Analysis of the Impact of the Space Guide Vane Wrap Angle on the Performance of a Submersible Well Pump

In order to study the influence of the wrap angle relating to the space guide vane of a submersible well pump(250QJ125)on the flow field and pump performance,seven possible configurations have been considered(obtained by changing the blade wrap angle while keeping unchanged all the other parameters).Such configurations have been numerically simulated in the framework of a computational model based on the Reynolds time-averaged N-S equations,the RNG k-εturbulence approach and the SIMPLE algorithm.The impact exerted by different wrap angles of the guide vane on the performance of the pump,the internal losses of the guide vane and the flow field distribution in the bladeless area at the guide vane outlet has been assessed via cross-comparison of all these cases.The results show that the wrap angle has a significant influence:the wrap angle with the highest head is different from that with the highest efficiency,and changes in this angle have a more significant effect on the head than efficiency.A moderate raise of the wrap angle can improve the properties of the flow,reduce turbulence losses and enhance the energy conversion rate inside the guide vane.Different wrap angles can also lead to different fluid circulation modes in the bladeless area from guide vane outlet to impeller inlet,while they have a weak influence on the absolute value of the velocity of the fluid entering the impeller.

A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data

Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.