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An Experimental Study on the Improvement of Aerodynamic Characteristics Using the Blowing for RLV
Shigeki TSUCHIYA,Shigeru ASO,Yasuhiro TANI 한국항공우주학회 2008 한국항공우주학회 학술발표회 논문집 Vol.- No.-
To improve the aerodynamic characteristics of Reusable Launch Vehicles (RLV), active flow control methods with blowing were investigated. The testing models are wing body and lifting body contigurations. The experiments have been conducted in low-speed wind tunnels of Kyushu University and transonic wind tunnel of ISAS (Institute of Space and Astronautical Science), JAXA (Japan Aerospace Exploration Agency). For the active flow control, the blowing along the fuselage surface was chosen. Flow visualization results show that the vortex on the fuselage is greatly moved near the fuselage, and the vortex core of the wing is clearly observed. The aerodynamic characteristics result shows that the blowing increases the lift and lift-to-drag ratio. From those results, we confirmed that the blowing can be useful for the improvement of aerodynamic characteristics of the RLV.
Murata, Shingo,Yamashita, Yuichi,Arie, Hiroaki,Ogata, Tetsuya,Sugano, Shigeki,Tani, Jun IEEE 2017 IEEE transactions on neural networks and learning Vol.28 No.4
<P>We suggest that different behavior generation schemes, such as sensory reflex behavior and intentional proactive behavior, can be developed by a newly proposed dynamic neural network model, named stochastic multiple timescale recurrent neural network (S-MTRNN). The model learns to predict subsequent sensory inputs, generating both their means and their uncertainty levels in terms of variance (or inverse precision) by utilizing its multiple timescale property. This model was employed in robotics learning experiments in which one robot controlled by the S-MTRNN was required to interact with another robot under the condition of uncertainty about the other's behavior. The experimental results show that self-organized and sensory reflex behavior-based on probabilistic prediction-emerges when learning proceeds without a precise specification of initial conditions. In contrast, intentional proactive behavior with deterministic predictions emerges when precise initial conditions are available. The results also showed that, in situations where unanticipated behavior of the other robot was perceived, the behavioral context was revised adequately by adaptation of the internal neural dynamics to respond to sensory inputs during sensory reflex behavior generation. On the other hand, during intentional proactive behavior generation, an error regression scheme by which the internal neural activity was modified in the direction of minimizing prediction errors was needed for adequately revising the behavioral context. These results indicate that two different ways of treating uncertainty about perceptual events in learning, namely, probabilistic modeling and deterministic modeling, contribute to the development of different dynamic neuronal structures governing the two types of behavior generation schemes.</P>