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Flux Estimation (flux + estimation)
Selected Abstracts,The National Stream Quality Accounting Network: a flux-based approach to monitoring the water quality of large riversHYDROLOGICAL PROCESSES, Issue 7 2001Richard P. Hooper Abstract Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: the Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing. Published in 2001 by John Wiley & Sons, Ltd. [source] A robust air-gap flux estimation for speed sensorless vector control of double-star induction machineINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2004M.F. Mimouni Abstract The paper presents a new direct field-oriented control (DFOC) for double-star induction machine (DSIM) drives using the stator currents. First, we propose a new algorithm to estimate air-gap flux for speed sensorless air-gap flux orientation control. Compared to the previous DFOC schemes the new one is independent from any motor parameter variation, specially on the stator resistance. Then, the DFOC is associated with a low pass filter (LPF) to solve the dc drift problems caused by the pure integration of air-gap flux. In the present paper, the rotor resistance is estimated by an algorithm using Lyapunov theory. Good results have been obtained in the benchmark simulations. Copyright © 2004 John Wiley & Sons, Ltd. [source] Control of induction motors: an adaptive passivity MIMO perspectiveINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2003Manuel A. Duarte-Mermoud The design of two multiple-input multiple-output (MIMO) controllers for induction motors, based on adaptive passivity, is presented in this paper. The controller design method is based on concepts of equivalence passivity via adaptive feedback, previously developed by the authors. Robustness under variations of the motor-load parameters is guaranteed and the knowledge of such a parameters is not needed in the design. Simple proportional controllers for the torque, rotor flux and stator current control loops are used, due to the control simplification introduced by the use of feedback passive equivalence. A principle called ,Torque-Flux Control Principle' is used in this article introducing a considerable simplification in the resultant controller. Because of the employment of this principle, the control efforts are diminished and rotor flux estimation (or measurement) is avoided. Copyright © 2003 John Wiley & Sons, Ltd. [source] An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations,HYDROLOGICAL PROCESSES, Issue 17 2003Arthur J. Horowitz Abstract In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating-curve method were made using data from long-term, daily sediment-measuring sites within large (>1 000 000 km2), medium (<1 000 000 to >1000 km2), and small (<1000 km2) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors. The sediment rating-curve method tends to underpredict high, and overpredict low SSCs. As such, the range of errors associated with concomitant flux estimates for relatively short time-frames (e.g. daily, weekly) are likely to be substantially larger than those associated with longer time-frames (e.g. quarterly, annually) because the over- and underpredictions do not have sufficient time to balance each other. Hence, when error limits must be kept under ±20%, temporal resolution probably should be limited to quarterly or greater. The evaluations indicate that over periods of 20 or more years, errors of <1% can be achieved using a single sediment rating curve based on data spanning the entire period. However, somewhat better estimates for the entire period, and markedly better annual estimates within the period, can be obtained if individual annual sediment rating curves are used instead. Relatively accurate (errors <±20%) annual suspended sediment fluxes can be obtained from hydrologically based monthly measurements/samples. For 5-year periods or longer, similar results can be obtained from measurements/samples collected once every 2 months. In either case, hydrologically based sampling, as opposed to calendar-based sampling is likely to limit the magnitude of flux estimation errors. Published in 2003 John Wiley & Sons, Ltd. [source] | ||||||||||