藤井 孝藏

<jats:title>Abstract</jats:title> <jats:p>Scale-resolving simulations possess considerable benefits over modeled approaches because of their ability to access the underlying nonlinear fluid dynamics, and thus to predict not only the correct phenomenology, but also to generate insights on strategies to mitigate or eliminate undesirable features. The expense of resolving all pertinent turbulent scales becomes prohibitive however, as the size of the problem, typically measured by the Reynolds number based on a suitable set of reference parameters, becomes large, as is the case with flows of industrial interest such as full aircraft or their complex subsystems. This paper provides an assessment of scale-resolving methods, including some of the main benefits as well as barriers for use on large problems, together with a perspective on historical and recent trends that appear promising in the quest for routine industrial use. The factors that constitute the biggest hurdles to achieving acceptable wall-clock times and costs include meshing of complicated geometries, numerical schemes that are robust as well as accurate, suitable initial and boundary conditions, economical yet appropriate representation of near-wall turbulence, code parallelism, scalability and portability, and post-processing of the resulting big datasets. Considerations for these interrelated aspects are highlighted in the context of several 3D problems of increasing complexity, from wing sections without and with sweep, to aircraft wakes, propulsion subsystems, scramjet flowpaths and finally, full aircraft including empennages. Collectively, these examples feature the benefits of scale-resolving simulations. An illustrative approach that has reached a relatively high level of maturity using automatic mesh generation, a non-dissipative yet robust scheme, wall-modeling of turbulence, superior scalability and requiring little user intervention beyond providing the surface model, is used to demonstrate the potential of scale-resolving simulations for industry, achievable at modest cost and in reasonable wall-clock time.</jats:p>

The advancement of Arrival MANager (AMAN) is crucial for addressing the increasing complexity and demand of modern airspace. This study evaluates the operational feasibility and effectiveness of an innovative AMAN designed for en route airspace, the so-called En Route AMAN. The En Route AMAN functions as a controller support system, facilitating the sharing of information between en route air traffic controllers (ATCos), approach controllers (current AMAN), and airport controllers (Departure Managers) in airports with multiple runways. The En Route AMAN aims to support upstream ATCos by sequencing and spacing of incoming streams via speed control and runway assignment, thereby enhancing overall air traffic efficiency. Human-In-The-Loop simulations involving rated ATCos are performed under scenarios that replicate real-world traffic and weather conditions. These simulations focus on upstream airspace to assess the impact of En Route AMAN on delay mitigation and ATCos’ performance. Unlike previous studies that solely relied on theoretical models and fast-time simulation for operational feasibility evaluation, this approach incorporates ATCos’ real-time decision-making, situational awareness, and task management, addressing critical operationalization challenges. The results demonstrated that the En Route AMAN could reduce the average flight duration by up to 25.6 s and decrease the total number of ATCo instructions by up to 20% during peak traffic volume. These findings support that the En Route AMAN is both operationally viable and effective in mitigating arrival delays, highlighting the importance of Human-In-The-Loop for practical validation.

Abstract The aerodynamic noise from fan motors is main noise source of domestic electric appliances and office automation equipment. Reduction of the fan motor noise emission is important to construct comfortable living and working environment. In this study, we investigate aerodynamic noise generation mechanisms associated with a rotating small axial fan using high-resolution large-eddy simulation. The computed results present the first mode of blade passing frequency and its harmonics although there is slight difference in the frequency. Using the data obtained, data analyses are carried out by using snapshot proper orthogonal decomposition and dynamic mode decomposition. The results indicate and suggest possible three sources of noise: variation of the flows over the impellers, complex flows near the boss of the fan, and separated flows in a narrow region between the impeller tips and the casing.

This paper reports computational analysis of location and strength of sound source of the noise generated by a small axial fan widely used as an air-cooling system. High-fidelity Navier-Stokes simulations with high-resolution compact scheme are conducted with an implicit Large Eddy Simulation (LES) method on a HPC system and the resultant large-scale data confirms existence of unsteady vortex structures and their interactions around the impellers, boss and casing of the fan. To identify location and strength of the sound sources, reduced order model analysis is conducted for the distribution of pressure fluctuations in space and time. Snapshot POD (Proper Orthogonal Decomposition) analysis both in time and in circumferential direction, together with conventional FFT analysis, identifies location and strength of the sound sources. In addition, Convolutional Neural Network (CNN) is attempted, which shows more physical mode decomposition and separates some of the important features shown in the snapshot POD analysis. The study shows that the two data-mining techniques considered here identify possible aerodynamic noise sources of the axial fan clearly in comparison to those in the previous studies.