On the morphodynamics of a wide class of large-scale meandering rivers: Insights gained by coupling LES with sediment-dynamics

In meandering rivers, interactions between flow, sediment transport, and bed topography affect diverse processes, including bedform development and channel migration. Predicting how these interactions affect the spatial patterns and magnitudes of bed deformation in meandering rivers is essential for various river engineering and geoscience problems. Computational fluid dynamics simulations can predict river morphodynamics at fine temporal and spatial scales but have traditionally been challenged by the large scale of natural rivers. We conducted coupled large-eddy simulation (LES) and bed morphodynamics simulations to create a unique database of hydro-morphodynamic datasets for 42 meandering rivers with a variety of planform shapes and large-scale geometrical features that mimic natural meanders. For each simulated river, the database includes (i) bed morphology, (ii) three-dimensional mean velocity field, and (iii) bed shear stress distribution under bankfull flow conditions. The calculated morphodynamics results at dynamic equilibrium revealed the formation of scour and deposition patterns near the outer and inner banks, respectively, while the location of point bars and scour regions around the apexes of the meander bends is found to vary as a function of the radius of curvature of the bends to the width ratio. A new mechanism is proposed that explains this seemingly paradoxical finding. The high-fidelity simulation results generated in this work provide researchers and scientists with a rich numerical database for morphodynamics and bed shear stress distributions in large-scale meandering rivers to enable systematic investigation of the underlying phenomena and support a range of river engineering applications

A Multilevel Finite Element Variational Multiscale Method for Incompressible Navier-Stokes Equations Based on Two Local Gauss Integrations

A multilevel finite element variational multiscale method is proposed and applied to the numerical simulation of incompressible Navier-Stokes equations. This method combines the finite element variational multiscale method based on two local Gauss integrations with the multilevel discretization using Newton correction on each step. The main idea of the multilevel finite element variational multiscale method is that the equations are first solved on a single coarse grid by finite element variational multiscale method; then finite element variational multiscale approximations are generated on a succession of refined grids by solving a linearized problem. Moreover, the stability analysis and error estimate of the multilevel finite element variational multiscale method are given. Finally, some numerical examples are presented to support the theoretical analysis and to check the efficiency of the proposed method. The results show that the multilevel finite element variational multiscale method is more efficient than the one-level finite element variational multiscale method, and for an appropriate choice of meshes, the multilevel finite element variational multiscale method is not only time-saving but also highly accurate

A data-driven machine learning approach for yaw control applications of wind farms

This study proposes a cost-effective machine-learning based model for predicting velocity and turbulence kinetic energy fields in the wake of wind turbines for yaw control applications. The model consists of an auto-encoder convolutional neural network (ACNN) trained to extract the features of turbine wakes using instantaneous data from large-eddy simulation (LES). The proposed framework is demonstrated by applying it to the Sandia National Laboratory Scaled Wind Farm Technology facility consisting of three 225 kW turbines. LES of this site is performed for different wind speeds and yaw angles to generate datasets for training and validating the proposed ACNN. It is shown that the ACNN accurately predicts turbine wake characteristics for cases with turbine yaw angle and wind speed that were not part of the training process. Specifically, the ACNN is shown to reproduce the wake redirection of the upstream turbine and the secondary wake steering of the downstream turbine accurately. Compared to the brute-force LES, the ACNN developed herein is shown to reduce the overall computational cost required to obtain the steady state first and second-order statistics of the wind farm by about 85%

Experimental Evaluation of Wicking Geotextile-Stabilized Aggregate Bases over Subgrade under Rainfall Simulation and Cyclic Loading

Wicking geotextile has been increasingly utilized in field projects to mitigate water-related roadway problems. The previous studies showed that the wicking geotextile could provide mechanical stabilization, serve as capillary barrier, and enhance lateral drainage. The wicking geotextile differentiates itself from non-wicking geotextiles by providing capillary or wicking drainage in unsaturated conditions, whereas non-wicking geotextiles only provide gravitational drainage under saturated or near-saturated conditions. Although the previous studies have demonstrated the benefits of soil water content reduction by the wicking drainage, it is not well understood how the wicking geotextile stabilization improves overall performance of aggregate bases over subgrade under traffic or cyclic loading. This paper presents an experimental study where large-scale cyclic plate loading tests were conducted under different conditions: (1) non-stabilized base, (2) non-wicking geotextile-stabilized base, and (3) wicking geotextile-stabilized base, over soft and moderate subgrades. Rainfall simulation was carried out for each test section. After each rainfall simulation, a drainage period was designed to allow water to drain from the section. The amounts of water applied and exiting from the test section were recorded and are compared. Cyclic loading was applied after each drainage period. The test results show that the combined hydraulic and mechanical stabilization effect by the wicking geotextile reduced the permanent deformation of the aggregate base over the subgrade as compared with the non-stabilized and non-wicking geotextile-stabilized sections

Correction to a Simple Biosphere Model 2 (SiB2) Simulation of Energy and Carbon Dioxide Fluxes over a Wheat Cropland in East China Using the Random Forest Model

Modeling the heat and carbon dioxide (CO2) exchanges in agroecosystems is critical for better understanding water and carbon cycling, improving crop production, and even mitigating climate change, in agricultural regions. While previous studies mainly focused on simulations of the energy and CO2 fluxes in agroecosystems on the North China Plain, their corrections, simulations and driving forces in East China are less understood. In this study, the dynamic variations of heat and CO2 fluxes were simulated by a standalone version of the Simple Biosphere 2 (SiB2) model and subsequently corrected using a Random Forest (RF) machine learning model, based on measurements from 1 January to 31 May 2015–2017 in eastern China. Through validation with direct measurements, it was found that the SiB2 model overestimated the sensible heat flux (H) and latent heat flux (LE), but underestimated soil heat flux (G0) and CO2 flux (Fc). Thus, the RF model was used to correct the results modeled by SiB2. The RF model showed that disturbances in temperature, net radiation, the G0 output of SiB2, and the Fc output of SiB2 were the key driving factors modulating the H, LE, G0, and Fc. The RF model performed well and significantly reduced the biases for H, LE, G0, and Fc simulated by SiB2, with higher R2 values of 0.99, 0.87, 0.75, and 0.71, respectively. The SiB2 and RF models combine physical mechanisms and mathematical correction to enable simulations with both physical meaning and accuracy

Evaluation of Moisture Reduction in Aggregate Base by Wicking Geotextile using Soil Column Tests

This study investigated the distance effect on water reduction by the wicking geotextile in a base course experimentally using three sets of soil column tests. In each set of tests, two soil columns were constructed by compacting well-graded aggregate over a non-wicking woven geotextile and a wicking geotextile. A portion of the geotextile specimen was extended outside of the soil column for evaporation. The changes of the water contents in the soil column were monitored by volumetric water content sensors installed at various depths. The experimental results indicate the capillary drainage by the wicking geotextile effectively reduced water content within the soil column up to a distance from the wicking geotextile (i.e., approximately 200 mm for this specific aggregate with 10% fines). The test results also show that the wicking geotextile could reduce more water content of the aggregate below its optimum water content at a faster rate than the non-wicking geotextile

Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES