Turbulence Characteristics in a Supersonic Cascade Wake Flow

Andrew, P.; Ng, Wing-Fai

doi:10.1115/93-gt-348

Cited by 6 publications

(6 citation statements)

References 17 publications

(36 reference statements)

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“…The turbulence level between the two peaks is still higher than that in the free stream. Similar wake characteristics are visible, to varying degrees, in others' results over a wide range of conditions [6][7][8][9]. Figure 1 shows that the axial velocity outside the wake is not constant; the velocity closer to the pressure side of the wake is higher.…”

Section: History Of Flow Passing a Fixed Pointsupporting

confidence: 49%

The nature of wakes in multistage axial flow compressors

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Elder

1999

Proceedings of the Institution of Mechanical Engineers, Part A:

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This paper describes the measurement and processing of unsteady turbulence data from a multistage low-speed compressor. In this study, a two-element hot-wire probe was traversed along a circumferential path downstream of the third rotor row of a large-scale low-speed four-stage facility. Simultaneous circumferential and axial velocity measurements were made. Three flow conditions were measured: peak efficiency with blade Re= 200 000, peak efficiency with blade Re= 55 000 and close to stall with blade Re= 200 000. Ensemble averaging has been used to deduce the periodic components of the velocity. In addition, measurements have been made of turbulence intensity, turbulence length scale and turbulent shear stress (Reynolds stress).

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Section: History Of Flow Passing a Fixed Pointsupporting

confidence: 49%

The nature of wakes in multistage axial flow compressors

Read

Elder

1999

Proceedings of the Institution of Mechanical Engineers, Part A:

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“…Following DEC and IDEC, the encoder and decoder are fully connected neural networks. The autoencoder has 8 layers with dimensions d -500 -500 -2000 -10 -2000 -500 -500 -d. Except for the bottleneck layer and the last layer, all the other ones use ReLu (47). During the pretraining stage, the autoencoder is trained adversarially end-to-end in competition with a critic network for 13 × 10 4 iterations.…”

Section: Methodsmentioning

confidence: 99%

Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction

Mrabah¹,

Khan

Ksantini³

et al. 2023

Preprint

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<p>In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concrete supervision suggests that smooth dynamics should be integrated. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that overcomes a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.</p>

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“…As shown in Table 1, the IRN without ESA showed a signi cant performance degradation for a parameter drop of approximately 10%, and the complete IRN showed signi cant performance improvements on the Set5, Set14, BSD100, Urban100 and Manga109 datasets. The results show that the ESA module can effectively improve the performance of SR. or LeakyReLU [47] as the activation function. Therefore, we investigate the effects of these three activation functions on the SR model.…”

Section: Model Analysismentioning

confidence: 99%

Lightweight Image Super-Resolution with ConvNeXt Residual Network

Zhang

Bai

Bing

et al. 2023

Neural Process Lett

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Single image super-resolution (SISR) based on convolutional neural networks has been very successful in recent years. However, as the computational cost is too high, making it di cult to apply to resource-constrained devices, a big challenge for existing approaches is to nd a balance between the complexity of the CNN model and the quality of the resulting SR. To solve this problem, various lightweight SR networks have been proposed. In this paper, we propose lightweight and e cient residual networks (IRN), which differ from previous lightweight SR networks that aggregate more powerful features by improving feature utilization through complex layer-connection strategies. The main idea is to simplify feature aggregation by using simple and e cient residual modules for feature learning, thus achieving a good trade-off between the computational cost of the model and the quality of the resulting SR. In addition, we revisit the impact of the activation function in the model and observe that different activation functions have an impact on the performance of the model. The experiment results show that IRN outperforms previous state-of-the-art methods in benchmark tests while maintaining a relatively low computational cost. The code will be available at https://github.com/kptx666/IRN.

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Turbulence Characteristics in a Supersonic Cascade Wake Flow

Cited by 6 publications

References 17 publications

The nature of wakes in multistage axial flow compressors

The nature of wakes in multistage axial flow compressors

Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction

Lightweight Image Super-Resolution with ConvNeXt Residual Network

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