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The wind energy exploitation technique has been developed very quickly in recent years. The vertical axis wind turbine is a hot research domain due to several advantages: low noise, flexible for installation, ease of maintenance, great safety and credibility, etc. The aerodynamic performances of different forms of airfoils including an active deformation airfoil and a fluid-solid coupling passive airfoil with two-dimensional (2D) and three-dimensional (3D) cases have been investigated numerically in this paper. Firstly, the aerodynamic performances of the airfoils with the maximum deformation amplitudes of their cambers which are 3%, 5% and 7% of the chord length have been discussed, respectively, with the angles of attack in the range of 0° and 20°. Secondly, for the angle of attack set at 18°, the two-way fluid-solid coupling simulations with the Young’s Modulus of 1 Mpa and 2 Mpa have also been investigated. Results show that: (1) for the pseudo 3D and real 3D single active deformation airfoil cases, the lift coefficients increase as the maximum deformation amplitudes augment from 3% to 7% of the chord length at the same angle of attack. With the same maximum deformation amplitude, when the angles of attack increase from 0° to 20°, the lift coefficients which increase firstly and then decrease are bigger than that of the original NACA0012 airfoil. When the maximum deformation amplitude of the pseudo 3D airfoil reaches 5% of the chord length, a relatively good aerodynamic performance with better inhibition effect of vortex generation can be obtained. The 3D vortex distribution demonstrates that the deformable airfoil has a better vortex generation controlling effect at the middle cross-section along the spanwise direction than the non-deformable airfoil. (2) From the aspect of fluid-solid coupling, the lift increases and the drag decreases so that the lift to drag ratio has a big improvement when the Young’s Modulus is equal to 1Mpa and 2Mpa. The deformable airfoil can inhibit the generation and the shedding of the surface vortex when the fluid-solid coupling effect is considered.

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Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people’s learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people’s learning styles.

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