Gortler vortices are key issues in the design of gas turbine blades. The present study deals with flow visualization over concave surface for gas turbine applications. The aim is to comprehend qualitatively the flow t...Gortler vortices are key issues in the design of gas turbine blades. The present study deals with flow visualization over concave surface for gas turbine applications. The aim is to comprehend qualitatively the flow trends, particularly the Gortler vortices formation and development. Gortler vortices have the shape of mushroom-like vortices regularly spaced at 25 mm. These vortices grow and increase in strength more rapidly along the surface in the case of the same grid of turbulence applied to the measuring section. The curvature radius of the studied blade is 0.5 m and the stream turbulence intensity level is 2.6%. The velocity field is measured by hot wire anemometer in the streamwise direction. The velocity profile is found to be highly distorted by the momentum transfer associated with Gortler vortices. The results are compared to Blasius flow and to literature data for a blade with curvature radius equal to 2 m.展开更多
Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient ...Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage.Specifically,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding sub-spaces.To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the RCAM.Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object detection.The project is publicly available at https://github.com/GewelsJI/FSNet.展开更多
文摘Gortler vortices are key issues in the design of gas turbine blades. The present study deals with flow visualization over concave surface for gas turbine applications. The aim is to comprehend qualitatively the flow trends, particularly the Gortler vortices formation and development. Gortler vortices have the shape of mushroom-like vortices regularly spaced at 25 mm. These vortices grow and increase in strength more rapidly along the surface in the case of the same grid of turbulence applied to the measuring section. The curvature radius of the studied blade is 0.5 m and the stream turbulence intensity level is 2.6%. The velocity field is measured by hot wire anemometer in the streamwise direction. The velocity profile is found to be highly distorted by the momentum transfer associated with Gortler vortices. The results are compared to Blasius flow and to literature data for a blade with curvature radius equal to 2 m.
基金This work was supported by the National Natural Science Foundation of China(62176169,61703077,and 62102207).
文摘Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage.Specifically,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding sub-spaces.To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the RCAM.Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object detection.The project is publicly available at https://github.com/GewelsJI/FSNet.
