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  • Shallow water equations  (2)
  • parameter identification  (2)
  • 1
    Electronic Resource
    Electronic Resource
    Identification of wind stress on shallow water surfaces by optimal smoothing (1990)
    Springer
    Stochastic environmental research and risk assessment 4 (1990), S. 105-119 
    Details
    ISSN: 1436-3259
    Keywords: Kalman filtering ; Optimal smoothing ; Shallow water equations ; Wind stress ; On-line prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Using the state space approach, an on-line filter procedure for combined wind stress identification and tidal flow forecasting is developed. The stochastic dynamic approach is based on the linear twodimensional shallow water equations. Using a finite difference scheme, a system representation of the model is obtained. To account for uncertainties, the system is embedded into a stochastic environment. By employing a Kalman filter, the on-line measurements of the water-level available can be used to identify and predict the shallow water flow. Because it takes a certain time before a fluctuation in the wind stress can be noticed in the water-level measurements, an optimal fixed-lag smoother is used to identify the stress.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543285
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Parameter identification in tidal models with uncertain boundary conditions (1990)
    Springer
    Stochastic environmental research and risk assessment 4 (1990), S. 193-208 
    Details
    ISSN: 1436-3259
    Keywords: Stochastic tidal modeling ; parameter identification ; model calibration
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract In this paper a parameter estimation algorithm is developed to estimate uncertain parameters in two dimensional shallow water flow models. Since in practice the open boundary conditions of these models are usually not known accurately, the uncertainty of these boundary conditions has to be taken into account to prevent that boundary errors are interpreted by the estimation procedure as parameter fluctuations. Therefore the open boundary conditions are embedded into a stochastic environment and a constant gain extended Kalman filter is employed to identify the state of the system. Defining a error functional that measures the differences between the filtered state of the system and the measurements, a quasi Newton method is employed to determine the minimum of this functional. To reduce the computational burden, the gradient of the criterium that is required using the quasi Newton method is determined by solving the adjoint system.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543083
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  • 3
    Electronic Resource
    Electronic Resource
    Parameter identification in particle models (1993)
    Springer
    Stochastic environmental research and risk assessment 7 (1993), S. 109-130 
    Details
    ISSN: 1436-3259
    Keywords: Particle models ; transport equations ; parameter identification ; adjoint modelling ; cost function ; gradient
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract For the simulation of the transport of dissolved matter particle models can be used. In this paper a technique is developed for the identification of uncertain parameters in these models. This model calibration is formulated as an optimization problem and is solved with a gradient based algorithm. Here adjoint particle tracks are used for the calculation of the gradient of the cost function. The performance of the calibration method is illustrated by simulations and an application to a river Rhine water quality calamity in November 1986.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01581420
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  • 4
    Electronic Resource
    Electronic Resource
    Data assimilation for non-linear tidal models (1990)
    Chichester : Wiley-Blackwell
    International Journal for Numerical Methods in Fluids 11 (1990), S. 1097-1112 
    Details
    ISSN: 0271-2091
    Keywords: Data assimilation ; Kalman filtering ; Shallow water equations ; Water level measurements On-line prediction ; Engineering ; Engineering General
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: A data assimilation procedure to incorporate measurements into a non-linear tidal model by using Kalman-filtering techniques is developed. The Kalman filter is based on the two-dimensional shallow water equations. To account for the inaccuracies, these equations are embedded into a stochastic environment by introducing a coloured system noise process into the momentum equations. The continuity equation is assumed to be perfect. The deterministic part of the equations is discretized using an ADI method, the stochastic part using the Euler scheme. Assuming that the system noise is less spatially variable than the underlying water wave process, this stochastic part can be approximated on a coarser grid than the grid used to approximate the deterministic part. A Chandrasekhar-type filter algorithm is employed to obtain the constant-gain extended Kalman filter for weakly non-linear systems. The capabilities of the filter are illustrated by applying it to the assimilation of water level measurements into a tidal model of the North Sea.
    Additional Material: 10 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/fld.1650110804
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