Parainfluenza virus type 1 (strain Sendai)

Most of the research done on tidal energy focuses on marine current turbines. Therefore, tidal turbine's wake is well-documented. The tidal energy converter studied here is based on the fluid-structure interactions that occur between a flexible membrane and an axial flow, resulting in an undulating motion that can be used to harvest energy. This device's performance had been studied but it is the first time its wake is experimentally characterized from two-dimensional Particle Image Velocimetry (PIV) measurements. PIV is synchronized with a motion tracking system that gives information on trajectory and power conversion. Wake measurement gives access to velocity deficit, turbulence intensity and vorticity. Three configurations are tested in order to identify the influence of the main adjustment parameters. Pre-stress increases the membrane vertical speed, leading to a more important vertical expansion of the wake. The power extraction slows down the membrane's motion, thus limiting the wake's length and intensity. The flume tank measurements suggest that the best location for a downstream device in a tidal farm would be in the same horizontal position, at 5 membrane's length. In the open sea, the membrane interaction effects should not predominant.

A novel space mapping (SM) modeling approach for gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with trapping effects is presented in this article, advancing the SM technique for nonlinear device modeling. Existing SM modeling approach uses an external mapping to map an existing device model onto device data. When different branches inside the existing device model need to address very different behaviors, such as trapping effects and frequency dispersion in GaN HEMTs, it is hard for one external mapping to simultaneously map different behaviors. The proposed SM technique develops separate mappings for different branches, such that different behaviors can be mapped separately. Each mapping module is formulated to map a specific behavior in the overall model. Each mapping module is developed through machine learning to systematically overcome the gap between each internal branch and each set of target data, accelerating the process of model development. The proposed SM technique is a fast and systematic modeling approach, compared with the existing empirical function/equivalent circuit approach. Compared with the pure neural network modeling approach, the proposed SM technique employs less training data. Measurement data of a 2 x 350 mu m GaN HEMT device are employed for model training and verification. Good agreement can he achieved between the developed large-signal model and the measurement data, including dc, pulsed I-V (PIV) at seven quiescent biases, S-parameters, and power characteristics. Reasonably close predictions of load-pull figures of merit are achieved by the developed model. The model development illustrated in the example shows that the proposed SM technique is a fast modeling approach to develop an accurate large-signal model for GaN HEMTs.