Vertical axis wind turbine is a special type of wind-force electric generator which is capable of working in the complicated wind environment. The self-starting aerodynamics is one of the most important considerations for this kind of turbine. This article aims at providing a systematic synthesis on the self-starting aerodynamic characteristics of vertical axis wind turbine based on the numerical analysis approach. First, the physical model of vertical axis wind turbine and its parameter definitions are presented. Secondary, the interaction model between the vertical axis wind turbine and fluid is developed by using the weak coupling approach; the numerical data of this model are then compared with the wind tunnel experimental data to show its feasibility. Third, the effects of solidity and fixed pitch angle on the self-starting aerodynamic characteristics of the vertical axis wind turbine are analyzed systematically. Finally, the quantification effects of the solidity and fixed pitch angle on the self-starting performance of the turbine can be obtained. The analysis in this study will provide straightforward physical insight into the self-starting aerodynamic characteristics of vertical axis wind turbine.

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Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.