Modeling Large Wood Transport in Semi-Congested Regime with Multiple Entry Points

Abstract

Wood transport during flood events can increase inundation risk and should be included in numerical models to estimate the associated residual risk. This paper presents the application of a fully Eulerian model that considers floating wood as a passive superficial pollutant through the adaptation of the advection–diffusion equation. A set of experiments is performed in a sinusoidal flume with a contraction to model semi-congested wood transport. The variation of the log release position replicates the possible variability of large wood entrainment during real events. The experiments are used to validate the numerical model, providing a comparison of the wood mass transport. Different release modes are also tested. The model predicts the position of the released logs and the overall transported mass, independently of the release position and modes, with an accuracy that varies along the flume length and across the flume axis. The analysis of the experimental and numerical transport velocity shows that modulation of the transport velocity is needed to ensure adequate model performances for semi-congested conditions.

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

“…Significant deviations became evident between computed orientation estimates and observed orientations along a heavily meandering stream section, while reliable and comprehensible orientation estimates resulted for straight stream sections. The gained results are of great relevance to validate and improve numerical models [65,74], and to identify an optimal location for LW retention and guiding structures [79] or other engineered instream structures (e.g., engineered log jams) in the course of river restoration projects [62]. Going forward, the introduced SmartWood-method requires further development in order to (i) generate continuous data of LW movement dynamics over longer periods of time; (ii) employ 9-DoF IMU-data that include sensor data from the magnetometer; to (iii) generate orientation estimates with regard to a global (earth) reference system, that will further improve the quality of quantitative data and analysis of LW movement dynamics at the field scale.…”

“…Using integration of angular velocities to estimate orientation is prone to drift over time, while sensor data from accelerometer solely return information about roll and pitch [52]. In addition, special emphasis should be put on the determination of the manually obtained orientation estimates in the field, which are intuitively determined with respect to the flow direction at the current location of the LW piece [72], rather than to the starting orientation [65].…”