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Assessment of some numerical methods for estimating the parameters of the one‑dimensional advection–dispersion model

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Języki publikacji
EN
Abstrakty
EN
This study appraised optimisations of numerical solutions of the one-dimensional advection–dispersion model (AD-Model) to synthetic data generated using an analytical solution. The motivation for the work was to identify reliable methods for estimating stream solute transport parameters from observed events in small rivers. Numerical solutions of the AD-Model must contend with several effects that might disturb the solution, with the introduction of numerical diffusion and numerical dispersion being particularly important issues. This poses a problem if physical dispersion is being identified by optimising model coefficients using observations of solute transport from field experiments. The discretisation schemes used were the Backward-Time/Centred-Space, Crank–Nicolson, Implicit QUICK, MacCormack and QUICKEST methods. Optimisations were obtained for several grid resolutions by keeping the time step constant whilst varying the space step: the range of Peclet number, Pe, was 1.5–12.0. Generally, increasing the space step led to poorer estimated coefficients and poorer fits to the synthetic concentration profiles. For Pe < 5 only Crank–Nicolson, MacCormack and QUICKEST gave reliable optimised dispersion coefficients: those from Backward-Time/Centred-Space and Implicit QUICK being significantly underestimated. For Pe > 5 Crank–Nicolson and MacCormack gave slightly overestimated dispersion coefficients whilst the other methods gave significantly underestimated dispersion coefficients. These findings were generally consistent with the known presence of numerical diffusion and numerical dispersion in the methods.
Czasopismo
Rocznik
Strony
999--1016
Opis fizyczny
Bibliogr. 43 poz.
Twórcy
  • Department of Civil Engineering, Stellenbosch University, Stellenbosch, Cape Town, South Africa
  • Department of Civil Engineering, Stellenbosch University, Stellenbosch, Cape Town, South Africa
  • School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
Bibliografia
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  • 39. Wallis SG (2007) The numerical solution of the advection–dispersion equation: a review of some basic principles. Acta Geophys 55:85–94. https://doi.org/10.2478/s11600-006-0044-5
  • 40. Wallis SG, Manson JR (1997) Accurate numerical simulation of advection using large time steps. Int J Numer Methods Fluids 24:127–139
  • 41. Wallis SG, Manson JR (2004) Methods for predicting dispersion coefficients in rivers. Water Manag ICE 157:131–141. https://doi.org/10.1680/wama.2004.157.3.131
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  • 43. Wallis SG, Osuch M, Manson JR, Romanowicz R, Demars BOL (2013) On the estimation of solute transport parameters for rivers. In: Rowiński P (ed) Experimental and computational solutions of hydraulic problems. Springer, Berlin, pp 415–425. https://doi.org/10.1007/978-3-642-30209-1_30
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-efb44801-62fd-494b-a004-5aa87d422f71
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