Matching Items (635)
Description
This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence.
ContributorsChoukulkar, Aditya (Author) / Calhoun, Ronald (Thesis advisor) / Mahalov, Alex (Committee member) / Kostelich, Eric (Committee member) / Huang, Huei-Ping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
Consideration of both biological and human-use dynamics in coupled social-ecological systems is essential for the success of interventions such as marine reserves. As purely human institutions, marine reserves have no direct effects on ecological systems. Consequently, the success of a marine reserve depends on managers` ability to alter human behavior in the direction and magnitude that supports reserve objectives. Further, a marine reserve is just one component in a larger coupled social-ecological system. The social, economic, political, and biological landscape all determine the social acceptability of a reserve, conflicts that arise, how the reserve interacts with existing fisheries management, accuracy of reserve monitoring, and whether the reserve is ultimately able to meet conservation and fishery enhancement goals. Just as the social-ecological landscape is critical at all stages for marine reserve, from initial establishment to maintenance, the reserve in turn interacts with biological and human use dynamics beyond its borders. Those interactions can lead to the failure of a reserve to meet management goals, or compromise management goals outside the reserve. I use a bio-economic model of a fishery in a spatially patchy environment to demonstrate how the pre-reserve fisheries management strategy determines the pattern of fishing effort displacement once the reserve is established, and discuss the social, political, and biological consequences of different patterns for the reserve and the fishery. Using a stochastic bio-economic model, I demonstrate how biological and human use connectivity can confound the accurate detection of reserve effects by violating assumptions in the quasi-experimental framework. Finally, I examine data on recreational fishing site selection to investigate changes in response to the announcement of enforcement of a marine reserve in the Gulf of California, Mexico. I generate a scale of fines that would fully or partially protect the reserve, providing a data-driven way for managers to balance biological and socio-economic goals. I suggest that natural resource managers consider human use dynamics with the same frequency, rigor, and tools as they do biological stocks.
ContributorsFujitani, Marie (Author) / Abbott, Joshua (Thesis advisor) / Fenichel, Eli (Thesis advisor) / Gerber, Leah (Committee member) / Anderies, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
This is a study of the plight of smallholder agriculture in Northwest Costa Rica. More specifically, this is the story of 689 rice farms, of an average size of 7.2 hectares and totaling just less than 5,300 hectares within the largest agricultural irrigation system in Central America. I was able to define the physical bounds of this study quite clearly, but one would be mistaken to think that this simplicity transfers to a search for rural development solutions in this case. Those solutions lie in the national and international politics that appear to have allowed a select few to pick winners and losers in Costa Rican agriculture in the face of global changes. In this research, I found that water scarcity among smallholder farms between 2006 and 2013 was the product of the adaptations of other, more powerful actors in 2002 to threats of Costa Rica's ratification of the Central American Free Trade Agreement. I demonstrate how the adaptations of these more powerful actors produced new risks for others, and how this ultimately prevented the rural development program from meeting its development goals. I reflect on my case study to draw conclusions about the different ways risks may emerge in rural development programs of this type. Then, I focus on the household level and show that determinants of successful adaptation to one type of global change risk may make farmers more vulnerable to other types, creating a "catch-22" among vulnerable farmers adapting to multiple global change risks. Finally, I define adaptation limits in smallholder rice farming in Northwest Costa Rica. I show that the abandonment of livelihood security and well-being, and of the unique "parcelaro" identities of rice farmers in this region define adaptation limits in this context.
ContributorsWarner, Benjamin (Author) / Childers, Daniel (Thesis advisor) / Eakin, Hallie (Committee member) / Abbott, Joshua (Committee member) / Wiek, Arnim (Committee member) / Arizona State University (Publisher)
Created2014
Description
With the projected population growth, the need to produce higher agricultural yield to meet projected demand is hindered by water scarcity. Out of many the approaches that could be implemented to meet the water gap, intensification of agriculture through adoption of advanced agricultural irrigation techniques is the focus for this research. Current high water consumption by agricultural sector in Arizona is due to historical dominance in the state economy and established water rights. Efficiency gained in agricultural water use in Arizona has the most potential to reduce the overall water consumption. This research studies the agricultural sector and water management of several counties in Arizona (Maricopa, Pinal, and Yuma). Several research approaches are employed: modeling of agricultural technology adoption using replicator dynamics, interview with water managers and farmers, and Arizona water management law and history review. Using systems thinking, the components of the local farming environment are documented through socio-ecological system/robustness lenses. The replicator dynamics model is employed to evaluate possible conditions in which water efficient agricultural irrigation systems proliferate. The evaluation of conditions that promote the shift towards advanced irrigation technology is conducted through a combination of literature review, interview data, and model analysis. Systematic shift from the currently dominant flood irrigation toward a more water efficient irrigation technologies could be attributed to the followings: the increase in advanced irrigation technology yield efficiency; the reduction of advanced irrigation technology implementation and maintenance cost; the change in growing higher value crop; and the change in growing/harvesting time where there is less competition from other states. Insights learned will further the knowledge useful for this arid state's agricultural policy decision making that will both adhere to the water management goals and meet the projected food production and demand gap.
ContributorsBudiyanto, Yoshi (Author) / Muneepeerakul, Rachata (Thesis advisor) / Smith, Karen (Committee member) / Abbott, Joshua (Committee member) / Arizona State University (Publisher)
Created2014
Description
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
It is possible in a properly controlled environment, such as industrial metrology, to make significant headway into the non-industrial constraints on image-based position measurement using the techniques of image registration and achieve repeatable feature measurements on the order of 0.3% of a pixel, or about an order of magnitude improvement on conventional real-world performance. These measurements are then used as inputs for a model optimal, model agnostic, smoothing for calibration of a laser scribe and online tracking of velocimeter using video input. Using appropriate smooth interpolation to increase effective sample density can reduce uncertainty and improve estimates. Use of the proper negative offset of the template function has the result of creating a convolution with higher local curvature than either template of target function which allows improved center-finding. Using the Akaike Information Criterion with a smoothing spline function it is possible to perform a model-optimal smooth on scalar measurements without knowing the underlying model and to determine the function describing the uncertainty in that optimal smooth. An example of empiric derivation of the parameters for a rudimentary Kalman Filter from this is then provided, and tested. Using the techniques of Exploratory Data Analysis and the "Formulize" genetic algorithm tool to convert the spline models into more accessible analytic forms resulted in stable, properly generalized, KF with performance and simplicity that exceeds "textbook" implementations thereof. Validation of the measurement includes that, in analytic case, it led to arbitrary precision in measurement of feature; in reasonable test case using the methods proposed, a reasonable and consistent maximum error of around 0.3% the length of a pixel was achieved and in practice using pixels that were 700nm in size feature position was located to within ± 2 nm. Robust applicability is demonstrated by the measurement of indicator position for a King model 2-32-G-042 rotameter.
ContributorsMunroe, Michael R (Author) / Phelan, Patrick (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recognition of algae as a “Fit for Purpose” biomass and its potential as an energy and bio-product resource remains relatively obscure. This is due to the absence of tailored and unified production information necessary to overcome several barriers for commercial viability and environmental sustainability. The purpose of this research was to provide experimentally verifiable estimates for direct energy and water demand for the algal cultivation stage which yields algal biomass for biofuels and other bio-products. Algal biomass productivity was evaluated using different cultivation methods in conjunction with assessment for potential reduction in energy and water consumption for production of fuel and feed. Direct water and energy demands are the major focal sustainability metrics in hot and arid climates and are influenced by environmental and operational variables connected with selected algal cultivation technologies. Evaporation is a key component of direct water demand for algal cultivation and directly related to variations in temperature and relative humidity. Temperature control strategies relative to design and operational variables were necessary to mitigate overheating of the outdoor algae culture in panel photobioreactors and sub-optimal cultivation temperature in open pond raceways. Mixing in cultivation systems was a major component in direct energy demand that was provided by aeration in panel bioreactors and paddlewheels in open pond raceways. Management of aeration time to meet required biological interactions provides opportunities for reduced direct energy demand in panel photobioreactors. However, the potential for reduction in direct energy demand in raceway ponds is limited to hydraulics and head loss. Algal cultivation systems were reviewed for potential integration into dairy facilities in order to determine direct energy demand and nutrient requirements for algal biomass production for animal feed. The direct energy assessment was also evaluated for key components of related energy and design parameters for conventional raceway ponds and a gravity fed system. The results of this research provide a platform for selecting appropriate production scenarios with respect to resource use and to ensure a cost effective product with the least environmental burden.
ContributorsBadvipour, Shahrzad (Author) / Sommerfeld, Milton (Thesis advisor) / Downes, Meghan (Committee member) / Abbott, Joshua (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2015
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
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
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.
ContributorsKnutson, Brent (Author) / Tang, Wenbo (Thesis advisor) / Calhoun, Ronald (Committee member) / Huang, Huei-Ping (Committee member) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2018