The characteristics of water and sand two-phase flow and their wear features in a rotating jet wear device at various impact angles are investigated by the wear weight loss test,spraying paint abrasion distribution ex...The characteristics of water and sand two-phase flow and their wear features in a rotating jet wear device at various impact angles are investigated by the wear weight loss test,spraying paint abrasion distribution experiment and numerically multiphase simulation.The results reveal that the weight loss of specimen abrasion initially increases and then decreases as the impact angle rises,peaking at about 40°.The annular abrasion distribution on the test disk can be obtained by the simulation model which adopts the slip grid method to handle the rotation of disk,aligning well with experimental results.Furthermore,the abrasion distribution and weight loss predicted by the Oka abrasion model and the Grant and Tabakoff(G&T)collision rebound model closely match the experimental data.At lower impact angles(15°–45°),the jet velocity is low while the rotational speed is high,and the two-phase jet flow spreads towards the specimen’s outer edge due to centrifugal force,which results in the increased wear on the specimens with the disk’s radius.At the impact angle of 60°,high abrasion rate strip is observed near the specimen’s centerline in both the paint spray test and numerical simulation.At this angle,the jet collides with the rotating wall and generates a spiral trajectory along the circumferential position of the disc,forming vortices at the downstream of the nozzle.The particle aggregate inside the vortices,forming high sediment concentration distribution and high wear rate strip on the specimen.This work will establish a foundation for the simulation and testing of sediment wear in hydraulic machineries.展开更多
In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.T...In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.展开更多
基金supported by the Science and Technology Plan Project of the Shaanxi Province Department of Water Resources(Grant No.2024slkj-05).
文摘The characteristics of water and sand two-phase flow and their wear features in a rotating jet wear device at various impact angles are investigated by the wear weight loss test,spraying paint abrasion distribution experiment and numerically multiphase simulation.The results reveal that the weight loss of specimen abrasion initially increases and then decreases as the impact angle rises,peaking at about 40°.The annular abrasion distribution on the test disk can be obtained by the simulation model which adopts the slip grid method to handle the rotation of disk,aligning well with experimental results.Furthermore,the abrasion distribution and weight loss predicted by the Oka abrasion model and the Grant and Tabakoff(G&T)collision rebound model closely match the experimental data.At lower impact angles(15°–45°),the jet velocity is low while the rotational speed is high,and the two-phase jet flow spreads towards the specimen’s outer edge due to centrifugal force,which results in the increased wear on the specimens with the disk’s radius.At the impact angle of 60°,high abrasion rate strip is observed near the specimen’s centerline in both the paint spray test and numerical simulation.At this angle,the jet collides with the rotating wall and generates a spiral trajectory along the circumferential position of the disc,forming vortices at the downstream of the nozzle.The particle aggregate inside the vortices,forming high sediment concentration distribution and high wear rate strip on the specimen.This work will establish a foundation for the simulation and testing of sediment wear in hydraulic machineries.
基金This work was supported by Science and Technology Project of State Grid Corporation of China(Research and application of audiovisual active perception and collaborative cognitive technology for smart grid operation and maintenance scenarios)(5600–202046347 A-0–0–00).
文摘In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.
