In this study, the dynamic stability derivatives of an aircraft model are calculated using CFD for the forced-pitch oscillation. The time-spectral, or reduced-frequency, method has been developed for RANS simulations on unstructured grids. It achieves faster computations than the time-marching method for periodically unsteady flows. The efficiency and accuracy of the method are first validated through comparisons with the transonic experiment of a pitching LANN wing. Next, the longitudinal dynamic-stability derivatives of a simplified aircraft model are calculated. Dependency of the damping-in-pitch and oscillatory longitudinal stability on the Mach number agreed reasonably well with the experimental results. Both the instantaneous flow field and frequency characteristics obtained directly from the time-spectral results are discussed to determine the effect of Mach number on the stability derivatives.
Increasing air traffic and airport congestion calls for solutions to improve traffic throughput. In this paper, we evaluate one solution, Flight-deck Interval Management and its underlying logic, particularly in terms of its utility. An environment to simulate traffic engaged in interval management on the most common arrival routes toward Tokyo International Airport was set up and run as a large-scale Monte Carlo simulation on the K-Supercomputer. The results show that under low wind conditions in particular, spacing goal times with a standard deviation of less than 5 s can be achieved and a speed tolerance mode can reduce the number of speed commands without compromising performance.
The objective of this study is to ascertain the aerodynamic characteristics of low-aspect-ratio wings at Reynolds numbers ranging between 1×103 and 1×104 which correspond to insect wings. The wings tested in this study are rectangular, thin, flat plates with aspect ratios varying from 0.5 to 2. The very small forces and moment acting on the wings were measured using a low-pressure wind tunnel. Although a large maximum lift coefficient was obtained for the wing with an aspect ratio of 1 at a Reynolds number of 1×104, it decreased as the Reynolds number decreased due to the disappearance of vortex lift. That is, the Reynolds number effects of low-aspect-ratio wings were found at the current Reynolds number. However, as the aspect ratio of the wing changed, the rate at which the maximum lift coefficient decreased was reduced. The main cause of the phenomenon was the behavior of the wing-tip vortices. These aerodynamic characteristics of wings will be important for developing insect-sized aircraft.
In the classification of remote-sensing sea ice images, labeled samples are difficult to acquire. To adequately utilize the massive number of unlabeled samples, which contain abundant information, we propose a cooperative framework based on active learning (AL) and semi-supervised learning (SSL) for sea ice image classification. We acquire the most valuable samples using AL and make full use of the abundant information contained in the unlabeled samples using SSL, and then conduct a label consistency verification procedure to further ensure the quality of the pseudo-labeled samples obtained through cooperation between AL and SSL. In the AL part, we adopt a sampling strategy that integrates uncertainty and diversity criteria to acquire the most valuable samples to label. In the SSL part, we utilize the SSL sampling strategy to choose the unlabeled samples with the most information and little redundancy, and use the transductive support vector machine (TSVM) as the classification model. The cooperation between AL and SSL ensures the accuracy of the pseudo-labeled samples through a consistency verification procedure. We conduct comparative experiments using the method proposed and other methods on two hyperspectral images obtained from the Earth Observation Satellite 1 (EO-1). The proposed method achieves the highest classification accuracy for both datasets and can be effectively applied to sea ice classification.