Hybrid Simulation of Distributed Parameter Systems

Tzafestas, Spyros G.

doi:10.1016/b978-0-08-027624-3.50008-2

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Because we are using exponential smoothing as a simple and parsimonious (albeit suboptimal) alternative to Kalman filtering, we are not necessarily interested here in estimating the optimal ␣. [It would entail performing rigorous, and probably too difficult to be practical, estimations of space-and time-varying parameters, comparable to that for distributed-parameter Kalman filtering (see, e.g., Tzafestas 1978. )] Instead, we estimate in this work a suboptimal ␣ based on the parallel estimation strategy employed in the mean field bias correction procedure (Seo et al 1999).…”

Section: Estimation Proceduresmentioning

confidence: 99%

Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements

Seo¹,

Breidenbach²

2002

J. Hydrometeor

254

144

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A procedure for real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements is described. Developed to complement the existing gauge-based bias correction procedures used in the National Weather Service (NWS), the proposed procedure is a generalized local bias estimator that may be used under varying conditions of rain gauge network density and types of rainfall. To arrive at the procedure, the correction problem is formulated as a space-time estimation of radar and bin-averaged gauge rainfall from radar rainfall data and rain gauge measurements, respectively, at all hours up to and including the current hour. The estimation problem is then solved suboptimally via a variant of exponential smoothing. To evaluate the procedure, parameter estimation and true validation were performed using hourly radar-rainfall and rain gauge data from the Arkansas-Red Basin River Forecast Center (ABRFC) area. The results indicate that the proposed procedure is generally superior to mean field bias correction, and that the improvement is particularly significant in the cool season.

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Section: Estimation Proceduresmentioning

confidence: 99%

Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements

Seo¹,

Breidenbach²

2002

J. Hydrometeor

254

144

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“…Carlo methods [Tzafestas, 1982b]. In addition, finite element techniques [Carotenuto and Raiconi, 1982] and singular perturbation methods have also been employed [Van Harten.…”

Section: Introductionmentioning

confidence: 99%

Strategy for Advanced Sensing and Control of Combustion.

Nagurka¹,

Ramos²,

Sirignano³

1986

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Distributed parameter thermal system control and observation by Green–Galerkin methods

Alaeddine

Doumanidis

2004

Numerical Meth Engineering

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SUMMARYThis article addresses the problem of distributed parameter control and observation in thermal processing of materials. A novel numerical technique is developed for infinite-dimensional thermal conduction systems, based on Galerkin optimization of an energy index employing Green's functions. Various simulations are conducted to prove that despite the complexities that arise from the distributed parameter nature of the system, the proposed method successfully observes the temperature field that exists inside a solid body by employing strictly surface temperature measurements. Moreover, the existence of a duality principle between distributed parameter thermal control and thermal observation is also investigated.

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Hybrid Simulation of Distributed Parameter Systems

Cited by 5 publications

References 39 publications

Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements

Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements

Strategy for Advanced Sensing and Control of Combustion.

Distributed parameter thermal system control and observation by Green–Galerkin methods

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