Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

In the last decade, spatially distributed hydrological models have rapidly advanced with the widespread availability of remotely sensed and geomatics information. Particularly, the areas of calibration and evaluation of spatially distributed hydrological models have been attempted in order to reduce the differences between models and improve realism through various techniques. Despite steady efforts, the study of calibrations and evaluations for spatially distributed hydrological models is still a largely unexplored field, in that there is no research in terms of the interactions of snow and water balance components with the traditional measurement methods as error functions. As one of the factors related to runoff, melting snow is important, especially in mountainous regions with heavy snowfall; however, no study considering both snow and water components simultaneously has investigated the procedures of calibration and evaluation for spatially distributed models. Additionally, novel approaches of error functions would be needed to reflect the characteristics of spatially distributed hydrological models in the comparison between simulated and observed values. Lastly, the shift from lumped model calibration to distributed model calibration has raised the model complexity. The number of unknown parameters can rapidly increase, depending on the degree of distribution. Therefore, a strategy is required to determine the optimal degree of model distributions for a study basin. In this study, we will attempt to address the issues raised above. This study utilizes the Research Distributed Hydrological Model (HL-RDHM) developed by Hydrologic Development Office of the National Weather Service (OHD-NWS). This model simultaneously simulates both snow and water balance components. It consists largely of two different modules, i.e., the Snow 17 as a snow component and the Sacramento Soil Moisture Accounting (SAC-SMA) as a water component, and is applied over the Durango River basin in Colorado, which is an area driven primarily by snow. As its main contribution, this research develops and tests various methods to calibrate and evaluate spatially distributed hydrological models with different, non-commensurate, variables and measurements. Additionally, this research provides guidance on the way to decide an appropriate degree of model distribution (resolution) for a specific water catchment.

The spatially distributed adaptive array is defined and analyzed. It is applied to both time division multiple access (TDMA) and code division multiple access (CDMA) cellular networks to improve the outage probability at either the base station or mobiles. In a TDMA network, the distributed array consists of three sub-arrays at alternate corners of a hexagonal cell. It is shown analytically that the SINR of combined beamforming of the distributed sub-arrays is greater than or equal to the SIR or independent beamforming of the sub-arrays. Closed form solutions are derived for estimating the BER performance of Rayleigh fading mobile signals received at a distributed adaptive array with combined beamforming of the sub-arrays. The simulated TDMA uplink outage probability of multiple same-cell co-channel users in a fading environment is compared between conventional, spatially distributed arrays with independent beamforming of the sub-arrays and combined beamforming of the sub-arrays. The effect of the antenna element spacing, number of elements and angular spread is also investigated. Spatially distributed arrays are formed in a CDMA network on the downlink with arrays in multi-way soft handoff with the mobiles. The outage probability performance of combined beamforming of the arrays in handoff is compared to independent beamforming of the arrays as well as to conventional sectorized antennas. The range between mobiles and distributed sub-arrays in the case of a spatially distribu-ted array can be larger than between conventional center cell arrays and mobiles. Therefore, the effect of interference on the range increase relative to an omni antenna of adaptive and phased arrays in a multipath environment for both narrowband and wideband spread spectrum systems is investigated. An analytical model for predicting the asymptotic range limitation of phased arrays when the angular spread exceeds the array beamwidth is derived. / Thesis (PhD (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

No description available.

The cross-sectional form of semi-alluvial bedrock channels was investigated. Channel geometry data were collected from a variety of streams in Ontario and Québec, Canada to develop empirical downstream scaling relationships. The relationships revealed that bedrock, mixed, and alluvial channels scale at similar rates with respect to discharge. The widest channels were formed in low-relief sedimentary bedrock with minimal alluvial cover. Channels influenced by resistant igneous/metamorphic bedrock produced a strong scaling relationship, whereas channels influenced by weak sedimentary bedrock produced a weak scaling relationship. Alluvial cover appeared to exhibit more control on channel width in low-relief settings in comparison to high-relief settings, with increased alluvial cover promoting channel narrowing. Channels influenced by igneous/metamorphic bedrock produced identifiable thalwegs, presumably due to well-defined bedload transport pathways. Channels influenced by sedimentary bedrock tended to have planar beds. Additionally, fish habitat was investigated at one semi-alluvial bedrock stream in Ontario, Canada. Fish sampling was conducted at proximate bedrock and alluvial sections followed by a survey of physical habitat parameters to evaluate habitat preferences. Adult logperch (Percina caprodes), juvenile white sucker (Catostomus commersonii), adult round goby (Neogobius melanostomus), and adult longnose dace (Rhinichthys cataractae) demonstrated preference toward alluvial substrate, whereas juvenile logperch and adult banded killifish (Fundulus diaphanus) demonstrated preference toward bedrock. Juvenile silver shiner (Notropis photogenis) and juvenile yellow perch (Perca flavescens) were indifferent to substrate type. Empirical depth and flow velocity habitat suitability indices (HSIs) were developed for each fish species. This study presents the first fish habitat suitability criteria developed from a small semi-alluvial bedrock stream and may provide valuable information for fisheries management endeavours in such environments.

<p>Combustion instability, a complex phenomenon observed in combustion chambers is due to the coupling between heat release and other unsteady flow processes. Combustion instability has long been a topic of interest to rocket scientists and has been extensively investigated experimentally and computationally. However, to date, there is no computational tool that can accurately predict the combustion instabilities in full-size combustors because of the amount of computational power required to perform a high-fidelity simulation of a multi-element chamber. Hence, the focus is shifted to reduced fidelity computational tools which may accurately predict the instability by using the information available from the high-fidelity simulations or experiments of single or few-element combustors. One way of developing reduced fidelity computational tools involves using a reduced fidelity solver together with the flame transfer functions that carry important information about the flame behavior from a high-fidelity simulation or experiment to a reduced fidelity simulation.</p> <p> </p> <p>To date, research has been focused mainly on premixed flames and using acoustic solvers together with the global flame transfer functions that were obtained by integrating over a region. However, in the case of rockets, the flame is non-premixed and distributed in space and time. Further, the mixing of propellants is impacted by the level of flow fluctuations and can lead to non-uniform mean properties and hence, there is a need for reduced fidelity solver that can capture the gas dynamics, nonlinearities and steep-fronted waves accurately. Nonlinear Euler equations have all the required capabilities and are at the bottom of the list in terms of the computational cost among the solvers that can solve for mean flow and allow multi-dimensional modeling of combustion instabilities. Hence, in the current work, nonlinear Euler solver together with the spatially distributed local flame transfer functions that capture the coupling between flame, acoustics, and hydrodynamics is explored.</p> <p> </p> <p>In this thesis, the approach to extract flame transfer functions from high-fidelity simulations and their integration with nonlinear Euler solver is presented. The dynamic mode decomposition (DMD) was used to extract spatially distributed flame transfer function (FTF) from high fidelity simulation of a single element non-premixed flame. Once extracted, the FTF was integrated with nonlinear Euler equations as a fluctuating source term of the energy equation. The time-averaged species destruction rates from the high-fidelity simulation were used as the mean source terms of the species equations. Following a variable gain approach, the local species destruction rates were modified to account for local cell constituents and maintain correct mean conditions at every time step of the nonlinear Euler simulation. The proposed reduced fidelity model was verified using a Rijke tube test case and to further assess the capabilities of the proposed model it was applied to a single element model rocket combustor, the Continuously Variable Resonance Combustor (CVRC), that exhibited self-excited combustion instabilities that are on the order of 10% of the mean pressure. The results showed that the proposed model could reproduce the unsteady behavior of the CVRC predicted by the high-fidelity simulation reasonably well. The effects of control parameters such as the number of modes included in the FTF, the number of sampling points used in the Fourier transform of the unsteady heat release, and mesh size are also studied. The reduced fidelity model could reproduce the limit cycle amplitude within a few percent of the mean pressure. The successful constraints on the model include good spatial resolution and FTF with all modes up to at least one dominant frequency higher than the frequencies of interest. Furthermore, the reduced fidelity model reproduced consistent mode shapes and linear growth rates that reasonably matched the experimental observations, although the apparent ability to match growth rates needs to be better understood. However, the presence of significant heat release near a pressure node of a higher harmonic mode was found to be an issue. This issue was rectified by expanding the pressure node of the higher frequency mode. Analysis of two-dimensional effects and coupling between the local pressure and heat release fluctuations showed that it may be necessary to use two dimensional spatially distributed local FTFs for accurate prediction of combustion instabilities in high energy devices such as rocket combustors. Hybrid RANS/LES-FTF simulation of the CVRC revealed that it might be necessary to use Flame Describing Function (FDF) to capture the growth of pressure fluctuations to limit cycle when Navier-Stokes solver is used.</p> <p> </p> <p>The main objectives of this thesis are:</p> <p>1. Extraction of spatially distributed local flame transfer function from the high fidelity simulation using dynamic mode decomposition and its integration with nonlinear Euler solver</p> <p>2. Verification of the proposed approach and its application to the Continuously Variable Resonance Combustor (CVRC).</p> <p>3. Sensitivity analysis of the reduced fidelity model to control parameters such as the number of modes included in the FTF, the number of sampling points used in the Fourier transform of the unsteady heat release, and mesh size.</p> <p> </p> <p>The goal of this thesis is to contribute towards a reduced fidelity computational tool which can accurately predict the combustion instabilities in practical systems using flame transfer functions, by providing a path way for reduced fidelity multi-element simulation, and by defining the limitations associated with using flame transfer functions and nonlinear Euler equations for non-premixed flames.</p> <p> </p><br>

Advanced monitoring of the spatio-temporal distribution of process parameters in large-scale vessels and containers such as stirred chemical or bioreactors offers a high potential for the investigation and further optimization of plants and embedded processes. This applies especially to large-scale fermentation biogas reactors where the process performance including the biological processes highly depend on mixing parameters of the complex bio-substrates. Sufficient mixing is a basic requirement for a stable operation of the process and adequate process performance. However, this condition is rarely met in agricultural biogas plants and the process efficiency is often reduced dramatically by inhomogeneities in the agitated vessels. Without a doupt, investigation and monitoring of biochemical parameters, such as the fermentation rate, pH distribution as well as O2 and CO2 concentration is of great importance. Nevertheless, also understanding of non-biological parameters, such as fluid dynamics (flow velocity profiles, circulation times), suspension mixing (homogeneity, location of dead zones and short-circuits) and heat transfer (temperature profiles), is necessary to analyze the impact of mixing on the biological system and also to improve the process efficiency. However, in most industrial scale applications the acquisition of these parameters and their spatial distributions in the large-scale vessels is hampered by the limited access to the process itself, because sensor mounting or cable connections are not feasible or desired. Therefore, state of the art instrumentation of such reactors is commonly limited to few spatial positions where it is doubtfully assumed that the measured parameters are representative for the whole reaction mixture. In this work, a concept of flow following sensor particles was developed. The sensor particles allow long-term measurement of spatially distributed process parameters in the chemically and mechanically harsh environments of agitated industrial vessels. Each sensor particle comprises of an onboard measurement electronics that logs the signals of measurement devices, namely temperature, absolute pressure (immersion depth, axial position) and 3D acceleration. The whole electronics is enclosed in a robust neutrally buoyant capsule (equivalent diameter 58.2 mm; sphericity 0.91), to allow free movement with the flow. The sensor particles were tested in pilot fermenters under comparable flow conditions of biogas fermenters. The experiments proved the applicability of the sensor particles and the robustness to resist the harsh environments of mixing processes. Moreover, the results show the capabilities of the sensor particles to monitor the internal conditions of the vessel correctly and thus deliver significant information about the flow regime. Therefore effects of liquid rheology, vessel geometry, impeller speed and axial impeller position on the macro-mixing process were properly detected. Evaluation of the impeller efficiency and the mixing processes was done based on mixing homogeneity, location of dead zones, axial velocity profiles, circulation time distributions as well as average circulation times, acceleration spectra and temperature profiles that were extracted from the measured data. Furthermore, it is shown, that parameters of mixing models such as circulation number, impeller head, PECLÉT-number and variance of suspended solid particles can be estimated from the measured data. The main achievement of this work is therefore the development and validation of instrumented flow followers for the investigation of macro-mixing effects in agitated vessels. The sensor particles show potential for employment to real applications such as biogas fermenters or large bioreactors and to monitor and improve the mixing and heating regimes.

Advanced monitoring of the spatio-temporal distribution of process parameters in large-scale vessels and containers such as stirred chemical or bioreactors offers a high potential for the investigation and further optimization of plants and embedded processes. This applies especially to large-scale fermentation biogas reactors where the process performance including the biological processes highly depend on mixing parameters of the complex bio-substrates. Sufficient mixing is a basic requirement for a stable operation of the process and adequate process performance. However, this condition is rarely met in agricultural biogas plants and the process efficiency is often reduced dramatically by inhomogeneities in the agitated vessels. Without a doupt, investigation and monitoring of biochemical parameters, such as the fermentation rate, pH distribution as well as O2 and CO2 concentration is of great importance. Nevertheless, also understanding of non-biological parameters, such as fluid dynamics (flow velocity profiles, circulation times), suspension mixing (homogeneity, location of dead zones and short-circuits) and heat transfer (temperature profiles), is necessary to analyze the impact of mixing on the biological system and also to improve the process efficiency. However, in most industrial scale applications the acquisition of these parameters and their spatial distributions in the large-scale vessels is hampered by the limited access to the process itself, because sensor mounting or cable connections are not feasible or desired. Therefore, state of the art instrumentation of such reactors is commonly limited to few spatial positions where it is doubtfully assumed that the measured parameters are representative for the whole reaction mixture. In this work, a concept of flow following sensor particles was developed. The sensor particles allow long-term measurement of spatially distributed process parameters in the chemically and mechanically harsh environments of agitated industrial vessels. Each sensor particle comprises of an onboard measurement electronics that logs the signals of measurement devices, namely temperature, absolute pressure (immersion depth, axial position) and 3D acceleration. The whole electronics is enclosed in a robust neutrally buoyant capsule (equivalent diameter 58.2 mm; sphericity 0.91), to allow free movement with the flow. The sensor particles were tested in pilot fermenters under comparable flow conditions of biogas fermenters. The experiments proved the applicability of the sensor particles and the robustness to resist the harsh environments of mixing processes. Moreover, the results show the capabilities of the sensor particles to monitor the internal conditions of the vessel correctly and thus deliver significant information about the flow regime. Therefore effects of liquid rheology, vessel geometry, impeller speed and axial impeller position on the macro-mixing process were properly detected. Evaluation of the impeller efficiency and the mixing processes was done based on mixing homogeneity, location of dead zones, axial velocity profiles, circulation time distributions as well as average circulation times, acceleration spectra and temperature profiles that were extracted from the measured data. Furthermore, it is shown, that parameters of mixing models such as circulation number, impeller head, PECLÉT-number and variance of suspended solid particles can be estimated from the measured data. The main achievement of this work is therefore the development and validation of instrumented flow followers for the investigation of macro-mixing effects in agitated vessels. The sensor particles show potential for employment to real applications such as biogas fermenters or large bioreactors and to monitor and improve the mixing and heating regimes.

<div><p><br></p></div><p>This study makes use of a Time-Resolved Raman Spectroscopy (TRRS) system developed in the Purdue Civil Engineering spectroscopy laboratory to advance technology critical to enable field deployment of Raman spectroscopic systems, with a primary focus on developing solutions to overcome two specific barriers to Raman analysis in the natural environment: (1) obtaining Raman spectra of chemical compounds at field-relevant concentrations, and (2) realizing economical spatial monitoring. To inform both streams of activity, this work first explores the role of component choice and apparatus design on Raman system output. A component-level Raman system transfer function is developed in terms of intensity, wavelength, and time which yields detailed insight into system performance that greatly exceeds traditional single “system factor” treatments of apparatus effects. The modelling frame provided by the transfer function is universally applicable in that it is inclusive of the majority of component choices that may be encountered in any open-path or closed-path Raman system, and is likely to be valuable in efforts to assess the performance benefits and limitations of system designs, modify or tailor apparatus layouts, facilitate experiment design, and compare results obtained on different systems. </p><p><br></p><p>The system characterization offered by the transfer function is then employed to develop a multi-photon counting algorithm realized through digital signal processing (DSP) which captures photon arrivals traditionally ignored in conventional counting methods. This approach increases acquired Raman intensity for any given analyte by using detector output voltage or a voltage-time product as an energy proxy – an approach that is likey broadly applicable to any spectroscopic techniques employing detectors that make use of the photoelectric effect. In experiments carried out on analytes (nitrate, isopropanol, and rhodamine 6G) in aqueous solutions, enhanced observations enabled by the multi-photon counting algorithm are shown to increase observed Raman intensities of low Raman-yield solutions 2.0-3.1-fold compared to single-threshold analysis, and also extend the upper observation limit of strong Raman-yield solutions that would traditionally saturate detectors using a binary photon counting scheme. Notably, the improved performance offered by the multi-photon counting algorithm is realized through comparison of multi-photon and conventional counting algorithms applied to the same data in a post-processing exercise, thus eliminating any effects of test-to-test variation on results, and highlighting the ability to employ the developed counting approach without modification of traditional systems.</p><p><br></p><p>Additional insights from the system transfer function are also used to inform exploration of a novel approach to enable spatial environmental monitoring via Raman spectroscopy by combining fiber optics, optical switch technology, and the Raman system prototype. Tests designed to evaluate the system configured as a multiplexed optically switched fiber optic network demonstrate the potential to deliver excitation and collect Raman scattering from different desired monitoring locations with a sole excitation source and a single detector over substantial distances. Using nitrate as an example compound of interest, it is demonstrated that the system has a detection limit of 5 ppm within approximately 1.5 meters, which increases to 15 ppm at 100 m, and 38 ppm at 200 m. Modelling informed using the developed system transfer function highlights that improving the prototype by eliminating fiber connectors and making use of commercially available visible-light optimized fiber can substantially extend the range of the system, offering a 15-ppm nitrate detection limit at 2100 m. As increases in laser power, testing time, and collection optic efficiency are all also straightforward and viable, the prototype demonstrates realistic potential to achieve field relevant detection sensitivity over great distance.</p><p><br></p><p>As a final demonstration of system potential, a set of experiments on aqueous nitrate solutions is performed to understand the influence of turbidity, fluorescence, optics size, and varied raw data integration lengths on Raman observations. Results demonstrate that cumulative advances in the TRRS system establish a new generation of Raman spectroscopic sensing amenable to long-term environmental monitoring over significant spatial extent in complex in-situ conditions. Specific advances made herein include enhanced power delivery and scattered light collection informed by the system transfer function, increases in sensitivity from multi-photon counting, and incorporation of optical multiplexing. Overall, the Time-Resolved Raman Spectroscopic System (TRRS) now offers a set of capabilities that bring in-field deployment within practical reach.</p>