Matching Items (291)
Description
The role of environmental factors that influence atmospheric propagation of sound originating from freeway noise sources is studied with a combination of field experiments and numerical simulations. Acoustic propagation models are developed and adapted for refractive index depending upon meteorological conditions. A high-resolution multi-nested environmental forecasting model forced by coarse global analysis is applied to predict real meteorological profiles at fine scales. These profiles are then used as input for the acoustic models. Numerical methods for producing higher resolution acoustic refractive index fields are proposed. These include spatial and temporal nested meteorological simulations with vertical grid refinement. It is shown that vertical nesting can improve the prediction of finer structures in near-ground temperature and velocity profiles, such as morning temperature inversions and low level jet-like features. Accurate representation of these features is shown to be important for modeling sound refraction phenomena and for enabling accurate noise assessment. Comparisons are made using the acoustic model for predictions with profiles derived from meteorological simulations and from field experiment observations in Phoenix, Arizona. The challenges faced in simulating accurate meteorological profiles at high resolution for sound propagation applications are highlighted and areas for possible improvement are discussed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
ContributorsShaffer, Stephen R. (Author) / Moustaoui, Mohamed (Thesis advisor) / Mahalov, Alex (Committee member) / Fernando, Harindra J.S. (Committee member) / Ovenden, Nicholas C. (Committee member) / Huang, Huei-Ping (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2014
Description
The tools developed for the use of investigating dynamical systems have provided critical understanding to a wide range of physical phenomena. Here these tools are used to gain further insight into scalar transport, and how it is affected by mixing. The aim of this research is to investigate the efficiency of several different partitioning methods which demarcate flow fields into dynamically distinct regions, and the correlation of finite-time statistics from the advection-diffusion equation to these regions.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
ContributorsWalker, Phillip (Author) / Tang, Wenbo (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
This thesis focuses on an improved understanding of the dynamics at different length scales of wind farms in an atmospheric boundary layer (ABL) using a series of visualization studies and Fourier, wavelet based spectral analysis using high fidelity large eddy simulation (LES). For this purpose, a robust LES based neutral ABL model at very high Reynolds number has been developed using a high order spectral element method which has been validated against the previous literature. This ABL methodology has been used as a building block to drive large wind turbine arrays or wind farms residing inside the boundary layer as documented in the subsequent work. Studies conducted in the thesis involving massive periodic wind farms with neutral ABL have indicated towards the presence of large scale coherent structures that contribute to the power generated by the wind turbines via downdraft mechanisms which are also responsible for the modulation of near wall dynamics. This key idea about the modulation of large scales have seen a lot of promise in the application of flow past vertically staggered wind farms with turbines at different scales. Eventually, studies involving wind farms have been progressively evolved in a framework of inflow-outflow where the turbulent inflow is being fed from the precursor ABL using a spectral interpolation technique. This methodology has been used to enhance the understanding related to the multiscale physics of wind farm ABL interaction, where phenomenon like the growth of the inner layer, and wake impingement effects in the subsequent rows of wind turbines are important owing to the streamwise heterogeneity of the flow. Finally, the presence of realistic geophysical effects in the turbulent inflow have been investigated that influence the flow past the wind turbine arrays. Some of the geophysical effects that have been considered include the presence of the Coriolis forces as well as the temporal variation of mean wind magnitude and direction that might occur due to mesoscale dynamics. This study has been compared against field experimental results which provides an important step towards understanding the capability of the mean data driven LES methodology in predicting realistic flow structures.
ContributorsChatterjee, Tanmoy (Author) / Peet, Yulia T. (Thesis advisor) / Adrian, Ronald J. (Committee member) / Calhoun, Ronald J. (Committee member) / Huang, Huei-Ping (Committee member) / Moustaoui, Mohamed (Committee member) / Arizona State University (Publisher)
Created2018
Description
Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch-
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
ContributorsWang, Meng, Ph.D (Author) / Kamarianakis, Yiannis (Thesis advisor) / Georgescu, Matei (Thesis advisor) / Fotheringham, A. Stewart (Committee member) / Moustaoui, Mohamed (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Earth-system models describe the interacting components of the climate system and
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
ContributorsDurazo, Juan, Ph.D (Author) / Kostelich, Eric J. (Thesis advisor) / Mahalov, Alex (Thesis advisor) / Tang, Wenbo (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
This research compares shifts in a SuperSpec titanium nitride (TiN) kinetic inductance detector's (KID's) resonant frequency with accepted models for other KIDs. SuperSpec, which is being developed at the University of Colorado Boulder, is an on-chip spectrometer designed with a multiplexed readout with multiple KIDs that is set up for a broadband transmission of these measurements. It is useful for detecting radiation in the mm and sub mm wavelengths which is significant since absorption and reemission of photons by dust causes radiation from distant objects to reach us in infrared and far-infrared bands. In preparation for testing, our team installed stages designed previously by Paul Abers and his group into our cryostat and designed and installed other parts necessary for the cryostat to be able to test devices on the 250 mK stage. This work included the design and construction of additional parts, a new setup for the wiring in the cryostat, the assembly, testing, and installation of several stainless steel coaxial cables for the measurements through the devices, and other cryogenic and low pressure considerations. The SuperSpec KID was successfully tested on this 250 mK stage thus confirming that the new setup is functional. Our results are in agreement with existing models which suggest that the breaking of cooper pairs in the detector's superconductor which occurs in response to temperature, optical load, and readout power will decrease the resonant frequencies. A negative linear relationship in our results appears, as expected, since the parameters are varied only slightly so that a linear approximation is appropriate. We compared the rate at which the resonant frequency responded to temperature and found it to be close to the expected value.
ContributorsDiaz, Heriberto Chacon (Author) / Mauskopf, Philip (Thesis director) / McCartney, Martha (Committee member) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Supernovae are vital to supplying necessary elements to forming bodies in our solar systems. This project studies the creation of a subset of these necessary elements, called short-lived radionuclides (SLRs). SLRs are isotopes with relatively short half-lives and can serve as heat sources for forming planetary bodies, and their traces can be used to date stellar events. Computational models of asymmetric supernovae provide opportunities to study the effect of explosion geometry on the SLR yields. We are most interested in the production of \iso{Al}{26}, \iso{Fe}{60}, and \iso{Ca}{41}, whose decayed products are found in our own solar system. To study the effect of explosion asymmetries in supernovae, we use TYCHO stellar evolution code, SNSHP smooth particle hydrodynamics code for 3D explosion simulations, Burn code for nucleosythesis post-processing, and Python code written to analyze the output of the post-processing code.
ContributorsJohnson, Charlotte (Author) / Young, Patrick (Thesis director) / Lunardini, Cecilia (Committee member) / Department of Physics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
About one in ten refugees from the American Revolution was African-descended, and unlike many white Loyalists fleeing war in the thirteen mainland North American colonies, black Loyalists were people without a country. Most were fleeing slavery in Virginia or the Carolinas, yet not fully able to claim to be British subjects, despite many heeding the call to join British forces. Among the 40,000 Loyalists who departed, around 3,500 black Loyalists evacuated from the newly founded United States between the years of 1776 and 1785. I hope to evaluate the movement patterns and thought process behind this particular group with what choices they ultimately had after the war using Dunmore’s Proclamation as a means to freedom. These black Loyalists faced the difficult decision in choosing what identity they would side with once they left. These former slaves ultimately had to choose between becoming forced migrants with the losing side of the war or staying with the winning side of the war as people bound by chains. Although there were a multitude of fascinating tales that could be told through the lens of these black Loyalists, one particular family caught my eye within my research. This story is the journey of the Fortune family who chose to run away from American slavery to migrate to Nova Scotia. Their story will grant me access to analyze the extreme discrimination families met as they fled, the contempt the new colonies felt against them, as well as the evolution of their societal roles as some of these immigrants integrated into their new country and became accepted as respected individuals. Furthermore, their tale aided me in understanding what caused some emigrant black Loyalists to stay in Nova Scotia despite the hardships they faced as outsiders who were unwelcome from the perspective of native white Nova Scotians.
ContributorsNanez-Krause, Michael L (Author) / Schermerhorn, Calvin J. (Thesis director) / Barnes, Andrew (Committee member) / Historical, Philosophical & Religious Studies (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
As of 2019, 30 US states have adopted abortion-specific informed consent laws that require state health departments to develop and disseminate written informational materials to patients seeking an abortion. Abortion is the only medical procedure for which states dictate the content of informed consent counseling. State abortion counseling materials have been criticized for containing inaccurate and misleading information, but overall, informed consent laws for abortion do not often receive national attention. The objective of this project was to determine the importance of informed consent laws to achieving the larger goal of dismantling the right to abortion. I found that informed consent counseling materials in most states contain a full timeline of fetal development, along with information about the risks of abortion, the risks of childbirth, and alternatives to abortion. In addition, informed consent laws for abortion are based on model legislation called the “Women’s Right to Know Act” developed by Americans United for Life (AUL). AUL calls itself the legal architect of the pro-life movement and works to pass laws at the state level that incrementally restrict abortion access so that it gradually becomes more difficult to exercise the right to abortion established by Roe v. Wade. The “Women’s Right to Know Act” is part of a larger package of model legislation called the “Women’s Protection Project,” a cluster of laws that place restrictions on abortion providers, purportedly to protect women, but actually to decrease abortion access. “Women’s Right to Know” counseling laws do not directly deny access to abortion, but they do reinforce key ideas important to the anti-abortion movement, like the concept of fetal personhood, distrust in medical professionals, the belief that pregnant people cannot be fully autonomous individuals, and the belief that abortion is not an ordinary medical procedure and requires special government oversight. “Women’s Right to Know” laws use the language of informed consent and the purported goal of protecting women to legitimize those ideas, and in doing so, they significantly undermine the right to abortion. The threat to abortion rights posed by laws like the “Women’s Right to Know” laws indicates the need to reevaluate and strengthen our ethical defense of the right to abortion.
ContributorsVenkatraman, Richa (Author) / Maienschein, Jane (Thesis director) / Brian, Jennifer (Thesis director) / Abboud, Carolina (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
We hypothesized that recurrent exposure to a temporal discounting task would habitize participants, so that they become insensitive to framing effects. Temporal discounting is a behavioral trend which describes how people discount the value of a reward dependent on the time until receipt. Participants completed a temporal discounting task weekly for five weeks, to promote formation of a habitual decision strategy. Concomitant with this, we expected that people would shift their decision process from a deliberate, goal-oriented approach that is sensitive to changes in reward outcomes and environmental context, to a simplified, automatic approach that minimizes cognitive effort. We expected that this shift in decision strategy would be evident in a reduced influence of contextual effects on choice outcomes. We tested this hypothesis by leveraging two framing effects \u2014 the date/delay effect and the decimal effect. Consistent with our hypothesis, we find that the date/delay effect is significant on week 1, shows significant changes in week 1 to week 5, and is no longer significant on week 5. The results for the decimal effects were not significant. We discuss these results with respect to the cognitive processes that underlie temporal discounting and self-control.
ContributorsSt Amand, Jesse Dean (Author) / McClure, Samuel (Thesis director) / Sanabria, Federico (Committee member) / School of Molecular Sciences (Contributor) / Department of Physics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05