The Effects of the Guide Cone on the Flow Field and Key Classification Performance of an Industrial-Scale Micron Air Classifier DOI Creative Commons
Nang X. Ho,

Hoi Thi Dinh,

Nhu The Dau

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 11504 - 11504

Published: Dec. 10, 2024

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

Language: Английский

Data interpolation and characteristic identification for particle segregation behavior and CNN-based dynamics correlation modeling DOI
Wei Wang, Yanze Wang, Shengchao Yang

et al.

Advanced Powder Technology, Journal Year: 2025, Volume and Issue: 36(2), P. 104761 - 104761

Published: Jan. 5, 2025

Language: Английский

Citations

0
Study on spatial flow field instability in a disturbing rotary centrifugal air classifier based on simulation and experimental methods DOI

Xinhao Li,

Runyu Liu,

Yuhan Liu

et al.

Powder Technology, Journal Year: 2025, Volume and Issue: unknown, P. 120990 - 120990

Published: April 1, 2025

Language: Английский

Citations

0
A Numerical Study on the Flow Field and Classification Performance of an Industrial-Scale Micron Air Classifier under Various Outlet Mass Airflow Rates DOI Open Access
Nang X. Ho,

Hoi Thi Dinh,

Nhu The Dau

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(9), P. 2035 - 2035

Published: Sept. 21, 2024

In this study, the gas−particle flow field in a real-size industrial-scale micron air classifier manufactured by Phenikaa Group using 3D transient simulations with FWC-RSM–DPM (Four-Way Coupling-Reynold Stress Model-Discrete Phase Model) ANSYS Fluent 2022 R2 and assistance of High-Performance Computing (HPC) systems is explored. A comparison among three coupling models carried out, highlighting significant influence interactions between solid gas phases on field. The complex two-phase flow, characterized formation multiple vortices different sizes, positions, rotation directions, successfully captured model classifier. Additionally, analyzing effects provides comprehensive understanding gas–solid classification mechanism. effect outlet mass airflow rate also investigated. classifier’s Key Performance Indicators (KPIs: d50, K, η, ΔP) constrained condition particle size distribution curve final product are used to evaluate efficiency. contributions work as follows: (i) simulation analysis conducted that highlights its advantages over lab-scale one; (ii) models, showing advancement four-way providing accurate results for phase particles; (iii) performances classified under rates addressed, from which optimal parameters can be selected design operation processes achieve required efficiency an

Language: Английский

Citations

2
Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier DOI
Jiale Yuan, Long Huang, Wenhao Li

et al.

Powder Technology, Journal Year: 2024, Volume and Issue: 447, P. 120223 - 120223

Published: Aug. 28, 2024

Language: Английский

Citations

1
Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information DOI Creative Commons
Ping Wang,

Guangzhong Hu,

Wenli Hu

et al.

Aerospace, Journal Year: 2024, Volume and Issue: 11(11), P. 871 - 871

Published: Oct. 24, 2024

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

Language: Английский

Citations

1
The Effects of the Guide Cone on the Flow Field and Key Classification Performance of an Industrial-Scale Micron Air Classifier DOI Creative Commons
Nang X. Ho,

Hoi Thi Dinh,

Nhu The Dau

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 11504 - 11504

Published: Dec. 10, 2024

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

Language: Английский

Citations

0