Search

In this work, we propose a deep learning-based model for mapping between the data of the flow field of the propeller generated by the Reynolds-averaged Navier–Stokes (RANS) and those generated by Large Eddy Simulation (LES). The goal of establishing the mapping model is to generate LES data, which needs higher computing power requirements, with the help of RANS data. The model utilizes a deep learning method for computer vision to handle three-dimensional data generated by RANS and those by LES. Firstly, the data samples of the RANS flow field and those of the LES flow field are processed to obtain their corresponding three-dimensional image data, respectively. Secondly, the two kinds of field flow images are used as the training data for constructing a mapping model between RANS flow field images and the corresponding LES flow field images. The obtained mapping model thus can be used to predict the LES flow field images. Thirdly, the regression module is employed to regress the three-dimensional LES image point-by-point to the velocities at the points of the LES flow field, thereby ultimately achieving the generation of LES flow field data from RANS data. The experimental results show that by applying this method to RANS data and LES data of propeller flow fields, the overall error rate of LES flow field prediction by this method is 17.68% compared to actual flow field data, which verifies the effectiveness and accuracy of the proposed model in LES flow field prediction. [ABSTRACT FROM AUTHOR]

Introduction: Simultaneous Localization and Mapping (SLAM) is a technology used in intelligent systems such as robots and autonomous vehicles. Visual SLAM has become a more popular type of SLAM due to its acceptable cost and good scalability when applied in robot positioning, navigation and other functions. However, most of the visual SLAM algorithms assume a static environment, so when they are implemented in highly dynamic scenes, problems such as tracking failure and overlapped mapping are prone to occur. Methods: To deal with this issue, we propose ISFM-SLAM, a dynamic visual SLAM built upon the classic ORB-SLAM2, incorporating an improved instance segmentation network and enhanced feature matching. Based on YOLACT, the improved instance segmentation network applies the multi-scale residual network Res2Net as its backbone, and utilizes CIoU_Loss in the bounding box loss function, to enhance the detection accuracy of the segmentation network. To improve the matching rate and calculation efficiency of the internal feature points, we fuse ORB key points with an efficient image descriptor to replace traditional ORB feature matching of ORB-SLAM2. Moreover, the motion consistency detection algorithm based on external variance values is proposed and integrated into ISFM-SLAM, to assist the proposed SLAM systems in culling dynamic feature points more effectively. Results and discussion: Simulation results on the TUM dataset show that the overall pose estimation accuracy of the ISFM-SLAM is 97% better than the ORB-SLAM2, and is superior to other mainstream and state-of-the-art dynamic SLAM systems. Further real-world experiments validate the feasibility of the proposed SLAM system in practical applications. [ABSTRACT FROM AUTHOR]