For practical large-scale applications of computational fluid dynamics in the aero-space industry,implicit flow solvers are necessitated for efficient simulations.This paper presents the implementation of a solver tha...For practical large-scale applications of computational fluid dynamics in the aero-space industry,implicit flow solvers are necessitated for efficient simulations.This paper presents the implementation of a solver that employs an unstructured finite volume approach and a Multi-Colored Gauss-Seidel(MCGS)method for steady-state compressible flow simulations on a server equipped with multiple Graphics Process-ing Units(GPUs).The mesh partition process is completed with PyMetis,and Mes-sage Passing Interface(MPI)is utilized for communications between mesh partitions.A parallel coloring algorithm is employed in the pre-processing module.The code is developed using a hybrid programming approach,with the main framework writ-ten in Python and the GPU kernel source codes written in C.The transonic turbulent flows over the CHN-T1 transport aircraft model are simulated on unstructured hybrid meshes.The numerical results are compared with experimental data,and the perfor-mance of the developed flow simulation framework is analysed.展开更多
Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniqu...Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these problems.This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft Kinect.It then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole detection.The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation.We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.展开更多
基金sponsored by Shanghai Pujiang Program(Grant No.22PJ1420200).
文摘For practical large-scale applications of computational fluid dynamics in the aero-space industry,implicit flow solvers are necessitated for efficient simulations.This paper presents the implementation of a solver that employs an unstructured finite volume approach and a Multi-Colored Gauss-Seidel(MCGS)method for steady-state compressible flow simulations on a server equipped with multiple Graphics Process-ing Units(GPUs).The mesh partition process is completed with PyMetis,and Mes-sage Passing Interface(MPI)is utilized for communications between mesh partitions.A parallel coloring algorithm is employed in the pre-processing module.The code is developed using a hybrid programming approach,with the main framework writ-ten in Python and the GPU kernel source codes written in C.The transonic turbulent flows over the CHN-T1 transport aircraft model are simulated on unstructured hybrid meshes.The numerical results are compared with experimental data,and the perfor-mance of the developed flow simulation framework is analysed.
基金the National Key R&D Program of China(Grant No.2020AAA0108100)the Fundamental Research Funds for the Central Universities(Grant Nos.22120220184,22120220214 and 2022-5-YB-08)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these problems.This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft Kinect.It then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole detection.The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation.We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.
