Disturbance in wind regime and sand erosion deposition balance may lead to burial and eventual vanishing of a site.This study conducted 3D computational fluid dynamics(CFD)simulations to evaluate the effect of a propo...Disturbance in wind regime and sand erosion deposition balance may lead to burial and eventual vanishing of a site.This study conducted 3D computational fluid dynamics(CFD)simulations to evaluate the effect of a proposed city design on the wind environment of the Crescent Spring,a downwind natural heritage site located in Dunhuang,Northwestern China.Satellite terrain data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)Digital Elevation Model(DEM)were used to construct the solid surface model.Steady-state Reynolds Averaged Navier-Stokes equations(RANS)with shear stress transport(SST)k-ωturbulence model were then applied to solve the flow field problems.Land-use changes were modeled implicitly by dividing the underlying surface into different areas and by applying corresponding aerodynamic roughness lengths.Simulations were performed by using cases with different city areas and building heights.Results show that the selected model could capture the surface roughness changes and could adjust wind profile over a large area.Wind profiles varied over the greenfield to the north and over the Gobi land to the east of the spring.Therefore,different wind speed reduction effects were observed from various city construction scenarios.The current city design would lead to about 2 m/s of wind speed reduction at the downwind city edge and about 1 m/s of wind speed reduction at the north of the spring at 35-m height.Reducing the city height in the north greenfield area could efficiently eliminate the negative effects of wind spee.By contrast,restricting the city area worked better in the eastern Gobi area compared with other parts of the study area.Wind speed reduction in areas near the spring could be limited to 0.1 m/s by combining these two abatement strategies.The CFD method could be applied to simulate the wind environment affected by other land-use changes over a large terrain.展开更多
Atmospheric boundary layer(ABL)flow over multiple-hill terrain is studied numerically.The spectral vanishing viscosity(SVV)method is employed for implicit large eddy simulation(ILES).ABL flow over one hill,double hill...Atmospheric boundary layer(ABL)flow over multiple-hill terrain is studied numerically.The spectral vanishing viscosity(SVV)method is employed for implicit large eddy simulation(ILES).ABL flow over one hill,double hills,and three hills are presented in detail.The instantaneous three-dimensional vortex structures,mean velocity,and turbulence intensity in mainstream and vertical directions around the hills are investigated to reveal the main properties of this turbulent flow.During the flow evolution downstream,the Kelvin-Helmholtz vortex,braid vortex,and hairpin vortex are observed sequentially.The turbulence intensity is enhanced around crests and reduced in the recirculation zones.The present results are helpful for understanding the impact of topography on the turbulent flow.The findings can be useful in various fields,such as wind energy,air pollution,and weather forecasting.展开更多
Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of i...Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of indoor point clouds,the amount of terrain point cloud is up to millions.Apart from that,terrain point cloud data obtained from remote sensing is measured in meters,but the indoor scene is measured in centimeters.In this case,the terrain debris obtained from remote sensing mapping only have dozens of points,which means that sufficient training information cannot be obtained only through the convolution of points.In this paper,we build multi-attribute descriptors containing geometric information and color information to better describe the information in low-precision terrain debris.Therefore,our process is aimed at the multi-attribute descriptors of each point rather than the point.On this basis,an unsupervised classification algorithm is proposed to divide the point cloud into several terrain areas,and regard each area as a graph vertex named super point to form the graph structure,thus effectively reducing the number of the terrain point cloud from millions to hundreds.Then we proposed a graph convolution network by employing PointNet for graph embedding and recurrent gated graph convolutional network for classification.Our experiments show that the terrain point cloud can reduce the amount of data from millions to hundreds through the super point graph based on multi-attribute descriptor and our accuracy reached 91.74%and the IoU reached 94.08%,both of which were significantly better than the current methods such as SEGCloud(Acc:88.63%,IoU:89.29%)and PointCNN(Acc:86.35,IoU:87.26).展开更多
基金supported by the National Basic Research Program of China(2012CB026105)the National Natural Science Foundation of China(41201003,41071009)the China Postdoctoral Science Foundation(2012M52819)
文摘Disturbance in wind regime and sand erosion deposition balance may lead to burial and eventual vanishing of a site.This study conducted 3D computational fluid dynamics(CFD)simulations to evaluate the effect of a proposed city design on the wind environment of the Crescent Spring,a downwind natural heritage site located in Dunhuang,Northwestern China.Satellite terrain data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)Digital Elevation Model(DEM)were used to construct the solid surface model.Steady-state Reynolds Averaged Navier-Stokes equations(RANS)with shear stress transport(SST)k-ωturbulence model were then applied to solve the flow field problems.Land-use changes were modeled implicitly by dividing the underlying surface into different areas and by applying corresponding aerodynamic roughness lengths.Simulations were performed by using cases with different city areas and building heights.Results show that the selected model could capture the surface roughness changes and could adjust wind profile over a large area.Wind profiles varied over the greenfield to the north and over the Gobi land to the east of the spring.Therefore,different wind speed reduction effects were observed from various city construction scenarios.The current city design would lead to about 2 m/s of wind speed reduction at the downwind city edge and about 1 m/s of wind speed reduction at the north of the spring at 35-m height.Reducing the city height in the north greenfield area could efficiently eliminate the negative effects of wind spee.By contrast,restricting the city area worked better in the eastern Gobi area compared with other parts of the study area.Wind speed reduction in areas near the spring could be limited to 0.1 m/s by combining these two abatement strategies.The CFD method could be applied to simulate the wind environment affected by other land-use changes over a large terrain.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12372220,12372219,11972220,12072185,91952102 and 12032016).
文摘Atmospheric boundary layer(ABL)flow over multiple-hill terrain is studied numerically.The spectral vanishing viscosity(SVV)method is employed for implicit large eddy simulation(ILES).ABL flow over one hill,double hills,and three hills are presented in detail.The instantaneous three-dimensional vortex structures,mean velocity,and turbulence intensity in mainstream and vertical directions around the hills are investigated to reveal the main properties of this turbulent flow.During the flow evolution downstream,the Kelvin-Helmholtz vortex,braid vortex,and hairpin vortex are observed sequentially.The turbulence intensity is enhanced around crests and reduced in the recirculation zones.The present results are helpful for understanding the impact of topography on the turbulent flow.The findings can be useful in various fields,such as wind energy,air pollution,and weather forecasting.
基金This research was funded by grant from the Key Research and Development Program of Shaanxi Province(2018NY-127,2019ZDLNY07-02-01,2020NY-205)National Undergraduate Training Program for Innovation and entrepreneurship plan(S201910712240,X201910712080).
文摘Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of indoor point clouds,the amount of terrain point cloud is up to millions.Apart from that,terrain point cloud data obtained from remote sensing is measured in meters,but the indoor scene is measured in centimeters.In this case,the terrain debris obtained from remote sensing mapping only have dozens of points,which means that sufficient training information cannot be obtained only through the convolution of points.In this paper,we build multi-attribute descriptors containing geometric information and color information to better describe the information in low-precision terrain debris.Therefore,our process is aimed at the multi-attribute descriptors of each point rather than the point.On this basis,an unsupervised classification algorithm is proposed to divide the point cloud into several terrain areas,and regard each area as a graph vertex named super point to form the graph structure,thus effectively reducing the number of the terrain point cloud from millions to hundreds.Then we proposed a graph convolution network by employing PointNet for graph embedding and recurrent gated graph convolutional network for classification.Our experiments show that the terrain point cloud can reduce the amount of data from millions to hundreds through the super point graph based on multi-attribute descriptor and our accuracy reached 91.74%and the IoU reached 94.08%,both of which were significantly better than the current methods such as SEGCloud(Acc:88.63%,IoU:89.29%)and PointCNN(Acc:86.35,IoU:87.26).
