The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses.

An airborne, tethered, multi-rotor wind turbine, effectively a rotorcraft kite, provides one platform for accessing the energy in high altitude winds. The craft is maintained at altitude by its rotors operating in autorotation, and its equilibrium attitude and dynamic performance are affected by the aerodynamic rotor forces, which in turn are affected by the orientation and motion of the craft. The aerodynamic performance of such rotors can vary significantly depending on orientation, influencing the efficiency of the system. This thesis analyzes the aerodynamic performance of an autorotating rotor through a range of angles of attack covering those experienced by a typical autogyro through that of a horizontal-axis wind turbine. To study the behavior of such rotors, an analytical model using the blade element theory coupled with momentum theory was developed. The model uses a rigid-rotor assumption and is nominally limited to cases of small induced inflow angle and constant induced velocity. The model allows for linear twist. In order to validate the model, several rotors -- off-the-shelf model-aircraft propellers -- were tested in a low speed wind tunnel. Custom built mounts allowed rotor angles of attack from 0 to 90 degrees in the test section, providing data for lift, drag, thrust, horizontal force, and angular velocity. Experimental results showed increasing thrust and angular velocity with rising pitch angles, whereas the in-plane horizontal force peaked and dropped after a certain value. The analytical results revealed a disagreement with the experimental trends, especially at high pitch angles. The discrepancy was attributed to the rotor operating in turbulent wake and vortex ring states at high pitch angles, where momentum theory has proven to be invalid. Also, aerodynamic design constants, which are not precisely known for the test propellers, have an underlying effect on the analytical model. The developments of the thesis suggest that a different analytical model may be needed for high rotor angles of attack. However, adding a term for resisting torque to the model gives analytical results that are similar to the experimental values.

This dissertation studies two outstanding microscale fluid mechanics problems: 1) mechanisms of gas production from the nanopores of shale; 2) enhanced mass flow rate in steady compressible gas flow through a micro-conduit.

The dissertation starts with a study of a volumetric expansion driven drainage flow of a viscous compressible fluid from a small capillary and channel in the low Mach number limit. An analysis based on the linearized compressible Navier-Stokes equations with no-slip condition shows that fluid drainage is controlled by the slow decay of the acoustic wave inside the capillary and the no-slip flow exhibits a slip-like mass flow rate. Numerical simulations are also carried out for drainage from a small capillary to a reservoir or a contraction of finite size. By allowing the density wave to escape the capillary, two wave leakage mechanisms are identified, which are dependent on the capillary length to radius ratio, reservoir size and acoustic Reynolds number. Empirical functions are generated for an effective diffusive coefficient which allows simple calculations of the drainage rate using a diffusion model without the presence of the reservoir or contraction.

In the second part of the dissertation, steady viscous compressible flow through a micro-conduit is studied using compressible Navier-Stokes equations with no-slip condition. The mathematical theory of Klainerman and Majda for low Mach number flow is employed to derive asymptotic equations in the limit of small Mach number. The overall flow, a combination of the Hagen-Poiseuille flow and a diffusive velocity shows a slip-like mass flow rate even through the overall velocity satisfies the no-slip condition. The result indicates that the classical formulation includes self-diffusion effect and it embeds the Extended Navier-Stokes equation theory (ENSE) without the need of introducing additional constitutive hypothesis or assuming slip on the boundary. Contrary to most ENSE publications, the predicted mass flow rate is still significantly below the measured data based on an extensive comparison with thirty-five experiments.

Lidar has demonstrated its utility in meteorological studies, wind resource assessment, and wind farm control. More recently, lidar has gained widespread attention for autonomous vehicles.

The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.

The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018).

Two urban flows are analyzed, one concerned with pollutant transport in a Phoenix, Arizona neighborhood and the other with windshear detection at the Hong Kong International Airport (HKIA).

Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion.

A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data.

Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.

The global energy demand is expected to grow significantly in the next several decades and support for energy generation with high carbon emissions is continuing to decline. Alternative methods have gained interest, and wind energy has established itself as a viable source. Standard wind farms have limited room for growth and improvement, so wind energy has started to explore different directions. The urban environment is a potential direction for wind energy due to its proximity to the bulk of energy demand. CFD analysis has demonstrated that the presence of buildings can accelerate wind speeds between buildings and on rooftops. However, buildings generate areas of increased turbulence at their surface. The turbulence thickness and intensity vary with roof shape, building height, and building orientation. The analysis has concluded that good wind resource is possible in the urban environment in specific locations. With that, turbine selection becomes very important. A comparison has concluded that vertical axis wind turbines are more useful in the urban environment than horizontal axis wind turbines. Furthermore, building-augmented wind turbines are recommended because they are architecturally integrated into a building for the specific purpose of generating more energy. The research has concluded that large-scale generation in the urban environment is unlikely to be successful, but small-scale generation is quite viable. Continued research and investigation on urban wind energy is recommended.

Environmental remote sensing has seen rapid growth in the recent years and Doppler wind lidars have gained popularity primarily due to their non-intrusive, high spatial and temporal measurement capabilities. While lidar applications early on, relied on the radial velocity measurements alone, most of the practical applications in wind farm control and short term wind prediction require knowledge of the vector wind field. Over the past couple of years, multiple works on lidars have explored three primary methods of retrieving wind vectors viz., using homogeneous windfield assumption, computationally extensive variational methods and the use of multiple Doppler lidars.

Building on prior research, the current three-part study, first demonstrates the capabilities of single and dual Doppler lidar retrievals in capturing downslope windstorm-type flows occurring at Arizona’s Barringer Meteor Crater as a part of the METCRAX II field experiment. Next, to address the need for a reliable and computationally efficient vector retrieval for adaptive wind farm control applications, a novel 2D vector retrieval based on a variational formulation was developed and applied on lidar scans from an offshore wind farm and validated with data from a cup and vane anemometer installed on a nearby research platform. Finally, a novel data visualization technique using Mixed Reality (MR)/ Augmented Reality (AR) technology is presented to visualize data from atmospheric sensors. MR is an environment in which the user's visual perception of the real world is enhanced with live, interactive, computer generated sensory input (in this case, data from atmospheric sensors like Doppler lidars). A methodology using modern game development platforms is presented and demonstrated with lidar retrieved wind fields. In the current study, the possibility of using this technology to visualize data from atmospheric sensors in mixed reality is explored and demonstrated with lidar retrieved wind fields as well as a few earth science datasets for education and outreach activities.

A numerical study of chemotaxis in 3D turbulence is presented here. Direct Numerical

Simulation were used to calculate the nutrient uptake for both motile and non-motile bacterial

species and by applying the dynamical systems theory the effect of flow topology on the

variability of chemotaxis is analyzed. It is done by injecting a highly localized patch of nutrient

in the turbulent flow, and analyzing the evolution of reaction associated with the observed

high and low stretching regions. The Gaussian nutrient patch is released at different locations

and the corresponding nutrient uptake is obtained. The variable stretching characteristics of

the flow is depicted by Lagrangian Coherent Structures and the roles they play in affecting the

uptake are analyzed. The Lagrangian Coherent Structures are quantified by the Finite Time

Lyapunov Exponents which is a measure of the average stretching experienced by the flow in

finite time. It is found that in high stretching regions, the motile bacteria are attracted to the

nutrient patch very quickly, but also dispersed quickly; whereas in low stretching regions the

bacteria respond slower towards the nutrient patch. However the total uptake is intricately

determined by stretching history. These reaction characteristics are reflected in the several

realizations of simulations. This helps in understanding turbulence intensity and how it affects

the uptake of the nutrient.

This research work uses the Weather Research and Forecasting Model to study the effect of large wind farms with an area of 900 square kilometers and a high power density of 7.58 W/m2 on regional climate. Simulations were performed with a wind farm parameterization scheme turned on in south Oregon. Control cases were also run with the parameterization scheme turned off. The primary emphasis was on offshore wind farms. Some analysis on onshore wind farms was also performed. The effects of these wind farms were studied on the vertical profiles of temperature, wind speed, and moisture as well as on temperature and on wind speed near the surface and at hub height. The effects during the day and at night were compared. Seasonal variations were also studied by performing simulations in January and in July. It was seen that wind farms produce a reduction in wind speed at hub height and that the downward propagation of this reduction in wind speed lessens as the atmosphere becomes more stable. In all the cases studied, the wind farms produced a warming effect near the surface, with greater atmospheric stability leading to higher near-surface temperatures. It was also observed that wind farms caused a drying effect below the hub height and a moistening effect above it, because they had facilitated vertical transport of moisture in the air from the lower layers of the atmosphere to the layers of the atmosphere above the wind farm.