show as the model size was increased, PTS results showed a better agreement with the available field data from TTU building. Although for the smaller models (i.e., 1:100,1:50) almost a full range of turbulence spectrum was present, the highest peaks observed at full-scale were not reproduced, which apparently was because of the Reynolds number effect. The most accurate results were obtained when the PTS was used in the case with highest Reynolds number, which was the1:6 scale model with a less than 5% blockage and a xLum/bm ratio of 0.78. Besides that, the results showed that the weighted average PTS method can be used in lieu of the 2DPTS approach. So to achieve the most accurate results, a large-scale test followed by a PTS peak estimation method deemed to be the desirable approach which also allows the xLum/bm values much smaller than the ASCE recommended numbers.

In this thesis the Ramberg-Osgood nonlinear model for describing the behavior of many different materials is investigated. A brief overview of the model as it is currently used in the literature is undertaken and several misunderstandings and possible pitfalls in its application is pointed out, especially as it pertains to more recent approaches to finding solutions involving the model. There is an investigation of the displacement of a cantilever beam under a combined loading consisting of a distributed load across the entire length of the beam and a point load at its end and new solutions to this problem are provided with a mixture of numerical techniques, which suggest strong mathematical consistency within the model for all theoretical assumptions made. A physical experiment was undertaken and the results prove to be inaccurate when using parameters derived from tensile tests, but when back calculating parameters from the beam test the model has a 14.40% error at its extreme against the experimental data suggesting the necessity for further testing.

The performance of building envelopes and roofing systems significantly depends on accurate knowledge of wind loads and the response of envelope components under realistic wind conditions. Wind tunnel testing is a well-established practice to determine wind loads on structures. For small structures much larger model scales are needed than for large structures, to maintain modeling accuracy and minimize Reynolds number effects. In these circumstances the ability to obtain a large enough turbulence integral scale is usually compromised by the limited dimensions of the wind tunnel meaning that it is not possible to simulate the low frequency end of the turbulence spectrum. Such flows are called flows with Partial Turbulence Simulation. In this dissertation, the test procedure and scaling requirements for tests in partial turbulence simulation are discussed. A theoretical method is proposed for including the effects of low-frequency turbulences in the post-test analysis. In this theory the turbulence spectrum is divided into two distinct statistical processes, one at high frequencies which can be simulated in the wind tunnel, and one at low frequencies which can be treated in a quasi-steady manner. The joint probability of load resulting from the two processes is derived from which full-scale equivalent peak pressure coefficients can be obtained. The efficacy of the method is proved by comparing predicted data derived from tests on large-scale models of the Silsoe Cube and Texas-Tech University buildings in Wall of Wind facility at Florida International University with the available full-scale data. For multi-layer building envelopes such as rain-screen walls, roof pavers, and vented energy efficient walls not only peak wind loads but also their spatial gradients are important. Wind permeable roof claddings like roof pavers are not well dealt with in many existing building codes and standards. Large-scale experiments were carried out to investigate the wind loading on concrete pavers including wind blow-off tests and pressure measurements. Simplified guidelines were developed for design of loose-laid roof pavers against wind uplift. The guidelines are formatted so that use can be made of the existing information in codes and standards such as ASCE 7-10 on pressure coefficients on components and cladding.

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.

Neuronal membrane disruption and mechanoporation are nanoscale damage mechanisms that critically affect brain cell viability during traumatic brain injury (TBI). These nanoscale cellular impairments are elusive in experiments and necessitate in silico approaches such as molecular dynamics (MD) simulations. Implementing MD, this research aims to investigate the effects of different key factors related to membrane deformation and damage, including force field resolutions, lipid compositions, and loading conditions. To examine the impact of force field resolution, MD deformation simulations were conducted on 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) lipid bilayer membranes, using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) force fields. The mechanical responses of the three models progressively changed based on the coarse-graining level. The coarser systems exhibited lower yield stresses and failure strains, and higher mechanoporation damage. To study the influence of lipid components, tensile deformation was applied on seven lipid bilayers, each of which contained a different lipid type commonly found in human brain membrane. Larger headgroup structure, greater degree of unsaturation, and tail-length asymmetry decreased lipid packing, increased the area per lipid (APL), and decreased the failure strain of membrane. Lastly, the deformation behavior of a complex multicomponent MD bilayer (realistically representing human neuronal plasma membrane) under different strain rates and strain states was inspected. The yield stress increased with increasing strain rates and more equibiaxial strain states. Meanwhile, lower strain rates resulted in fewer but larger pores, as well as lower strain and APL at failure. Besides, more equibiaxial strain states exhibited more and larger pores, and lower failure strain. Similar failure APL was obtained regardless of strain states, suggesting that the membrane failed when reaching a critical APL value. In addition, the inclusion of cholesterol was shown to decrease the critical APL. The strain-state dependence results were then used to update the Membrane Failure Limit Diagram (MFLD) that indicates the planar strains for potential membrane failure. Overall, the study provides a non-invasive approach that aids in the current understanding of nanoscale neuronal damage dynamics and essential aspects affecting membrane mechanical responses, and furthermore lays the groundwork for future studies on brain injury biomechanics under various TBI scenarios.

Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to system identification and forecasting of solutions of nonlinear differential equations by replacing matrix multiplication with convolution transformation. In particular, we develop convolution-based approach to dynamic mode decomposition and discuss its application to problems not solvable otherwise.

An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%.

<p>In this study, we concentrate on spin-1/2 systems. A series of tools using the Liouville space method have been developed for simulating of NMR of arbitrary pulse sequences.</p> <p>We have calculated one- and two-spin symbolically, and larger systems numerically of steady states. The one-spin calculations show how SSFP converges to continuous wave NMR. A general formula for two-spin systems has been derived for the creation of double-quantum signals as a function of irradiation strength, coupling constant, and chemical shift difference. The formalism is general and can be extended to more complex spin systems.</p> <p>Estimates of transverse relaxation, R₂, are affected by frequency offset and field inhomogeneity. We find that in the presence of expected B₀ inhomogeneity, off-resonance effects can be removed from R₂ measurements, when ||omega||<= 0.5 gamma\,B₁ in Hahn echo experiments, when ||omega||<=gamma\,B₁ in CPMG experiments with specific phase variations, by fitting exact solutions of the Bloch equations given in the Lagrange form.</p> <p>Approximate solutions of CPMG experiments show the specific phase variations can significantly smooth the dependence of measured intensities on frequency offset in the range of +/- 1/2 gamma\,B₁. The effective R₂ of CPMG experiments when using a phase variation scheme can be expressed as a second-order formula with respect to the ratio of offset to pi-pulse amplitude.</p> <p>Optimization problems using the exact or approximate solution of the Bloch equations are established for designing optimal broadband universal rotation (OBUR) pulses. OBUR pulses are independent of initial magnetization and can be applied to replace any pulse of the same flip angles in a pulse sequence. We demonstrate the process to exactly and efficiently calculate the first- and second-order derivatives with respect to pulses. Using these exact derivatives, a second-order optimization method is employed to design pulses. Experiments and simulations show that OBUR pulses can provide more uniform spectra in the designed offset range and come up with advantages in CPMG experiments.</p> / Doctor of Philosophy (PhD)

<p>This thesis is concerned with a theoretical and experimental investigation of a duckbill valve (DBV). Duckbill valves are non-return valves made of a composite material, which deforms to open the valve as the upstream pressure increases. The head-discharge behavior is a fluid-structure interaction (FSI) problem since the discharge depends on the valve opening that in turn depends on the pressure distribution along the valve produced by the discharge. To design a duckbill valve, a theoretical model is required, which will predict the head-discharge characteristics as a function of the fluid flow through the valve and the valve material and geometry.</p> <p>The particular valves of concern in this study, which can be very large, are made from laminated, fiber-reinforced rubber. Thus, the structural problem has strong material as well as geometric nonlinearities due to large deflections. Clearly, a fully coupled FSI analysis using three-dimensional viscous flow would be very challenging and therefore, a simplified approach was sought that treats the essential aspects of the problem in a tractable way. For this purpose, the DBV was modeled using thick shell finite elements, which included the laminates of hyperelastic rubber and orthotropic fabric reinforcement. The finite element method (FEM) was simplified by assuming that the arch side edges of the valve were clamped. The unsteady 1D flow equation was used to model the ideal fluid dynamics that enabled a full FSI analysis. Moreover, verification for the ideal flow was carried out using a transient, Reynolds-averaged Navier-Stokes finite volume solver for the viscous flow corresponding to the deformed valve predicted by the simplified FSI model.</p> <p>In order to validate the predictions of the FSI simulations, an experimental study was performed at several mass flow rates. Pressure drops along the water tunnel, valve inlet and outlet velocity profiles were measured, as well as valve opening deformations as functions of upstream pressures.</p> <p>Additionally, the valve deformations under various back pressures were analyzed when the downstream pressure exceeded the upstream pressure using the layered shell model without coupling and with simplified boundary constraints to avoid solving the contact problem for the inward-deformed duckbill valve. Flow-induced vibration (FIV) of the valve at small openings was also examined to improve our understanding of the valve stability behaviour. Some interesting valve oscillation phenomena were observed.</p> <p>Conclusions are drawn regarding the FSI model on the predictions and comparisons with the experimental results. The transient 1D flow equation has been demonstrated to adequately model the fluid dynamics of a duckbill valve, largely due to the fact that viscous effects are negligible except when the valve is operating at very small openings. Fiber reinforcement of the layered composite rubber was found to play an important role in controlling duckbill valve material stretch, especially at large openings. The model predicts oscillations at small openings but more research is required to better understand this behaviour.</p> / Doctor of Philosophy (PhD)

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of categorical attributes. Visualizing preferences is essential to provide users with insights into the process of decision making, while learning preferences from data is practically important, as it is ineffective to elicit preference models directly from users. The results obtained from this thesis are two parts: 1) for preference visualization, aweb- basedsystem is created that visualizes various types of lexicographic preference tree models learned by a greedy learning algorithm; 2) for preference learning, a genetic algorithm is designed and implemented, called GA, that learns a restricted type of lexicographic preference tree, called unconditional importance and unconditional preference tree, or UIUP trees for short. Experiments show that GA achieves higher accuracy compared to the greedy algorithm at the cost of more computational time. Moreover, a Dynamic Programming Algorithm (DPA) was devised and implemented that computes an optimal UIUP tree model in the sense that it satisfies as many examples as possible in the dataset. This novel exact algorithm (DPA), was used to evaluate the quality of models computed by GA, and it was found to reduce the factorial time complexity of the brute force algorithm to exponential. The major contribution to the field of machine learning and data mining in this thesis would be the novel learning algorithm (DPA) which is an exact algorithm. DPA learns and finds the best UIUP tree model in the huge search space which classifies accurately the most number of examples in the training dataset; such model is referred to as the optimal model in this thesis. Finally, using datasets produced from randomly generated UIUP trees, this thesis presents experimental results on the performances (e.g., accuracy and computational time) of GA compared to the existent greedy algorithm and DPA.