The study of flow diversions in open channels plays an important practical role in the design and management of open-channel networks for irrigation or drainage. To accurately predict the mean flow and turbulence char...The study of flow diversions in open channels plays an important practical role in the design and management of open-channel networks for irrigation or drainage. To accurately predict the mean flow and turbulence characteristics of open-channel dividing flows, a hybrid LES-RANS model, which combines the large eddy simulation （LES） model with the Reynolds-averaged Navier-Stokes （RANS） model, is proposed in the present study. The unsteady RANS model was used to simulate the upstream and downstream regions of a main channel, as well as the downstream region of a branch channel. The LES model was used to simulate the channel diversion region, where turbulent flow characteristics are complicated. Isotropic velocity fluctuations were added at the inflow interface of the LES region to trigger the generation of resolved turbulence. A method based on the virtual body force is proposed to impose Reynolds-averaged velocity fields near the outlet of the LES region in order to take downstream flow effects computed by the RANS model into account and dissipate the excessive turbulent fluctuations. This hybrid approach saves computational effort and makes it easier to properly specify inlet and outlet boundary conditions. Comparison between computational results and experimental data indicates that this relatively new modeling approach can accurately predict open-channel T-diversion flows.展开更多
目的低剂量投影条件下的CT图像重建。方法采用双层K-奇异值分解(K-singular value decomposition,K-SVD)字典训练的学习方法进行图像的超分辨率重建。字典学习方法中采用KSVD算法,稀疏编码采用正交匹配追踪(orthogonal matching pursuit...目的低剂量投影条件下的CT图像重建。方法采用双层K-奇异值分解(K-singular value decomposition,K-SVD)字典训练的学习方法进行图像的超分辨率重建。字典学习方法中采用KSVD算法,稀疏编码采用正交匹配追踪(orthogonal matching pursuit,OMP)算法。该算法首先利用训练库进行第一层字典训练,然后利用第一层训练的字典对低分辨率图像进行重建。进而将重建图像作为第二层待重建图像的输入,这样使得第二层输入图像含有较多的高频细节信息,因此能在重构的过程中恢复更多的细节信息,让高分辨率重构图像达到较好的效果。结果双层字典重建效果明显优于KSVD算法,重建图像更接近于原始高分辨率CT图像。结论本研究对双层字典训练学习的框架进行反迭代投影的全局优化改进,改善了图像的重建质量。展开更多
文摘The study of flow diversions in open channels plays an important practical role in the design and management of open-channel networks for irrigation or drainage. To accurately predict the mean flow and turbulence characteristics of open-channel dividing flows, a hybrid LES-RANS model, which combines the large eddy simulation （LES） model with the Reynolds-averaged Navier-Stokes （RANS） model, is proposed in the present study. The unsteady RANS model was used to simulate the upstream and downstream regions of a main channel, as well as the downstream region of a branch channel. The LES model was used to simulate the channel diversion region, where turbulent flow characteristics are complicated. Isotropic velocity fluctuations were added at the inflow interface of the LES region to trigger the generation of resolved turbulence. A method based on the virtual body force is proposed to impose Reynolds-averaged velocity fields near the outlet of the LES region in order to take downstream flow effects computed by the RANS model into account and dissipate the excessive turbulent fluctuations. This hybrid approach saves computational effort and makes it easier to properly specify inlet and outlet boundary conditions. Comparison between computational results and experimental data indicates that this relatively new modeling approach can accurately predict open-channel T-diversion flows.
文摘目的低剂量投影条件下的CT图像重建。方法采用双层K-奇异值分解(K-singular value decomposition,K-SVD)字典训练的学习方法进行图像的超分辨率重建。字典学习方法中采用KSVD算法,稀疏编码采用正交匹配追踪(orthogonal matching pursuit,OMP)算法。该算法首先利用训练库进行第一层字典训练,然后利用第一层训练的字典对低分辨率图像进行重建。进而将重建图像作为第二层待重建图像的输入,这样使得第二层输入图像含有较多的高频细节信息,因此能在重构的过程中恢复更多的细节信息,让高分辨率重构图像达到较好的效果。结果双层字典重建效果明显优于KSVD算法,重建图像更接近于原始高分辨率CT图像。结论本研究对双层字典训练学习的框架进行反迭代投影的全局优化改进,改善了图像的重建质量。
