A New Ionospheric Model (ANIMo) based upon the physics of production, loss, and vertical transport has been developed. The model is driven by estimates of neutral composition, temperature and solar flux and is applicable to the mid-latitude regions of the Earth under quiet and moderate geomagnetic conditions. This model was designed to exhibit specific features that were not easy to find all together in other existing ionospheric models. ANIMo needed to be simple to use and interact with, relatively accurate, reliable, robust and computationally efficient. The definition of these characteristics was mostly driven by the intention to use ANIMo in a Data Assimilation (DA) scheme. DA or data ingestion can be described as a technique where observations and model realizations, called background information, are combined together to achieve a level of accuracy that is higher than the accuracy of the two elements taken separately. In this project ANIMo was developed to provide a robust and reliable background contribution. The observations are given by the Global Positioning System (GPS) ionospheric measurements, collected from several networks of GPS ground-station receivers and are available on on-line repositories. The research benefits from the Multi-Instrument Data Analysis System (MIDAS) [Mitchell and Spencer, 2003; Spencer and Mitchell, 2007], which is an established ionospheric tomography software package that produces three dimensional reconstructions of the ionosphere starting from GPS measurements. Utilizing ANIMo in support of MIDAS has therefore the potential to generate a very stable set-up for monitoring and study the ionosphere. In particular, the model is expected to compensate some of the typical limitations of ionospheric tomography techniques described by Yeh and Raymund [1991] and Raymund et al. [1994]. These are associated with the lack of data due to the uneven distribution of ground-based receivers and limitations to viewing angles. Even in regions of good receiver coverage there is a need to compensate for information on the vertical profile of ionisation. MIDAS and other tomography techniques introduce regularization factors that can assure the achievement of a unique solution in the inversion operation. These issues could be solved by aiding the operation with external information provided by a physical model, like ANIMo, through a data ingestion scheme; this ensures that the contribution is completely independent and there is an effective accuracy improvement. Previously, the limitation in vertical resolution has been solved by applying vertical orthonormal functions based upon empirical models in different ways [Fougere, 1995; Fremouw et al., 1992; Sutton and Na, 1994]. The potential for the application of a physical model, such ANIMo is that it can provide this information according to the current ionospheric conditions. During the project period ANIMo has been developed and incorporated with MIDAS. The result is A New Ionospheric Data Assimilation System (ANIDAS); its name suggests that the system is the implementation of ANIMo in MIDAS. Because ANIDAS is a data ingestion scheme, it has the potential to be used to perform not only more accurate now-casting but also forecasting. The outcomes of ANIDAS at the current time can be used to initialise ANIMo for the next time step and therefore trigger another assimilation turn. In future, it is intended that ANIMo will form the basis to a new system to predict the electron density of the ionosphere – ionospheric forecasting.

Cropland management practices have traditionally focused on maximising the production of food, feed and fibre. However, croplands also provide valuable regulating ecosystem services, including carbon (C) storage in soil and biomass. Consequently, management impacts the extents to which croplands act as sources or sinks of atmospheric carbon dioxide (CO2). And so, reliable information on cropland ecosystem C fluxes and yields are essential for policy-makers concerned with climate change mitigation and food security. Eddy-covariance (EC) flux towers can provide observations of net ecosystem exchanges (NEE) of CO2 within croplands, however the tower sites are temporally and spatially sparse. Process-based crop models simulate the key biophysical mechanisms within cropland ecosystems, including the management impacts, crop cultivar, soil and climate on crop C dynamics. The models are therefore a powerful tool for diagnosing and forecasting C fluxes and yield. However, crop model spatial upscaling is often limited by input data (including meteorological drivers and management), parameter uncertainty and model complexity. Earth observation (EO) sensors can provide regular estimates of crop condition over large extents. Therefore, EO data can be used within data assimilation (DA) schemes to parameterise and constrain models. Research presented in this thesis explores the key challenges associated with crop model upscaling. First, fine-scale (20-50 m) EO-derived data, from optical and radar sensors, is assimilated into the Soil-Plant-Atmosphere crop (SPAc) model. Assimilating all EO data enhanced the simulation of daily C exchanges at multiple European crop sites. However, the individually assimilation of radar EO data (as opposed to combined with optical data) resulted in larger improvements in the C fluxes simulation. Second, the impacts of reduced model complexity and driver resolution on crop photosynthesis estimates are investigated. The simplified Aggregated Canopy Model (ACM) – estimating daily photosynthesis using coarse-scale (daily) drivers – was calibrated using the detailed SPAc model, which simulates leaf to canopy processes at half-hourly time-steps. The calibrated ACM photosynthesis had a high agreement with SPAc and local EC estimates. Third, a model-data fusion framework was evaluated for multi-annual and regional-scale estimation of UK wheat yields. Aggregated model yield estimates were negatively biased when compared to official statistics. Coarse-scale (1 km) EO data was also used to constrain the model simulation of canopy development, which was successful in reducing the biases in the yield estimates. And fourth, EO spatial and temporal resolution requirements for crop growth monitoring at UK field-scales was investigated. Errors due to spatial resolution are quantified by sampling aggregated fine scale EO data on a per-field basis; whereas temporal resolution error analysis involved re-sampling model estimates to mimic the observational frequencies of current EO sensors and likely cloud cover. A minimum EO spatial resolution of around 165 m is required to resolve the field-scale detail. Monitoring crop growth using EO sensors with a 26-day temporal resolution results in a mean error of 5%; however, accounting for likely cloud cover increases this error to 63%.

In the year 2000, croplands covered about 12% of the Earth’s ice-free land surface. Through cropland management, humankind momentarily appropriates about 25% of terrestrial ecosystem productivity. Not only are croplands a key element of human food supply, but also bear potential in increased carbon (C) uptake when best-practice land management approaches are adopted. A detailed assessment of the impact of land use on terrestrial ecosystems can be achieved by modelling, but the simulation of crop C cycling itself is a relatively new discipline. Observational data on crop net ecosystem exchange (NEE) are available only recently, and constitute an important tool for model development, diagnosis, and validation. Before crop functional types (CFT) had been introduced, however, large-scale biogeochemical models (BGCM) lacked crop-specific patterns of phenology, C allocation, and land management. As a consequence, the influence of cropland C cycling on biosphere-atmosphere C exchange seasonality and magnitude is currently poorly known. To date, no regional assessment of crop C cycling and yield formation exists that specifically accounts for spatially and temporally varying patterns of sowing dates within models. In this thesis, I present such an assessment for the first time. In the first step (chapter 2), I built a crop C mass balance model (SPAc) that models crop development and C allocation as a response to ambient meteorological conditions. I compared model outputs against C flux and stock observations of six different sites in Europe, and found a high degree of agreement between simulated and measured fluxes (R2 = 0.83). However, the model tended to overestimate leaf area index (LAI), and underestimate final yield. In a model comparison study (chapter 3), I found in cooperation with further researchers that SPAc best reproduces observed fluxes of C and water (owed to the model’s high temporal and process resolution), but is deficient due to a lack in simulating full crop rotations. I then conducted a detailed diagnosis of SPAc through the assimilation of C fluxes and biometry with the Ensemble Kalman Filter (EnKF, chapter 4), and identified potential model weaknesses in C allocation fractions and plant hydraulics. Further, an overestimation of plant respiration and seasonal leaf thickness variability were evident. Temporal parameter variability as a response to C flux data assimilation (DA) is indicative of ecosystem processes that are resolved in NEE data but are not captured by a model’s structure. Through DA, I gained important insights into model shortcomings in a quantitative way, and highlighted further needs for model improvement and future field studies. Finally, I developed a framework allowing for spatio-temporally resolved simulation of cropland C fluxes under observational constraints on land management and canopy greenness (chapter 5). MODIS (Moderate Resolution Imaging Spectroradiometer) data were assimilated both variationally (for sowing date estimation) and sequentially (for improved model state estimation, using the EnKF) into SPAc. In doing so, I was able to accurately quantify the multiannual (2000-2006) regional C flux and biometry seasonality of maize-soybean crop rotations surrounding the Bondville Ameriflux eddy covariance (EC) site, averaged over 104 pixel locations within the wider area. Results show that MODIS-derived sowing dates and the assimilation of LAI data allow for highly accurate simulations of growing season C cycling at locations for which groundtruth sowing dates are not available. Through quantification of the spatial variability in biometry, NEE, and net biome productivity (NBP), I found that regional patterns of land management are important drivers of agricultural C cycling and major sources of uncertainty if not appropriately accounted for. Observing C cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem behaviour. Here, I developed a framework that enables modellers to accurately simulate current (i.e. last 10 years) C cycling of major agricultural regions and their contribution to atmospheric CO2 variability. Follow-up studies can provide crucial insights into testing and validating large-scale applications of biogeochemical models.

This thesis presents a research work on the development of new methodology for near-real-time detection and localisation of pipe bursts in a Water Distribution System (WDS) at the District Meters Area (DMA) level. The methodology makes use of online hydraulic model coupled with a demand forecasting methodology and several statistical techniques to process the hydraulic meters data (i.e., flows and pressures) coming from the field at regular time intervals (i.e. every 15 minutes). Once the detection part of the methodology identifies a potential burst occurrence in a system it raises an alarm. This is followed by the application of the burst localisation methodology to approximately locate the event within the District Metered Area (DMA). The online hydraulic model is based on data assimilation methodology coupled with a short-term Water Demand Forecasting Model (WDFM) based on Multi-Linear Regression. Three data assimilation methods were tested in the thesis, namely the iterative Kalman Filter method, the Ensemble Kalman Filter method and the Particle Filter method. The iterative Kalman Filter (i-KF) method was eventually chosen for the online hydraulic model based on the best overall trade-off between water system state prediction accuracy and computational efficiency. The online hydraulic model created this way was coupled with the Statistical Process Control (SPC) technique and a newly developed burst detection metric based on the moving average residuals between the predicted and observed hydraulic states (flows/pressures). Two new SPC-based charts with associated generic set of control rules for analysing burst detection metric values over consecutive time steps were introduced to raise burst alarms in a reliable and timely fashion. The SPC rules and relevant thresholds were determined offline by performing appropriate statistical analysis of residuals. The above was followed by the development of the new methodology for online burst localisation. The methodology integrates the information on burst detection metric values obtained during the detection stage with the new sensitivity matrix developed offline and hydraulic model runs used to simulate potential bursts to identify the most likely burst location in the pipe network. A new data algorithm for estimating the ‘normal’ DMA demand and burst flow during the burst period is developed and used for localisation. A new data algorithm for statistical analysis of flow and pressure data was also developed and used to determine the approximate burst area by producing a list of top ten suspected burst location nodes. The above novel methodologies for burst detection and localisation were applied to two real-life District Metred Areas in the United Kingdom (UK) with artificially generated flow and pressure observations and assumed bursts. The results obtained this way show that the developed methodology detects pipe bursts in a reliable and timely fashion, provides good estimate of a burst flow and accurately approximately locates the burst within a DMA. In addition, the results obtained show the potential of the methodology described here for online burst detection and localisation in assisting Water Companies (WCs) to conserve water, save energy and money. It can also enhance the UK WCs’ profile customer satisfaction, improve operational efficiency and improve the OFWAT’s Service Incentive Mechanism (SIM) scores.

Orientador: Jorge Isaías Llagostera Beltran / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-16T04:44:44Z (GMT). No. of bitstreams: 1 Gonzaga_RodrigoReno_M.pdf: 2267084 bytes, checksum: 6521810ed190a172ab073db526a2bf37 (MD5) Previous issue date: 2010 / Resumo: Este trabalho é resultado do desenvolvimento de um programa computacional para representação termodinâmica de desempenho 'off-design' de ciclos de turbinas a gás para geração de potência elétrica, ou seja, desempenho fora das condições de projeto ou condições nominais. Foram utilizados mapas genéricos de desempenho de compressores e turbinas, gerados a partir de mapas disponíveis na literatura. Tais mapas foram normalizados e relativizados em relação às características de projeto, de tal forma que a metodologia utilizada permite abranger máquinas de todas as faixas operativas para razão de pressão, fluxo e velocidade de rotação. O modelo desenvolvido possibilita a geração de curvas confiáveis de desempenho de turbinas a gás a partir de poucos pontos conhecidos e, com isso, a estimativa de desempenho 'off-design' destas máquinas para um determinado ponto de operação escolhido dentro da região útil do mapa da curva. Permite ainda a automatização do processo de cálculo em condições fora de projeto, além dos procedimentos de cálculo serem úteis para o projeto de sistemas de controle de turbinas a gás / Abstract: In this work a computer program was developed for thermodynamic representation of gas turbine cycles off-design performance for electric power generation. Generic performance maps of compressors and turbines were generated using maps available in the literature. These ones were normalized in relation to design features, by the way that the methodology used allowed to cover engines of all operative ranges for pressure ratio, flow and rotational speed. The model developed makes possible the generation of reliable maps of gas turbine performance by a few points known and so, the estimate of off-design performance of these engines for a determined operation point chosen in map curve useful region. It allows yet the automatization of the off-desing calculation process, besides of the calculation procedures be useful to gas turbines control systems / Mestrado / Termica e Fluidos / Mestre em Engenharia Mecânica

Systems biology attempts to understand biological systems through the generation of predictive models that allow the behaviour of the system to be simulated in silico. Metabolic systems biology has in recent years focused upon the reconstruction and constraint-based analysis of genome-scale metabolic networks, which provide computational and mathematical representations of the known metabolic capabilities of a given organism. This thesis initially concerns itself with the development of such metabolic networks, first considering the community-driven development of consensus networks of the metabolic functions of Saccharomyces cerevisiae. This is followed by a consideration of automated approaches to network reconstruction that can be applied to facilitate what has, until recently, been an arduous manual process. The use of such large-scale networks in the generation of dynamic kinetic models is then considered. The development of such models is dependent upon the availability of experimentally determined parameters, from omics approaches such as transcriptomics, proteomics and metabolomics, and from kinetic assays. A discussion of the challenges faced with developing informatics infrastructure to support the acquisition, analysis and dissemination of quantitative proteomics and enzyme kinetics data follows, along with the introduction of novel software approaches to address these issues. The requirement for integrating experimental data with kinetic models is considered, along with approaches to construct, parameterise and simulate kinetic models from the network reconstructions and experimental data discussed previously. Finally, future requirements for metabolic systems biology informatics are considered, in the context of experimental data management, modelling infrastructure, and data integration required to bridge the gap between experimental and modelling approaches.