Dynamic characteristics of the internal flow field of a rotary centrifugal air classifier and pressure prediction through attention mechanism-enhanced CNN-LSTM

Huang, Long; Li, Wenhao; Liu, Runyu; Yuan, Jiale; Li, Xinhao; Jiang, Haishen; Duan, Chenlong

doi:10.1016/j.apt.2024.104578

Advanced Powder Technology

2024

DOI: 10.1016/j.apt.2024.104578

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Dynamic characteristics of the internal flow field of a rotary centrifugal air classifier and pressure prediction through attention mechanism-enhanced CNN-LSTM

Long Huang,

Wenhao Li,

Runyu Liu

et al.

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2024

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Cited by 4 publications

References 35 publications

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Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier

Yuan,

Huang,

et al. 2024

Powder Technology

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Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier

Yuan,

Huang,

et al. 2024

Powder Technology

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Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information

Wang,

Hu,

et al. 2024

Aerospace

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The rapid reconstruction of the internal flow field within pressure vessel equipment based on features from limited detection points was of significant value for online monitoring and the construction of a digital twin. This paper proposed a surrogate model that combined Proper Orthogonal Decomposition (POD) with deep learning to capture the dynamic mapping relationship between sensor monitoring point information and the global flow field state during equipment operation, enabling rapid reconstruction of the temperature field and velocity field. Using POD, the order of the tested temperature field was reduced by 99.75%, and the order of the velocity field was reduced by 99.13%, effectively decreasing the dimensionality of the flow field. Our analysis revealed that the first modal coefficient of the temperature field snapshot data, after modal decomposition, had a higher energy proportion compared to that of the velocity field snapshot data, along with a more pronounced marginal effect. This indicates that more modes need to be retained for the velocity field to achieve a higher total energy proportion. By constructing a CSSA-BP model to represent the mapping relationship between the modal coefficients of the temperature and velocity fields and the data collected from the detection points, a comparison was made with the BP method in reconstructing the temperature field of a shell-and-tube heat exchanger. The CSSA-BP method yielded a maximum mean squared error (MSE) of 9.84 for the reconstructed temperature field, with a maximum mean absolute error (MAE) of 1.85. For the velocity field, the maximum MSE was 0.0135 and the maximum MAE was 0.0728. The global maximum errors for the reconstructed temperature field were 4.85%, 3.65%, and 4.29%, respectively. The global maximum errors for the reconstructed velocity field were 17.72%, 11.30%, and 16.79%, indicating that the model established in this study has high accuracy. Conventional CFD simulation methods require several hours, whereas the reconstruction model proposed here can rapidly reconstruct the flow field within 1 min after training is completed, significantly reducing reconstruction time. This work provides a new method for quickly obtaining the internal flow field state of pressure vessel equipment under limited detection points, offering a reference for online monitoring and the development of digital twins for pressure vessel equipment.

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The Effects of the Guide Cone on the Flow Field and Key Classification Performance of an Industrial-Scale Micron Air Classifier

Ho,

Dinh,

Dau

2024

Applied Sciences

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In this study, the effects of the structural parameters (SPs) of the guide cone, such as the surface inclination and the material recirculation gap size, on the two-phase flow field and classification performance of a real-sized industrial-scale micron air classifier were investigated. This was achieved using the two-way coupling of a computational fluid dynamics–discrete phase model in ANSYS 2022 R2, with the assistance of a high-performance system (HPC). The objective of this study was to determine the optimal SPs of the guide cone so as to achieve the best classification efficiency and satisfy the required particle size distribution curve, named the know-how curve (KHC), for the particle size range (0 ÷ 400 μm) used in producing quartz-based artificial stone. The bottom diameter (d) of the guide cone (CHL) was altered while keeping the outer diameter of the feeding tube unchanged. As a consequence, the material recirculation gap size was changed, and the size, shape, position, and rotational direction of the vortices formed in the secondary classification space and classification chamber were also changed. These vortices significantly affected the classification performance. Specifically, the classifiers with different guide cone structures, named CHL1, CHL2, CHL3, and CHL4, yielded Newton efficiencies of 75.06%, 87.26%, 95.5%, and 94.02%, respectively. According to the simulation results, the best guide cone structure is recommended to satisfy objectives such as (i) the highest classification efficiency, the smallest cut size, and the smallest classification sharpness index and (ii) those in (i) under the constraint of the required KHC.

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Dynamic characteristics of the internal flow field of a rotary centrifugal air classifier and pressure prediction through attention mechanism-enhanced CNN-LSTM

Cited by 4 publications

References 35 publications

Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier

Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier

Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information

The Effects of the Guide Cone on the Flow Field and Key Classification Performance of an Industrial-Scale Micron Air Classifier

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