Characteristics of unsteady flow field and flow-induced noise for an axial cooling fan used in a rack mount server computer

Lim, Tae-Gyun; Jeon, Wan-Ho; Minorikawa, Gaku

doi:10.1007/s12206-016-0931-x

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The aeroacoustic characteristics of cooling fans are determined by their aerodynamics, such as flow separation, secondary flow and other special flow structures. A large number of studies have been performed on aerodynamic characteristics [3][4][5][6][7][8][9] and aeroacoustic characteristics [10][11][12][13][14][15][16]. For example, Nashimoto et al [3] used phase-averaged PIV measurement and found that the separation and reattachment of the flow occurred on the suction surface at the leading edge of the blade.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation on the Aerodynamic and Aeroacoustic Characteristics in New Energy Vehicle Cooling Fan with Shroud

Huang,

Xu,

Wang

et al. 2024

Processes

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The cooling fan is one of the important noise sources for new energy vehicles, and the research on its aerodynamic and aeroacoustic characteristics is of great help to improve the noise, vibration and harshness performance of new energy vehicles. However, most of these studies focus on the impeller, and little consideration has been given to the study of the shroud. Based on the coupling calculation method of large eddy simulation and the Ffowcs-Williams and Hawkings acoustics model, the aerodynamic and aeroacoustic characteristics in a cooling fan with the shroud are investigated at flow rates from 0.623 kg/s to 1.019 kg/s (where 0.865 kg/s is the flow rate corresponding to the best efficiency point). The accuracy of numerical simulation results is verified by the grid independence verification and the comparison of experimental data. Research shows that several large-scale vortex structures are observed in the clearance between the impeller and the shroud. The maximum peak-to-peak values of pressure fluctuation at different flow rates occur in the intermediate section or outlet section of the shroud. Although the shroud contributes relatively less to the far field noise, its different distribution may change the position of the maximum sound pressure level. The dominant frequency of pressure fluctuation equals the blade passage frequency (BPF) and the maximum SPL is around the BPF, both of which are independent of flow rates. The maximum SPL and the amplitude of the dominant frequency decrease as the flow rate increases.

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Section: Introductionmentioning

confidence: 99%

Numerical Investigation on the Aerodynamic and Aeroacoustic Characteristics in New Energy Vehicle Cooling Fan with Shroud

Huang,

Xu,

Wang

et al. 2024

Processes

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“…Deep learning models can forecast the remaining useful life of critical components, such as fan blades or bearings. By analyzing historical data and patterns of wear and tear, these models can provide maintenance schedules that are more precise and cost-effective [16]. This approach helps avoid both premature maintenances which can costly and unplanned downtime due to component failures.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimal deep learning with IoT based smart based monitoring and maintenance system for axial flow fan using feature optimization

Andreica,

Offenberg

2023

J. Adv. Zool.

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Axial flow fan is a mechanical fan that generates airflow in the same direction as its rotational axis. These fans find widespread use in various applications, including ventilation, cooling, and air circulation across industrial, commercial, and residential settings. However, designing these fans can be challenging in the fan manufacturing industry due to the need to accommodate diverse operating conditions. This complexity arises from the fact that multiple design parameters significantly influence fan performance, requiring careful consideration and optimization to ensure efficient operation across various scenarios. In this paper, we present a technique for monitoring and maintenance of axial flow fans using hybrid optimal deep learning with IoT system. Our method leverages the pre-trained U-Net architecture to extract hidden features effectively from the dataset. Furthermore, we introduce an improved triple tree-seed optimization (IT2SO) algorithm for feature optimization, which identifies the most optimal features among the extracted ones. To make informed decisions about axial flow fan process monitoring, we propose the deep boosted hybrid learning (DBHL) technique as the decision model to maintain the proper operation. To validate the effectiveness of proposed IT2SO+DBHL technique, we have conducted experiments using the air movement and control association international (AMCA) dataset. The results demonstrate the superior performance of our monitoring approach compared to existing techniques across various evaluation measures.

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Computational study for noise reduction and characteristic of unsteady flow field/flow-induced noise generated in a small radial fan

Lim¹,

Jeon²,

Minorikawa

2017

J Mech Sci Technol

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Characteristics of unsteady flow field and flow-induced noise for an axial cooling fan used in a rack mount server computer

Cited by 4 publications

References 16 publications

Numerical Investigation on the Aerodynamic and Aeroacoustic Characteristics in New Energy Vehicle Cooling Fan with Shroud

Numerical Investigation on the Aerodynamic and Aeroacoustic Characteristics in New Energy Vehicle Cooling Fan with Shroud

Hybrid optimal deep learning with IoT based smart based monitoring and maintenance system for axial flow fan using feature optimization

Computational study for noise reduction and characteristic of unsteady flow field/flow-induced noise generated in a small radial fan

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