Wall-resolved large eddy simulation over NACA0012 airfoil

The work presented here forms part of a project on Large-Eddy Simulation (LES) of aeroengine aeroacoustic interactions. In this paper we concentrate on LES of near-field flow over an isolated NACA0012 airfoil at zero angle of attack with Rec=2e5. The predicted unsteady pressure/velocity field is used in an analytically-based scheme for far-field trailing edge noise prediction. A wall resolved implicit LES or so-callednumerical Large Eddy Simulation (NLES) approach is employed to resolve streak-like structure in the near-wall flow regions. The mean and RMS velocity and pressure profile on airfoil surface and in wake are validated against experimental data and computational results from other researchers. The results of the wall-resolved NLES method are very encouraging. The effects of grid-refinement and higher-order numerical scheme on the wall-resolved NLES approach are also discussed

Multi-point and multi-objective optimization of a centrifugal compressor impeller based on genetic algorithm

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis

Embedded large eddy simulation of transitional flow over NACA0012 aerofoil

An accurate computation of near-field unsteady turbulent flow around aerofoil is of outstanding importance for aerofoil trailing edge noise source prediction, which is a representative of main contributor to airframe noise and fan noise in modern commercial aircraft. In this study, an embedded large eddy simulation (ELES) is fully implemented in a separation-induced transitional flow over NACA0012 aerofoil at a moderate Reynolds number. It aims to evaluate the performance of the ELES method in aerodynamics simulation for wall-bounded aerospace flow in terms of accuracy, computational cost and complexity of implementation. Some good practice is presented including the special treatments at RANS-LES interface to provide more realistic turbulence generation in LES inflow. A comprehensive validation of the ELES results is performed by comparing with the experimental data and the wall-resolved large eddy simulation results. It is concluded that the ELES method could provide sufficient accuracy in the transitional flow simulations around aerofoil. It is proved to be a promising alternative to the pure LES for industrial flow applications involving wall boundary layer due to its significant computational efficiency

Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph

Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Additionally, there are far fewer anomalous nodes than normal nodes, indicating a long-tailed data distribution. To address these challenges, a unique algorithm,Decoupled Self-supervised Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection. DSLAD employs bilinear pooling and masked autoencoder as the anomaly discriminators. By decoupling anomaly discrimination and representation learning, a balanced feature space is constructed, in which nodes are more semantically discriminative, as well as imbalance issue can be resolved. Experiments conducted on various six benchmark datasets reveal the effectiveness of DSLAD

Large eddy simulation of airfoil self-noise using OpenFOAM

Purpose The purpose of this paper is to investigate airfoil self-noise generation and propagation by using a hybrid method based on the large-eddy simulation (LES) approach and Curle’s acoustic analogy as implemented in OpenFOAM. Design/methodology/approach Large-eddy simulation of near-field flow over a NACA6512-63 airfoil at zero angle of attack with a boundary layer trip at Rec = 1.9 × 105 has been carried out using the OpenFOAM® computational fluid dynamics (CFD) code. Calculated flow results are compared with published experimental data. The LES includes the wind tunnel installation effects by using appropriate inflow boundary conditions obtained from a RANS κ – ω SST model computation of the whole wind tunnel domain. Far-field noise prediction was achieved by an integral method based on Curle’s acoustic analogy. The predicted sound pressure levels are validated against the experimental data at various frequency ranges. Findings The numerical results presented in this paper show that the flow features around a NACA6512-63 airfoil have been correctly captured in OpenFOAM LES calculations. The mean surface pressure distributions and the local pressure peaks for the step trip setup agree very well with the experimental measurements. Aeroacoustic prediction using Curle’s analogy shows an overall agreement with the experimental data. The sound pressure level-frequency spectral analysis produces very similar data at low to medium frequency, whereas the experimentally observed levels are slightly over predicted at a higher frequency range. Practical implications This study has achieved and evaluated an alternative aeroacoustic simulation method based on the combination of LES with a simple Smagorinsky SGS model and Curle’s analogy, as implemented in the OpenFOAM CFD code. The unsteady velocity/pressure source data produced can be used for any simpler analytically based far-field noise prediction scheme. Originality/value A complete integration of the LES and Curle’s acoustic analogy for aeroacoustic simulations has been achieved in OpenFOAM. The capability and accuracy of the hybrid method are fully evaluated for high-camber airfoil self-noise predictions. Wind tunnel installation effects have been incorporated properly into the LES

Towards Real-World Burst Image Super-Resolution: Benchmark and Method

Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especially in realistic scenarios. In this paper, we establish a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to explore the faithful reconstruction of image details from multiple frames. Furthermore, we introduce a Federated Burst Affinity network (FBAnet) to investigate non-trivial pixel-wise displacements among images under real-world image degradation. Specifically, rather than using pixel-wise alignment, our FBAnet employs a simple homography alignment from a structural geometry aspect and a Federated Affinity Fusion (FAF) strategy to aggregate the complementary information among frames. Those fused informative representations are fed to a Transformer-based module of burst representation decoding. Besides, we have conducted extensive experiments on two versions of our datasets, i.e., RealBSR-RAW and RealBSR-RGB. Experimental results demonstrate that our FBAnet outperforms existing state-of-the-art burst SR methods and also achieves visually-pleasant SR image predictions with model details. Our dataset, codes, and models are publicly available at https://github.com/yjsunnn/FBANet.Comment: Accepted by ICCV202

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.Comment: Accepted by CIKM 202

Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation

Although face recognition starts to play an important role in our daily life, we need to pay attention that data-driven face recognition vision systems are vulnerable to adversarial attacks. However, the current two categories of adversarial attacks, namely digital attacks and physical attacks both have drawbacks, with the former ones impractical and the latter one conspicuous, high-computational and inexecutable. To address the issues, we propose a practical, executable, inconspicuous and low computational adversarial attack based on LED illumination modulation. To fool the systems, the proposed attack generates imperceptible luminance changes to human eyes through fast intensity modulation of scene LED illumination and uses the rolling shutter effect of CMOS image sensors in face recognition systems to implant luminance information perturbation to the captured face images. In summary,we present a denial-of-service (DoS) attack for face detection and a dodging attack for face verification. We also evaluate their effectiveness against well-known face detection models, Dlib, MTCNN and RetinaFace , and face verification models, Dlib, FaceNet,and ArcFace.The extensive experiments show that the success rates of DoS attacks against face detection models reach 97.67%, 100%, and 100%, respectively, and the success rates of dodging attacks against all face verification models reach 100%