Amélioration de loi de paroi de Simulation aux Grands Échelles pour des applications aéroacoustiques

Le bruit de train d’atterrissage, généré par l’interaction de l’écoulement turbulent avec des corps solides et le décollement de la couche limite, sont les sources principales de bruit d’un avion en phase d’atterrissage. Les données expérimentales existantes ne sont pas suffisantes pour fournir les informations détaillées sur ces mécanismes de génération du bruit, et, depuis des années, les simulations numériques ont prouvé être un moyen efficace pour la prévision du bruit de ce type. Comparée à la Simulation Numérique Directe et aux modèles à moyenne de Reynolds, la Simulation aux Grandes Échelles (SGE), est un compromis efficace entre la précision des résultats et le coût de calcul. Cependant, la prévision de l’écoulement dans la couche limite turbulente reste un défi en SGE. En effet, les simulations existantes résolvent souvent les plus petites échelles aux parois, nécessitant alors un maillage très raffiné proche des surfaces, augmentant considérablement le coût de calcul. Par conséquent, un modèle de paroi qui est capable de reconstituer la contrainte de ci- saillement à la paroi sur la base de données extraites à une certaine distance au-dessus du paroi est nécessaire pour réduire les coûts. La revue de litérature met en évidence le modèle analytique proposé par Afzal [6] qui considère les effets de gradient de pression défavorables avec un surcoût négligeable. Outre les effets de gradient de pression, la couche limite lami- naire dans la partie amont du cylindre avant la transition vers la turbulence pose un autre problème. L’utilisation des lois de paroi pour la couche limite turbulente peut être imprécise et même changer complètement le régime d’écoulement. Pour surmonter cet obstacle, un modèle a été proposé dans ce travail pour estimer la contrainte de cisaillement de la paroi dans la couche limite laminaire lorsque le gradient de pression est important. Un capteur de transition basé sur le modèle de sous-maille a été utilisé pour déclencher l’utilisation de la loi de paroi turbulente. L’écoulement d’un cylindre circulaire dans le régime critique a été considérée comme une première validation de la loi d’Afzal et son extension. La valeur du nombre de Reynolds choisi correspond à la configuration de l’écoulement qui se trouve sur la jambe principale du train d’atterrissage LAGOON. L’écoulement complexe du cylindre est examiné par une SGE résolue, qui a ensuite été utilisée extensivement comme base de données de validation intense pour la loi d’Afzal et son extension. Tous les modèles de paroi sont capables de prédire correctement la moyenne et le RMS de la pression pariétale de la simulation de référence. L’utilisation des lois turbulentes sur toute la surface du cylindre entraîne une contrainte de cisaillement de la paroi inférieure dans la région laminaire et supérieure dans la région turbulente par rapport à la simulation résolue. L’extension de la loi d’Afzal fournit une prédiction améliorée dans les régions laminaires et turbulentes. Comme dans les systèmes du train d’atterrissage réels, il existe des interactions entre ses composants cylindriques, tels que la barre de traction avec la jambe principale. L’expérience canonique de barreau-profil pour une telle interaction, est donc sélectionnée comme deuxième cas de validation. Les simulations avec loi de paroi montrent des résultats acoustiques en champ lointain en bon accord avec les messures. Enfin, des SGE avec ces modèles de paroi ont été effectuées sur la configuration LA- GOON#1. En général, toutes les simulations prédisent précisément la pression moyenne pariétale. Cependant, l’application d’un modèle pour la couche limit turbulente partout prévoient des valeurs RMS et des spectres de pression plus élevés sur le périmètre de la roue depuis la première position de mesure expérimentale. Une transition plus précoce se produit systématiquement. L’extension de la loi d’Afzal retarde la transition et permet de mieux prédire le spectre de pression des parois, à la fois sur la surface de la roue et sur la jambe principale. Toutes les simulations sont capables de récupérer les spectres de pres- sion des parois dans la région séparée. Malgré ces divergences sur le développement de la couche limite, toutes les simulations prédisent une valeur OASPL acceptable dans le champ lointain, avec une amélioration notable de l’extension de la loi d’Afzal.Abstract: Airframe noise, generated through the interaction of turbulent flow with solid bodies such as landing gears becomes the main contributor to the airplane noise during approach and landing phases, since significant progress has been made on the noise reduction of turbo-jet engines. The existing experimental data haven’t been able to provide sufficiently detailed information on airframe noise mechanism and numerical simulations have been considered as an effective method in understanding both aerodynamic and noise generation mechanisms. Among different numerical methods, Large Eddy Simulation (LES) is considered as the best trade-off between predictive accuracy and computational cost. However, wall-bounded flows at high Reynolds number remain the most crucial challenge for LES since the resolution of the boundary layer dominates the computational cost which is close to Direct Numerical Simulations. One solution to overcome this difficulty is the use of wall models to provide boundary conditions for the LES simulation. The classical logarithmic-law is not suitable in simulations of landing gear flows in which the longitudinal adverse pressure gradient have significant effects. A new analytical wall model (proposed by Afzal [6]) which accounts for the adverse pressure gradient effect has been considered to tackle the noise prediction of a realistic landing gear. Another challenge of such flows is the presence of the laminar state boundary layer. The use of wall models for the turbulent boundary layer can be inaccurate and even change completely the flow regime. To overcome this obstacle, a model has been proposed in this work to approximate the wall-shear stress in the laminar boundary layer when important pressure gradient effects are present. A transition sensor based on the subgrid-scale model has been used to trigger the use of wall law for the turbulent boundary layer. The benchmark of the circular cylinder flow in the critical regime has been considered as a first validation for the above wall models. The flow at such a critical Reynolds number combines complex features: large favorable and adverse pressure gradient, separation and turbulence transition and flow reattachment. This flow regime is also the most relevant for landing gear flow applications because of the Reynolds number range involved on its components. The complex cylinder flow has been investigated by a wall-resolved LES which has then been used extensively as validation database for Afzal’s law and its extension. All the wall-models are able to predict the mean and the RMS wall pressure distributions of the reference simulation. The use of a turbulent wall model on the entire surface results in lower wall-shear stress in the laminar region and higher in the turbulent region compared with the resolved simulation. The extended model shows improved prediction of the shear stress in both laminar and turbulent regions. All of the models recover the dipole pattern with similar OASPL levels as in the wall-resolved simulation. Since in actual landing gear systems, there are actually interaction between various cylindrical components such as the tow bar with the main strut for instance. The canonical experiment for such an interaction, the rod-airfoil interaction is therefore selected as a second validation case. These models show reasonable aerodynamic and acoustic results compared with the experimental references. Finally, wall-modeled LES has been performed on a modeled landing gear configuration. In general, the mean wall pressure profiles are accurately predicted by all the simulations. However, turbulent wall models predict higher rms and spectra of pressure on the wheel perimeter since the first experimental measurement position. Earlier transition systematically occurs. The extended Afzal’s law delays the transition and shows improved prediction of the wall pressure spectra both on the wheel surface and on the main leg. All the models are able to recover the wall-pressure spectra in the separated region. Despite these discrepancies on the boundary layer development, all the simulations predict satisfactory OASPL in the far-field with a significant improvement from the extended Afzal’s law

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index

A COMPARISON OF HAZE REMOVAL ALGORITHMS AND THEIR IMPACTS ON CLASSIFICATION ACCURACY FOR LANDSAT IMAGERY

The quality of Landsat images in humid areas is considerably degraded by haze in terms of their spectral response pattern, which limits the possibility of their application in using visible and near-infrared bands. A variety of haze removal algorithms have been proposed to correct these unsatisfactory illumination effects caused by the haze contamination. The purpose of this study was to illustrate the difference of two major algorithms (the improved homomorphic filtering (HF) and the virtual cloud point (VCP)) for their effectiveness in solving spatially varying haze contamination, and to evaluate the impacts of haze removal on land cover classification. A case study with exploiting large quantities of Landsat TM images and climates (clear and haze) in the most humid areas in China proved that these haze removal algorithms both perform well in processing Landsat images contaminated by haze. The outcome of the application of VCP appears to be more similar to the reference images compared to HF. Moreover, the Landsat image with VCP haze removal can improve the classification accuracy effectively in comparison to that without haze removal, especially in the cloudy contaminated area

Detection of porcine parvovirus using a taqman-based real-time pcr with primers and probe designed for the NS1 gene

A TaqMan-based real-time polymerase chain reaction (PCR) assay was devised for the detection of porcine parvovirus (PPV). Two primers and a TaqMan probe for the non-structural protein NS1 gene were designed. The detection limit was 1 × 102 DNA copies/μL, and the assay was linear in the range of 1 × 102 to 1 × 109 copies/μL. There was no cross-reaction with porcine circovirus 2 (PCV2), porcine reproductive and respiratory syndrome virus (PRRSV), pseudorabies virus (PRV), classical swine fever virus (CSFV), or Japanese encephalitis virus (JEV). The assay was specific and reproducible. In 41 clinical samples, PPV was detected in 32 samples with the real-time PCR assay and in only 11 samples with a conventional PCR assay. The real-time assay using the TaqMan-system can therefore be practically used for studying the epidemiology and management of PPV

Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation

Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent works explore vision-language pre-training to handle this task, but suffer from unrealistic assumptions in practical scenarios, i.e., low-quality textual category names. For example, this paradigm assumes that new textual categories will be accurately and completely provided, and exist in lexicons during pre-training. However, exceptions often happen when meet with ambiguity for brief or incomplete names, new words that are not present in the pre-trained lexicons, and difficult-to-describe categories for users. To address these issues, this work proposes a novel decomposition-aggregation framework, inspired by human cognition in understanding new concepts. Specifically, in the decomposition stage, we decouple class names into diverse attribute descriptions to enrich semantic contexts. Two attribute construction strategies are designed: using large language models for common categories, and involving manually labelling for human-invented categories. In the aggregation stage, we group diverse attributes into an integrated global description, to form a discriminative classifier that distinguishes the target object from others. One hierarchical aggregation is further designed to achieve multi-level alignment and deep fusion between vision and text. The final result is obtained by computing the embedding similarity between aggregated attributes and images. To evaluate the effectiveness, we annotate three datasets with attribute descriptions, and conduct extensive experiments and ablation studies. The results show the superior performance of attribute decomposition-aggregation

Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks

Transforming the Interactive Segmentation for Medical Imaging

The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.Comment: Accepted to MICCAI 202

Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models

When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we investigate simple, yet effective approaches for adapting the pre-trained foundation models to the downstream task of interest, namely, open-vocabulary semantic segmentation. To this end, we make the following contributions: (i) we introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept through a handful of image segmentation data. As a consequence, the model gains the capability of zero-shot transfer to segment novel categories; (ii) without loss of generality, we experiment on a broad range of self-supervised models that have been pre-trained with different schemes, e.g. visual-only models (MoCo v3, DINO), language-only models (BERT), visual-language model (CLIP), and show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data; (iii) we conduct thorough ablation studies to analyze the critical components in our proposed Fusioner, while evaluating on standard benchmarks, e.g. PASCAL-5i and COCO-20i , it surpasses existing state-of-the-art models by a large margin, despite only being trained on frozen visual and language features; (iv) to measure the model's robustness on learning visual-language correspondence, we further evaluate on synthetic dataset, named Mosaic-4, where images are constructed by mosaicking the samples from FSS-1000. Fusioner demonstrates superior performance over previous models.Comment: BMVC 2022 Ora

Genetic Basis and Expression Pattern Indicate the Biocontrol Potential and Soil Adaption of Lysobacter capsici CK09

Lysobacter species have attracted increasing attention in recent years due to their capacities to produce diverse secondary metabolites against phytopathogens. In this research, we analyzed the genomic and transcriptomic patterns of Lysobacter capsici CK09. Our data showed that L. capsici CK09 harbored various contact-independent biocontrol traits, such as fungal cell wall lytic enzymes and HSAF/WAP-8294A2 biosynthesis, as well as several contact-dependent machineries, including type 2/4/6 secretion systems. Additionally, a variety of hydrolytic enzymes, particularly extracellular enzymes, were found in the L. capsici CK09 genome and predicted to improve its adaption in soil. Furthermore, several systems, including type 4 pili, type 3 secretion system and polysaccharide biosynthesis, can provide a selective advantage to L. capsici CK09, enabling the species to live on the surface in soil. The expression of these genes was then confirmed via transcriptomic analysis, indicating the activities of these genes. Collectively, our research provides a comprehensive understanding of the biocontrol potential and soil adaption of L. capsici CK09 and implies the potential of this strain for application in the future