The grid fin is an unconventional control surface used on missiles and rockets. Although aerodynamics of grid fin has been studied by many researchers, few considers the aeroelastic effects.In this paper, the static a...The grid fin is an unconventional control surface used on missiles and rockets. Although aerodynamics of grid fin has been studied by many researchers, few considers the aeroelastic effects.In this paper, the static aeroelastic simulations are performed by the coupled viscous computational fluid dynamics with structural flexibility method in transonic and supersonic regimes. The developed coupling strategy including fluid–structure interpolation and volume mesh motion schemes is based on radial basis functions. Results are presented for a vertical and a horizontal grid fin mounted on a body. Horizontal fin results show that the deformed fin is swept backward and the axial force is increased. The deformations also induce the movement of center of pressure, causing the reduction and reversal in hinge moment for the transonic flow and the supersonic flow,respectively. For the vertical fin, the local effective incidences are increased due to the deformations so that the deformed normal force is greater than the original one. At high angles of attack, both the deformed and original normal forces experience a sudden reduction due to the interference of leeward separated vortices on the fin. Additionally, the increment in axial force is shown to correlate strongly with the increment in the square of normal force.展开更多
A cell-centred overset unstructured grids approach is developed.In this approach,the intergrid boundary is initially established based on the wall distance from the cell centre,and is then optimized.To accelerate the ...A cell-centred overset unstructured grids approach is developed.In this approach,the intergrid boundary is initially established based on the wall distance from the cell centre,and is then optimized.To accelerate the intergrid-boundary definition much more,a neighbor-toneighbor donor search algorithm based on advancing-front method is modified with the help of minimum cuboid boxes.To simplify the communications between different grid cell types and to obtain second-order spatial accuracy,a new interpolation method is constructed based on linear reconstruction,which employs only one layer of fringe cells along the intergrid boundary.For unsteady flows with relative motion,the intergrid boundary can be redefined fast and automatically.Several numerical results show that the present dynamic overset unstructured grids approach is accurate and reliable.展开更多
Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate ...Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate timely clinical interventions,preventing adverse outcomes.Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images(FFA)which capture retinal components featuring diverse morphologies such as retinal vasculature,macula,optical disk etc.However,these images have low resolutions,hindering the accurate detection of ocular disorders.Construction of high resolution images from these images,by super resolution approaches expedites the diagnosis of pathologies with better accuracy.This paper presents a deep learning network for Single Image Super Resolution(SISR)of fundus fluorescein angiography images,modeled on residual learning,gridded interpolation and Swish activation functions.The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors.Evaluation of the performance of this network and comparative analysis with benchmark architectures,on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.展开更多
文摘The grid fin is an unconventional control surface used on missiles and rockets. Although aerodynamics of grid fin has been studied by many researchers, few considers the aeroelastic effects.In this paper, the static aeroelastic simulations are performed by the coupled viscous computational fluid dynamics with structural flexibility method in transonic and supersonic regimes. The developed coupling strategy including fluid–structure interpolation and volume mesh motion schemes is based on radial basis functions. Results are presented for a vertical and a horizontal grid fin mounted on a body. Horizontal fin results show that the deformed fin is swept backward and the axial force is increased. The deformations also induce the movement of center of pressure, causing the reduction and reversal in hinge moment for the transonic flow and the supersonic flow,respectively. For the vertical fin, the local effective incidences are increased due to the deformations so that the deformed normal force is greater than the original one. At high angles of attack, both the deformed and original normal forces experience a sudden reduction due to the interference of leeward separated vortices on the fin. Additionally, the increment in axial force is shown to correlate strongly with the increment in the square of normal force.
基金supported by the National Basic Research Program of China (2009CB724104)
文摘A cell-centred overset unstructured grids approach is developed.In this approach,the intergrid boundary is initially established based on the wall distance from the cell centre,and is then optimized.To accelerate the intergrid-boundary definition much more,a neighbor-toneighbor donor search algorithm based on advancing-front method is modified with the help of minimum cuboid boxes.To simplify the communications between different grid cell types and to obtain second-order spatial accuracy,a new interpolation method is constructed based on linear reconstruction,which employs only one layer of fringe cells along the intergrid boundary.For unsteady flows with relative motion,the intergrid boundary can be redefined fast and automatically.Several numerical results show that the present dynamic overset unstructured grids approach is accurate and reliable.
文摘Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate timely clinical interventions,preventing adverse outcomes.Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images(FFA)which capture retinal components featuring diverse morphologies such as retinal vasculature,macula,optical disk etc.However,these images have low resolutions,hindering the accurate detection of ocular disorders.Construction of high resolution images from these images,by super resolution approaches expedites the diagnosis of pathologies with better accuracy.This paper presents a deep learning network for Single Image Super Resolution(SISR)of fundus fluorescein angiography images,modeled on residual learning,gridded interpolation and Swish activation functions.The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors.Evaluation of the performance of this network and comparative analysis with benchmark architectures,on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.
