A Deep Learning Framework Based on Multisensor Fusion Information to Identify the Airplane Wake Vortex

Along with the rapid improvement of the aviation industry, flight density also increases with the increase of flight demand, which directly leads to the increasingly prominent influence of wake vortex on flight safety and aviation control. In this paper, we propose a new joint framework—a deep learning framework—based on multisensor fusion information to address the detection and identification of wake vortices in the near-Earth phase. By setting multiple Doppler lidar in near-Earth flight areas at different airports, a large number of accurate wind field data are captured for wake vortex detection. Meanwhile, the airport surveillance radar is used to locate the wake vortex. In the deep learning framework, an end-to-end CNN-LSTM model has been employed to identify the airplane wake vortex from the data detected by Doppler lidar and the airport surveillance radar. The variables including the wind field matrix, positioning matrix, and the variance sequence are used as inputs to the CNN channel and LSTM channel. The identification and location information of the wake vortex in the wind field image will be output by the framework. Experiments show that the joint framework based on a multisensor possesses stronger ability to capture local feature and sequence feature than the traditional CNN or LSTM model.

Detecting vortical structures in time-resolved volumetric flow fields

The detection of three-dimensional coherent vortical structures that get advected as well as deformed with time is a challenge. However, it is critical for the statistical analysis of these vortices, for example, the quasi-streamwise vortices (QSVs) in the near field of a turbulent shear layer, where cavitation inception typically occurs. These structures exhibit underlying correlations among different properties that can be derived from the velocity gradients. Exploiting these correlations, a pseudo-Lagrangian vortex detection method is proposed that uses k-means clustering based on vorticity magnitude and direction, values of λ2, strain rate structure, axial stretching, and location. The method facilitates the finding that QSVs have pressure minima that are lower than those in the surrounding flow, including the primary spanwise vortices. These minima typically appear after a period of axial stretching and before contraction events.

A Possible Resolution to Troubles of SU(2) Center Vortex Detection in Smooth Lattice Configurations

The center vortex model of quantum-chromodynamics can explain confinement and chiral symmetry breaking. We present a possible resolution for problems of the vortex detection in smooth configurations and discuss improvements for the detection of center vortices.

A Possible Resolution to Troubles of SU(2) Center Vortex Detection in Smooth Lattice Configurations

The center vortex model of quantum-chromodynamics can explain confinement and chiral symmetry breaking. We present a possible resolution for problems of the vortex detection in smooth configurations and discuss improvements for the detection of center vortices.

Numerical prediction of vortex trajectories and vortex–blade interaction on the CROR engine

Purpose The exponential growth in computational capabilities and the increasing reliability of current simulation tools have fostered the use of computational fluid dynamics (CFD) in the design of pioneering aircraft engine architectures, such as the counter rotating open rotor (CROR) engine. Today, this design process is led by tight performance and noise constraints from a very early stage, thus requiring deep investigations of the aerodynamic and acoustic behaviour of the fluid flow. The purpose of this study is to track the trajectory of tip vortices, which is of critical importance to understand and prevent potential vortex–blade interactions with subsequent rows, as this condition strongly influences the aerodynamic and structural performance and acoustic footprints of the engine. Design/methodology/approach In this paper, a flow feature detection methodology is applied to a particular CROR test case with the goal of visualizing and tracking the development of these coherent structures from the tip of front rotating blades. The suitability and performance of four typical region-based methodologies and one line-based (LB) criteria are firstly evaluated. Then, two novel seeding methodologies are presented as an attempt to improve the performance of the LB algorithm previously investigated. Findings It was demonstrated that the new seeding algorithms increase the probability of the selected seeds to grow into a tip vortex line and reduce the user’s dependence upon the selection of candidate seeds, providing faster and more accurate answers during the design-to-noise iterative process. Originality/value Apart from the new vortex detection initialization methodologies, the paper also attempts to assist the user in the endeavour of extracting rotating structures from their own CFD simulations.