Qing Zhang

Conceptual simplifications for long-term sediment transport simulations

– Application to Iffezheim reservoir, Germany


Front	Back

ISBN:

978-3-8440-6335-6

Series:

Heftreihe des Instituts für Bauingenieurwesen / Book Series of the Department of Civil Engineering, Technische Universität Berlin
Herausgeber: Prof. Dr.-Ing. Matthias Barjenbruch, Prof. Dr.-Ing. Karsten Geißler, Prof. Dr.-Ing. Reinhard Hinkelmann, Prof. Dr.-Ing. Wolfgang Huhnt, Prof. Dr.-Ing. Yuri Petryna, Prof. Dr.-Ing. Frank Rackwitz, Prof. Dr. sc. techn. Mike Schlaich, Prof. Dr.-Ing. Volker Schmid, Prof. Dr.-Ing. Matthias Sundermeier and Prof. Dr.-Ing. Frank U. Vogdt
Berlin

Volume:

Keywords:

sediment transport; 3D numerical simulation; time serie analysis and -synthesis; artificial neural networks; Iffezheim reservoir

Type of publication:

Thesis

Language:

English

Pages:

196 pages

Figures:

57 figures

Weight:

285 g

Format:

24 x 17 cm

Bindung:

Paperback

Price:

48,80 € / 61,10 SFr

Published:

December 2018

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Abstract:

The aim of this thesis is to assess the long-term evolution of the fine sediment budget in the Iffezheim reservoir, which is the last barrage of the impounded section of the Rhine River. Based on the observed flow and suspended sediment concentration as well as echo-sounding data, various methods were proposed to estimate long-term riverbed changes. The application of a high-resolution 3D model was one focus in order to capture the multi-dimensional flow effects upstream of the barrage as accurately as possible and to represent the local deposition and erosion in a realistic manner.

Since practicable computation time for long-term predictions using high-resolution 3D models has not yet been considered to be satisfactory with the current technique, methods for reducing the computational effort while maintaining similar accuracy were developed. First, the reduction of the high-resolution model was carried out by coarsening the grid in space and time and decreasing the number of sediment fractions. Secondly, an upscaling approach was developed, where long-term instationary simulations of riverbed volume changes were replaced by a series of precalculated stationary ones. In order to obtain projected boundary conditions of the numerical model, time-series analysis and synthesis and artificial neural networks were used.

Ultimately, a coupled concept was developed linking advantages of the above-mentioned approaches. It was possible to determine a reasonable prediction of the sediment volume changes in the study area for the future with a reasonable computation time. The results reveal that for the chosen climate scenario for the near future (2020-2049) 33% and for the far future (2070-2099) 63% more sediment volume changes, mainly deposition, than currently are expected.