Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v7.i6.70
pages 577-594

Inverse Shallow-Water Flow Modeling Using Model Reduction

Muhammad Umer Altaf
Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
Arnold W. Heemink
Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
Martin Verlaan
Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

Краткое описание

The idea presented in this paper is variational data assimilation based on model reduction using proper orthogonal decomposition. An ensemble of forward model simulations is used to determine the approximation of the covariance matrix of the model variability, and only the dominant eigenvectors of this matrix are used to define a model subspace. An approximate linear reduced model is obtained by projecting the original model onto this reduced subspace. Compared to the classical variational method, the adjoint of the tangent linear model is replaced by the adjoint of a linear reduced forward model. Thus, it does not require the implementation of the adjoint of the tangent linear model. The minimization process is carried out in reduced subspace and hence reduces the computational cost. Twin experiments using an operational storm surge prediction model in the Netherlands, the Dutch Continental Shelf Model are performed to estimate the water depth, with the findings that the approach with relatively little computational cost and without the burden of implementation of the adjoint model can be used in variational data assimilation.

ЛИТЕРАТУРА

  1. Ten-Brummelhuis, P. G. J., Heemink, A.W., and van den Boogard, H. F. P., Identification of shallow sea models. DOI: 10.1002/fld.1650170802

  2. Lardner, R.W., Al-Rabeh, A. H., and Gunay, N., Optimal Estimation of Parameters for a Two Dimensional Hydrodynamical Model of the Arabian Gulf. DOI: 10.1029/93JC01411

  3. Ulman, D. S., andWilson, R. E., Model Parameter Estimation for Data Assimilation Modeling: Temporal and Spatial Variability of the Bottom Drag Coefficient. DOI: 10.1029/97JC03178

  4. Heemink, A. W., Mouthaan, E. E. A., and Roest, M. R. T., Inverse 3D Shallow-Water Flow Modeling of the Continental Shelf. DOI: 10.1016/S0278-4343(01)00071-1

  5. Kaminski, T., Giering, R., and Scholze, M., An Example of an Automatic Differentiationbased Modeling System. DOI: 10.1007/3-540-44843-8_11

  6. Antoulas, A. C., Approximation of Large-Scale Dynamical Systems.

  7. Pearson, K., On Lines and Planes of Closest Fit to Points in Space. DOI: 10.1080/14786440109462720

  8. Kepler, G. M., Tran, H. T., and Banks, H. T., Reduced-Order Compensator Control of Species Transport in CVD Reactor.

  9. Prabhu, R. D., Scott, C. S., and Changly, Y., The Influence of Control on Proper Orthogonal Decomposition of Wall-Bounded Turbulent Flows. DOI: 10.1063/1.1333038

  10. Alfonsi, G., Restanob, C., and Primaveral, L., Coherent Structures of the Flow around a Surface-Mounted Cubic Obstacle in Turbulent Channel Flow. DOI: 10.1016/S0167-6105(02)00429-4

  11. Cao, Y., Zhu, J., Luo, Z., and Navon, I. M., Reduced Order Modeling of the Upper Tropical Pacific Ocean Model Using Proper Orthogonal Decomposition. DOI: 10.1016/j.camwa.2006.11.012

  12. Gunzburger, M. D., Reduced-Order Modeling, Data Compression and the Design of Experiments.

  13. Le Dimet, F. X., and Talagrand, O., Variational Algorithms for Analysis and Assimilation of Meteorological Observations: Theoratical Aspects. DOI: 10.1111/j.1600-0870.1986.tb00459.x

  14. Lawless, A. S., Nichols, N. C., Boess, C., and Bunse-Gerstner, A., Using Model Reduction Methods within Incremental 4DVAR. DOI: 10.1175/2007MWR2103.1

  15. Daescu, D. N., and Navon, I. M., A Dual Weighted Approach to Order Reduction in 4DVAR Data Assimilation. DOI: 10.1175/2007MWR2102.1

  16. Fang, F., Pain, C. C., Navon, I. M., Piggott, D., Gorman, G. J., Farrell, P. E., Allison, P. A., and Goddard, A. J. H., Reduced order modeling of an adaptive mesh ocean model. DOI: 10.1002/fld.1841

  17. Fang, F., Pain, C. C., Navon, I. M., Piggott, D., Gorman, G. J., Allison, P. A., and Goddard, A. J. H., A POD Reduced-Order Unstructured Mesh Ocean Modelling Method for Moderate Reynolds Number Flows. DOI: 10.1016/j.ocemod.2008.12.006

  18. Delay, F., Buoro, A., and de Marsily, G., Empirical Orthogonal Functions Analysis Applied to the Inverse Problem in Hydrogeology: Evaluation of Uncertainty and Simulation of New Solutions. DOI: 10.1023/A:1012298023051

  19. Vermeulen, P. T. M., Heemink, A. W., and Valstar, J. R., Inverse Modeling of Groundwater Flow Using Model Reduction. DOI: 10.1029/2004WR003698

  20. Vermeulen, P. T. M., and Heemink, A. W., Model-Reduced Variational Data Assimilation. DOI: 10.1175/MWR3209.1

  21. Courant, R., and Hilbert, D., Methods of Mathematical Physics.

  22. Sirovich, L., Choatic Dynamics of Coherent Structures. DOI: 10.1016/0167-2789(89)90123-1

  23. Cao, Y., Zhu, J., Navon, I. M., and Luo, Z., A Reduced-Order Approach to Fourdimensional Variational Data Assimilation Using Proper Orthogonal Decomposition. DOI: 10.1002/fld.1365

  24. Leendertse, J., Aspects of a Computational Model for Long-Period Water Wave Propagation.

  25. Stelling, G. S., On the Construction of Computational Methods for ShallowWater Flow Problem.

  26. Verboom, G. K., de Ronde, J. G., and van Dijk, R. P., A Fine Grid Tidal Flow and Storm Surge Model of the North Sea. DOI: 10.1016/0278-4343(92)90030-N

  27. Mouthaan, E., Heemink, A. W., and Robaczewska, K., Assimilation of ERS-1 Altimeter Data in a Tidal Model of the Continental Shelf. DOI: 10.1007/BF02226308

  28. Verlaan, M., Zijderveld, A., Vries, H., and Kroos, J., Operational Storm Surge Forcasting in the Netherlands: Developments in Last Decade. DOI: 10.1098/rsta.2005.1578

  29. Verlaan, M., Mouthaan, E., Kuijper, E., and Philippart, M., Parameter Estimation Tools for Shallow Water Flow Models.

  30. Verlaan, M., Efficient Kalman Filtering Algorithms for Hydrodynamic Models.

  31. Ten-Brummelhuis, P. G. J., Parameter Estimation in Tidal Flow Models with Uncertain Boundary Conditions.

  32. Velzen, N., and Verlaan, M., Costa a Problem Solving Environment for Data Assimilation Applied for Hydrodynamical Modeling.


Articles with similar content:

Identification Card Authentication System Based on Watermarking Technique
Telecommunications and Radio Engineering, Vol.67, 2008, issue 20
Mariko Nakano-Miyatake, Hector Manuel Perez-Meana
OPTIMIZATION-BASED SAMPLING IN ENSEMBLE KALMAN FILTERING
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 4
Alexander Bibov, Heikki Haario, Antti Solonen, Johnathan M. Bardsley
Approximate Algorithms for Estimating Trends in Financial Intelligence Tasks. Part II. Instants of Origination of Elements of Financial Flow are Unknown
Journal of Automation and Information Sciences, Vol.50, 2018, issue 2
Farit F. Idrisov
HESSIAN-BASED SAMPLING FOR HIGH-DIMENSIONAL MODEL REDUCTION
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 2
Omar Ghattas, Peng Chen
Synthesis of Robust Optimal Adaptive Control Systems for Nonstationary Objects under Bounded Disturbances
Journal of Automation and Information Sciences, Vol.36, 2004, issue 3
Vsevolod M. Kuntsevich