A 2D vertical （2DV） numerical model, without o-coordinate transformation in the vertical direction, is developed for the simulation of flow and sediment transport in open channels. In the model, time-averaged Reynol...A 2D vertical （2DV） numerical model, without o-coordinate transformation in the vertical direction, is developed for the simulation of flow and sediment transport in open channels. In the model, time-averaged Reynolds equations are closed by the k-e nonlinear turbulence model. The modified Youngs- VOF method is introduced to capture free surface dynamics, and the free surface slope is simulated using the ELVIRA method. Based on the power-law scheme, the k-e model and the suspended-load transport model are solved numerically with an implicit scheme applied in the vertical plane and an explicit scheme applied in the horizontal plane. Bedload transport is modeled using the Euler-WENO scheme, and the grid-closing skill is adopted to deal with the moving channel bed boundary. Verification of the model using laboratory data shows that the model is able to adequately simulate flow and sediment transport in open channels, and is a good starting point for the study of sediment transport dynamics in strong nonlinear flow scenarios.展开更多
Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （T...Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （TSVD）. An earth-rock dam in China is presented and discussed as an example. The analysis consists of three steps： multicollinearity detection, regularization pa- rameter selection, and crack opening modeling and forecasting. Generalized Cross-Validation （GCV） function and L-curve criterion are both adopted in the regularization parameter selection. Partial Least-Squares Regression （PLSR） and stepwise regression are also included for comparison. The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models. However, no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem. Both fitting accuracy and coefficients＇ reasonability should be considered when evaluating the mode/reliability.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.51579036,51579030)the Fundamental Research Funds for the Central Universities of China(No.DUT14YQ108)
文摘A 2D vertical （2DV） numerical model, without o-coordinate transformation in the vertical direction, is developed for the simulation of flow and sediment transport in open channels. In the model, time-averaged Reynolds equations are closed by the k-e nonlinear turbulence model. The modified Youngs- VOF method is introduced to capture free surface dynamics, and the free surface slope is simulated using the ELVIRA method. Based on the power-law scheme, the k-e model and the suspended-load transport model are solved numerically with an implicit scheme applied in the vertical plane and an explicit scheme applied in the horizontal plane. Bedload transport is modeled using the Euler-WENO scheme, and the grid-closing skill is adopted to deal with the moving channel bed boundary. Verification of the model using laboratory data shows that the model is able to adequately simulate flow and sediment transport in open channels, and is a good starting point for the study of sediment transport dynamics in strong nonlinear flow scenarios.
基金Supported by the Research Project of Department of Water Resources of Zhejiang Province of China (No. RB1010)
文摘Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （TSVD）. An earth-rock dam in China is presented and discussed as an example. The analysis consists of three steps： multicollinearity detection, regularization pa- rameter selection, and crack opening modeling and forecasting. Generalized Cross-Validation （GCV） function and L-curve criterion are both adopted in the regularization parameter selection. Partial Least-Squares Regression （PLSR） and stepwise regression are also included for comparison. The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models. However, no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem. Both fitting accuracy and coefficients＇ reasonability should be considered when evaluating the mode/reliability.
