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Gedeon streaming, a time-averaged mass flow circulating throughout the loop configuration in the oscillating flow, can considerably deteriorate the thermal efficiency of traveling-wave thermoacoustic engine. Jet pump is characterized by a tapered channel with different inlet and outlet areas. It can produce a time-averaged pressure drop to suppress Gedeon streaming. In this study, the suppression mechanism of the jet pump in oscillatory flow is investigated, and the emphasis is put on the effect of cross-sectional area ratio on the performance of jet pumps with different taper angles. An experimental apparatus has been built to measure the time-averaged pressure induced by the jet pump. When the taper angle ranges from 3° to 9°, the time-averaged pressure drop and working efficiency can be improved by increasing cross-sectional area ratio, and the change of taper angle has little effect on the performance of jet pumps. However, as the taper angle increases beyond 9°, the performance of jet pump with a large cross-sectional area ratio degrades considerably. In addition, the time-averaged pressure drops induced by the jet pumps with different cross-sectional area ratios are similar, and the taper angle becomes more dominant for jet pump’s performance. [ABSTRACT FROM AUTHOR]

As an unnoticed specialized task in image restoration, rain-by-snow weather removal aims to eliminate the complicated coexisting rain streaks and snow particles. In this work, we propose a simple attention-based sampling cross-hierarchy Transformer (SASCFormer). Initially, we explore the proximity of convolution network and Transformer in hierarchical architectures and experimentally find they perform approximately for intra-stage feature representation. On this basis, we utilize a Transformer-like convolution block (TCB) to replace the computation-heavy self-attention while preserving the attention characteristics for adapting to the input content. Meanwhile, we demonstrate that cross-stage sampling progression is critical for the performance improvement in rain-by-snow weather removal, and propose a global–local self-attention sampling mechanism (GLSASM) that samples the features while preserving both the global and local dependencies. Finally, we synthesize two novel rain-by-snow weather-degraded benchmarks, RSCityscapes and RS100K datasets. Extensive experiments verify that our proposed SASCFormer achieves the best trade-off between the performance and inference time. In particular, our approach advances existing methods by 1. 14 dB ∼ 4. 89 dB in peak signal-to-noise ratio. Related resources are available at https://github.com/chdwyb/Rain-by-snow. • We first focus on rain-by-snow weather-degraded image restoration. • We propose a simple attention-based sampling cross-hierarchy Transformer. • We demonstrate that cross-stage progression is crucial for performance improvement. • Two novel rain-by-snow datasets are synthesized for rain-by-snow weather removal. [ABSTRACT FROM AUTHOR]

High-temperature components utilised in practical service are often subjected to multiaxial non-proportional loading due to their complex geometry. In the present study, a set of strain-controlled fatigue tests were conducted on P92 steel at 600 °C, including uniaxial, multiaxial proportional, and multiaxial non-proportional loading conditions. Results reveal that the axial and shear stresses exhibit varied responses to the different non-proportionalities, with proportional loading resulting in the shortest fatigue life when the strain ratio is small. The non-proportional loading induces accelerated cyclic softening at axial direction while the shear stress is enhanced. Electron backscatter diffraction (EBSD) and transmission electron microscope (TEM) analysis indicate that the increased role of shear stress promotes the dislocation motion from the planar slip to cross-slip under non-proportional loading, which in turn facilitates the microstructure evolution. It is also important to note that the evident development of microstructure and the combined effect of more crack initiations and more secondary cracks contribute to the lowest fatigue life under proportional loading. • The non-proportional loading accelerates axial cyclic softening while the shear stress is enhanced. • The lowest fatigue life of P92 steel appears under proportional loading when the strain ratio is small. • The increased role of shear stress promotes the dislocation motion from the planar slip to cross-slip. [ABSTRACT FROM AUTHOR]

The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significant proportion of these methods tend to overlook the precise reconstruction of the intricate details. In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images. This approach comprises two key modules, a rain streaks removal network (R 2 Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images. Firstly, we introduce a dilated dense residual block (DDRB) within R 2 Net, enabling the aggregation of high-level and low-level features. Secondly, an enhanced residual pixel-wise attention block (ERPAB) is integrated into DRNet to facilitate the incorporation of contextual information. To further enhance the fidelity of our approach, we employ a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images. Extensive experiments conducted on publicly available benchmarks demonstrates the noteworthy efficiency and effectiveness of our proposed DPENet. The source code and pre-trained models are currently available at https://github.com/chdwyb/DPENet. • Dual-stage deraining network performs better than the single-stage network. • Multi-level feature fusion is significant for rain streaks removal. • Contextual information aggregation facilitates textural details reconstruction. [ABSTRACT FROM AUTHOR]

Image restoration under hazy weather condition, which is called single image dehazing, has been of significant interest for various computer vision applications. In recent years, deep learning-based methods have achieved success. However, existing image dehazing methods typically neglect the hierarchy of features in the neural network and fail to exploit their relationships fully. To this end, we propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD), which is based on feature fusion and contrastive learning strategies. HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL). Specifically, the core design in the HDN is a hierarchical interaction module, which utilizes multi-scale activation to revise the feature responses hierarchically. To cooperate with the training of HDN, we propose HCL which performs contrastive learning on hierarchically paired exemplars, facilitating haze removal. Extensive experiments on public datasets, RESIDE, HazeRD, and DENSE-HAZE, demonstrate that HCD quantitatively outperforms the state-of-the-art methods in terms of PSNR, SSIM and achieves better visual quality. • Our method employs contrastive learning to enhance feature representation ability. • Hierarchical interaction module allows information flow to exchange efficiently across different branches. • Hierarchical contrastive loss effectively guides the model to learn the valid features for image dehazing. [ABSTRACT FROM AUTHOR]