A neural network technique that extracts underlying flow features from the original flow field data is newly proposed. The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder. Unlike the typical convolutional neural network (CNN) that changes the size of the data itself in the intermediate layers, flow field data size is not changed in the learning process of this method and only the numbers of channels are changed in each layer. Different but the same size of the data as the input are obtained by convolution with multiple spatially overlapping flow field data under the assumption of sparsity. When data restoration is realized in this autoencoder system, the channel numbers of data in the intermediate layers turn out to contain different flow characteristics of the original flow field. The proposed method is applied to the low Reynolds number flows over a circular cylinder. The high-fidelity unsteady flow data obtained by solving two-dimensional compressible Navier–Stokes equations with a high-resolution numerical scheme are used as a test case. In the proposed method, sparsity introduced in the middle-hidden layer is essential for the successful separation of the original data. The results presented in the example seem to correspond to positive and negative magnitudes of the original data, but future studies will reveal other features of the method. The present method shows flow features different from those of proper orthogonal decomposition in each mode, which is probably due to nonlinear decomposition in the CNN process.

A computational study was conducted on flows over an NACA0015 airfoil with dielectric barrier discharge (DBD) plasma. The separated flows were controlled by a DBD plasma actuator installed at the 5% chord position from the leading edge, where operated AC voltage was modulated with the duty cycle not given a priori but dynamically changed based on the flow fluctuations over the airfoil surface. A single-point pressure sensor was installed at the 40% chord position of the airfoil surface and the DBD plasma actuator was activated and deactivated based on the strength of the measured pressure fluctuations. The Reynolds number was set to 63,000 and flows at angles of attack of 12 and 16 degrees were considered. The three-dimensional compressible Navier–Stokes equations including the DBD plasma actuator body force were solved using an implicit large-eddy simulation. Good flow control was observed, and the burst frequency proven to be effective in previous fixed burst frequency studies is automatically realized by this approach. The burst frequency is related to the characteristic pressure fluctuation; our approach was improved based on the findings. This improved approach realizes the effective burst frequency with a lower control cost and is robust to changing the angle of attack.

There are ongoing research efforts to implement En route arrival manager (AMAN), which decides arrival runways and controls cruise speed in en route airspace. Air traffic control operations that regulate arrival air traffic flows from en route airspace are considered effective in mitigating the congestion close to destination airports. Therefore, this study proposes a scientific system design for operationally feasible En Route AMAN assisting air traffic controllers (ATCos) through runway-flow and inter-aircraft control. Herein, we devise an airline-oriented runway assignment rule that selects a target minimizing arrival taxi time in case of over-demand according to the maximum estimated through the stochastic distribution of inter-aircraft time and runway occupancy time. We also formulate speed control rules based on inter-aircraft spacing using simulation-based optimization and decision tree analysis to visualize the distinct strategies and rules for the traffic responsible for each ATCo. Furthermore, an agent-based simulation is performed to evaluate the system effectiveness in reducing the arrival delay. The simulation indicates 20-d arrival and departure at the Tokyo International Airport, Japan, between 06:00 and 23:00. The results show that the designed IF–THEN rules reduce the total arrival sequencing delay time and arrival taxi time by 21% (median, 55.8 s) and 6.9% (median, 24.6 s). Our findings suggest that truly optimal scheduled time of arrival (STA) and operationally feasible rules for ATCos could promise congestion relief while ensuring the interpretability and possibility of En Route AMAN implementation.

Large-eddy simulations of the flows over an NACA0015 airfoil were conducted to investigate a flow control authority of a dielectric barrier discharge plasma actuator at pre-stall angles of attack. The Reynolds number was set to 63,000, and angles of attack were set to 4, 6, 8, and 10 degrees. The plasma actuator was installed at 5% chord length from the airfoil’s leading edge. Good flow control authority was confirmed in terms of lift-to-drag ratio increase and drag reduction. These improvements mainly result from the reduction of the pressure drug, which is due to the change in pressure distribution accompanying the movement and shrink of the laminar separation bubble on the airfoil surface. Additionally, although flow control using a burst drive with a nondimensional burst frequency of six improves the lift-to-drag ratio at all angles of attack, the phenomena leading to the improvement differ between near-stall angles (10 and 12 degrees) and the other lower angles. At near-stall angles, the turbulent transition is rapidly promoted by PA, and the flow is reattached. Whereas, at the lower angles, the transport of two-dimensional vortex structures, which maintain their structures up to downstream and suppress the turbulent transition, makes the flow reattachment.

Although schedule design has further potential to reduce airline operation costs and flight delay, the effectiveness of the globally optimal schedule design integrating air traffic flow has not been discussed thus far. This paper presents a global multi-objective takeoff time optimization to design efficient flight schedules that lead to minimal congestion and provide sufficient resilience against traffic problems. NSGA-II is adopted as the multi-objective optimization technique in this study. The objective functions include minimization of the total arrival delay and total fuel consumption because these are key performance indicators of air traffic management (ATM). The design variable used in this study is the takeoff time offset of each flight landing at the Tokyo International Airport. 607 design variables were used in this study. The range of the design variables was ±300 s to investigate the effect of a minor variation in the takeoff time. A cellular automaton-based model was utilized to simulate the interaction of the flights with each other. The results of the simulations demonstrated that the obtained optimal solutions could drastically reduce the total arrival delay and total fuel consumption by 1500 min and 80 tons, respectively. The spacing adjustments of one of the optimum flight schedules, in comparison to the original flight schedule, were reduced by 80% in the en-route and terminal airspaces. Additional analyses suggest that it is preferable to have longer takeoff time intervals for flights originating from the same point during congestion hours than those during non-congestion hours. This indicates that the optimization of ground movements in airports improves the efficiency of air traffic operations.