Der Einfluss von Kolloiden auf Wasserfluss und Stofftransport in Böden: Randaspekt oder Schlüsselprozess?

Drittmittelfinanzierte Einzelförderung

Details zum Projekt

Prof. Dr. Peter Knabner

Prof. Dr. Florian Frank
Dr. Alexander Prechtel

Beteiligte FAU-Organisationseinheiten:
Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Mittelgeber: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Projektstart: 01.11.2006
Projektende: 31.12.2009


Multicomponent reactive transport in natural porous media
Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Abstract (fachliche Beschreibung):

Soil colloids may influence the interaction between solutes and the
immobile solid phase. A coupling to the fluid transport is possible by
processes of sedimentation, flocculation, precipitation, filtration and
deposition. The objective of this research project is the qualitative
and quantitative examination of the crucial aspects of
colloidal-influenced solute- and fluid transport by means of systematic,
prognostic simulation. In detail,

  1. the attachment and detachment of colloids under consideration air-water interface of the soil,
  2. the transformation of the pore space and the thus induced coupling to the fluid transport in soil, and
  3. the transformation of the surface properties of the solid phase and the thus induced coupling to the solute transport

have to be analyzed. The main hypothesis of this project states that
the couplings incorporated in the model conception affect the
praxis-relevant situations not only qualitatively, but also
quantitatively in a significant way. The deterministic description of
the physicochemical mechanisms on basis of the conservation laws for
mass, impulse and energy results in systems of time-dependent non-linear
partial differential equations. In order to make the model operative
with respect to the problem formulation, one has to approximate it via
numerical methods and to implement those in a software tool. For each
level of complexity which has to be achieved, a comparison with existing
experimental data has to be accomplished. In particular, these datasets
have is to be used to obtain a realistic parametrization of the model
via inverse modelling.

Zuletzt aktualisiert 2019-09-04 um 11:17