334 research outputs found
AmeĢlioration de loi de paroi de Simulation aux Grands Ćchelles pour des applications aeĢroacoustiques
Le bruit de train dāatterrissage, geĢneĢreĢ par lāinteraction de lāeĢcoulement turbulent avec des corps solides et le deĢcollement de la couche limite, sont les sources principales de bruit dāun avion en phase dāatterrissage. Les donneĢes expeĢrimentales existantes ne sont pas suffisantes pour fournir les informations deĢtailleĢes sur ces meĢcanismes de geĢneĢration du bruit, et, depuis des anneĢes, les simulations numeĢriques ont prouveĢ eĢtre un moyen efficace pour la preĢvision du bruit de ce type. CompareĢe aĢ la Simulation NumeĢrique Directe et aux modeĢles aĢ moyenne de Reynolds, la Simulation aux Grandes EĢchelles (SGE), est un compromis efficace entre la preĢcision des reĢsultats et le couĢt de calcul. Cependant, la preĢvision de lāeĢcoulement dans la couche limite turbulente reste un deĢfi en SGE. En effet, les simulations existantes reĢsolvent souvent les plus petites eĢchelles aux parois, neĢcessitant alors un maillage treĢs raffineĢ proche des surfaces, augmentant consideĢrablement le couĢt de calcul.
Par conseĢquent, un modeĢle de paroi qui est capable de reconstituer la contrainte de ci- saillement aĢ la paroi sur la base de donneĢes extraites aĢ une certaine distance au-dessus du paroi est neĢcessaire pour reĢduire les couĢts. La revue de liteĢrature met en eĢvidence le modeĢle analytique proposeĢ par Afzal [6] qui consideĢre les effets de gradient de pression deĢfavorables avec un surcouĢt neĢgligeable. 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 probleĢme. Lāutilisation des lois de paroi pour la couche limite turbulente peut eĢtre impreĢcise et meĢme changer compleĢtement le reĢgime dāeĢcoulement. Pour surmonter cet obstacle, un modeĢle a eĢteĢ proposeĢ 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 baseĢ sur le modeĢle de sous-maille a eĢteĢ utiliseĢ pour deĢclencher lāutilisation de la loi de paroi turbulente.
LāeĢcoulement dāun cylindre circulaire dans le reĢgime critique a eĢteĢ consideĢreĢe comme une premieĢre validation de la loi dāAfzal et son extension. La valeur du nombre de Reynolds choisi correspond aĢ la configuration de lāeĢcoulement qui se trouve sur la jambe principale du train dāatterrissage LAGOON. LāeĢcoulement complexe du cylindre est examineĢ par une SGE reĢsolue, qui a ensuite eĢteĢ utiliseĢe extensivement comme base de donneĢes de validation intense pour la loi dāAfzal et son extension. Tous les modeĢles de paroi sont capables de preĢdire correctement la moyenne et le RMS de la pression parieĢtale de la simulation de reĢfeĢrence. Lāutilisation des lois turbulentes sur toute la surface du cylindre entraiĢne une contrainte de cisaillement de la paroi infeĢrieure dans la reĢgion laminaire et supeĢrieure dans la reĢgion turbulente par rapport aĢ la simulation reĢsolue. Lāextension de la loi dāAfzal fournit une preĢdiction ameĢlioreĢe dans les reĢgions laminaires et turbulentes. Comme dans les systeĢmes du train dāatterrissage reĢels, il existe des interactions entre ses composants cylindriques, tels que la barre de traction avec la jambe principale. LāexpeĢrience canonique de barreau-profil pour une telle interaction, est donc seĢlectionneĢe comme deuxieĢme cas de validation. Les simulations avec loi de paroi montrent des reĢsultats acoustiques en champ lointain en bon accord avec les messures.
Enfin, des SGE avec ces modeĢles de paroi ont eĢteĢ effectueĢes sur la configuration LA- GOON#1. En geĢneĢral, toutes les simulations preĢdisent preĢciseĢment la pression moyenne parieĢtale. Cependant, lāapplication dāun modeĢle pour la couche limit turbulente partout preĢvoient des valeurs RMS et des spectres de pression plus eĢleveĢs sur le peĢrimeĢtre de la roue depuis la premieĢre position de mesure expeĢrimentale. Une transition plus preĢcoce se produit systeĢmatiquement. Lāextension de la loi dāAfzal retarde la transition et permet de mieux preĢdire le spectre de pression des parois, aĢ la fois sur la surface de la roue et sur la jambe principale. Toutes les simulations sont capables de reĢcupeĢrer les spectres de pres- sion des parois dans la reĢgion seĢpareĢe. MalgreĢ ces divergences sur le deĢveloppement de la couche limite, toutes les simulations preĢdisent une valeur OASPL acceptable dans le champ lointain, avec une ameĢlioration notable de lāextension de la loi dāAfzal.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 havenāt 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 Afzalās 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 Afzalās 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 Afzalās law
Resilience-Assuring Hydrogen-Powered Microgrids
Green hydrogen has shown great potential to power microgrids as a primary
source, yet the operation methodology under extreme events is still an open
area. To fill this gap, this letter establishes an operational optimization
strategy towards resilient hydrogen-powered microgrids, where the frequency and
voltage regulation characteristics of hydrogen sources under advanced controls
are accurately represented by piecewise linear constraints. The results show
that the new operation approach can output a safety-assured operation plan with
rational power change distribution and reduced frequency and voltage variation
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 scenariosā1.5 Ā°C and 2.0 Ā°Cāin 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 Ā°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 Ā°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 Ā°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 Ā°C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the PenmanāMonteith (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 Ć 102 DNA copies/Ī¼L, and the assay was linear in the range of 1 Ć 102 to 1 Ć 109 copies/Ī¼L. 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
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