Numerical simulation of low-Reynolds number flows past two tandem cylinders of different diameters

The flow past two tandem circular cylinders of different diameters was simulated using the finite volume method. The diameter of the downstream main cylinder （D） was kept constant, and the diameter of the upstream control cylinder （d） varied from 0.1D to D. The studied Reynolds numbers based on the diameter of the downstream main cylinder were 100 and 150. The gap between the control cylinder and the main cylinder （G） ranged from 0.1D to 4D. It is concluded that the gap-to-diameter ratio （G/D） and the diameter ratio between the two cylinders （d/D） have important effects on the drag and lift coefficients, pressure distributions around the cylinders, vortex shedding frequencies from the two cylinders, and flow characteristics.

An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection

With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.