Structures of icy flows in a river bend

Abstract:

Flow dynamics under ice coverage has not been well understood despite its significance in alluvial morphodynamics. In this study, three-dimensional flow structures are analyzed in a river bend during winter to understand the linkage between ice coverage and velocity profile distribution. A river reach of the Red River of the North in Fargo, North Dakota, United States is selected as the study site. Data monitoring is completed using Acoustic Doppler Current Profiler (ADCP) in both stationary and moving boat modes. Large Eddy Simulation (LES) is performed using ADCP data to provide detailed hydrodynamic data with the total grid points of 100 millions resolving flows in 1- kilometer ice-covered reach. We compare LES and ADCP to examine both the time-averaged profiles as well as turbulent statistics. Time-series analysis is carried out for ADCP data to analyze velocity profile distribution near the bend apex. Due to the difference in shear velocity of the bed and the ice surface, a double log-law is formed instead of the classical log-law in open channel flow. There exists three distinct layers: (1) ice-layer, (2) bed layer, and (3) the core flow. Distribution curves show that the location of maximum velocity is closer to the bed layer due to the non-negligible shear stress, which is caused by the ice layer at the top. The double log-law is modified following the suggestion (Guo et al., Journal of Hyd. Engr. 143.10, 2017) with two significant parameters: (1) flow condition (n), and (2) layer permeability (ß). It is found out that the minimum ß=2 value gives the best fit in most of the measurements; however, the optimal n value varies. Further investigation from LES data shows that turbulent flows interact strongly with both the top and bed surfaces, which leads to the variation in local flow condition (n). This work is supported by a start-up package of T.Le from North Dakota State University and North Dakota Water Resources Research Institute. We also acknowledge the use of computational resources at the Center for Computationally Assisted Science and Technology (CCAST)-NDSU and an allocation (CTS200012) from the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.