Numerical Simulation and Optimization of Oil Jet Lubrication for Rotorcraft Meshing Gears

Dai, Yu; Wu, Whang-Zong; Zhou, Hong-liang; Zhang, J.; Y., F.

doi:10.2507/ijsimm17(2)co6

Cited by 34 publications

(29 citation statements)

References 12 publications

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“…Any fluid flows in CFD simulations meet three basic governing equations of fluid mechanics, including the continuity equation, the momentum equation, and the energy equation [36][37][38], which can be respectively expressed as Equations (13), (14), and (15):…”

Section: Fluid Governing Equationmentioning

confidence: 99%

Numerical Simulation Investigation on the Windage Power Loss of a High-Speed Face Gear Drive

Dai

Zhu

et al. 2019

Energies

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Reducing the energy consumption and improving the efficiency of high-speed transmission systems are increasingly common goals; the windage power loss is not negligible in these methods. In this work, the multi-reference frame (MRF) and periodic boundary conditions (PBC) based on the computational fluid dynamics (CFD) method were adopted to investigate the windage phenomena of a single face gear with and without a shroud, and the impact of the gear speed on the windage power loss was analyzed. Furthermore, the effects on the distribution of static pressure due to the distances between the shroud and the gear body in different directions, including the outer radius direction, the inner radius direction, and the addendum direction were investigated. The results indicate that the gear speed significantly affected the windage loss, as the higher the gear speed was, the greater the windage power loss. Additionally, the shroud could effectively reduce the windage power loss, where the optimal distance from the addendum to the shroud was not the minimum distance; however, for the distances from the shroud to the inner radius and the outer radius, the smaller the distance was, the smaller the windage loss. The results can provide a theoretical basis and technical reference for reducing the windage power loss of various face gear drives.

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Section: Fluid Governing Equationmentioning

confidence: 99%

Numerical Simulation Investigation on the Windage Power Loss of a High-Speed Face Gear Drive

Dai

Zhu

et al. 2019

Energies

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“…The two datasets differ in size, sequence length, sequencing method and alignment algorithm. In order to verify the universality and stability of the computing model for different data sets, the choice of experimental data should be representative [6,7]. Therefore, two data sets with different characteristics are selected in this experiment.…”

Section: Experimental Designmentioning

confidence: 99%

Gene Sequences Parallel Alignment Model Based on Multiple Inputs and Outputs

Feng¹,

Gao²

2019

INT J COMPUT COMMUN

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Bioinformatics computing is a kind of big data processing problem, which usually has the characteristics of large data scale, large computational load and long computational time. Therefore, the use of big data technology in bioinformatics computing has gradually become a research hotspot, and using Hadoop for gene sequence alignment is one of it. It is a common way to use various tools to complete a job in the field of Biocomputing. In most studies of parallel alignment of gene sequences using Hadoop, third-party tools are also needed. However, there are few methods using Hadoop independently to complete gene sequences alignment. Adding data processing with other tools to Hadoop workflow not only affects the improvement of computing performance, but also complicates the application. In this paper, a parallel alignment model of gene sequences based on multiple inputs and outputs is proposed, which can independently complete parallel alignment of gene sequences in Hadoop platform without using other tools. This model not only simplifies the process flow of gene sequence alignment, but also improves the performance compared with other methods. This paper describes in detail the method of manipulating gene sequences with multiple inputs and outputs modes on Hadoop platform and the design of a computing model based on this method, and proves the superiority of this model through experiments.

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“…For video flow, Shot Boundary Detection is conducted first and the video is segmented into short takes with video segmentation algorithms, such as pixel algorithm, histogram algorithm, X2 histogram algorithm, X2 histogram block algorithm and contour a boundary ROC (Rate of Change) algorithm; then, the original motion trails of video object are extracted by use of moving object tracking algorithms, such as mean shift algorithm, object tracking based on Kalman filter, object tracking based on particle filter and algorithm based on modeling of moving object, with the longest trail to be processed and information extracted therefrom, including motion direction and slop of motion trail curve. At last, the said motion action will be marked by hand to extract the video verb semantic label [14][15][16][17][18][19][20][21]. Also, some other researchers proposed that semantic clews of multiple event recognitions should be fused by means of a deep-level learning strategy so that the issue of recognition would be solved by answering how to jointly analyse human actions, objects and scenes.…”

Section: Video Semantic Analysis and Relevant Researchmentioning

confidence: 99%

Application of Video Scene Semantic Recognition Technology in Smart Video

Qin

Kang

2018

Teh. vjesn.

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Video behaviour recognition and semantic recognition understanding are important components of intelligent video analytics. Traditionally, human behaviour recognition has met problems of low recognition efficiencies and poor accuracies. For example, most existing behaviour recognition methods use the video frames obtained by even segmentation and fixed sampling as the input, which may lose important information between sampling intervals, fail to identify the key frames of the video segments and make use of the contextual semantics to understand current behaviour. In order to improve the semantic understanding capacity and efficiency of video segments, this paper adopts a 3-layer semantic recognition approach based on key frame extraction. First, it completes the segmentation for video recognition at the bottom layer, extracts the key frames in the video segments, primarily understands basic semantics of the persons' identifications, behaviours and environment, and then introduces the primarily acquired information into the middle layer for semantic integration, and through the integration of various semantics, adopts the loss function to learn the latent relationship between different modal semantics, to enhance the integrating capacity and the robustness of the character semantic integration, and finally, by overall fine tuning, semantic recognition and adjusting all the parameters of the network, completes the semantic recognition task of the video scenario. This method enjoys higher recognition accuracies based on certain datasets, capable of effectively recognizing the semantics of characters and behaviours in videos. Through practical testing, the adoption of the algorithm integrating key frame extractions with the video scene semantic recognition has improved the recognition accuracy and effect of the video character semantics.

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Numerical Simulation and Optimization of Oil Jet Lubrication for Rotorcraft Meshing Gears

Cited by 34 publications

References 12 publications

Numerical Simulation Investigation on the Windage Power Loss of a High-Speed Face Gear Drive

Numerical Simulation Investigation on the Windage Power Loss of a High-Speed Face Gear Drive

Gene Sequences Parallel Alignment Model Based on Multiple Inputs and Outputs

Application of Video Scene Semantic Recognition Technology in Smart Video

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