Multiscale and multilayer structural modeling and simulation on drying of grain packing porous media

Yuan, Yuejin; Tan, Libin; Zhao, Zhe; Xu, Yingying; Zhao, Yu; Yuan, Yueding

doi:10.1080/07373937.2016.1141213

Cited by 14 publications

(7 citation statements)

References 33 publications

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“…The rice layer was model as a porous medium with direction of flow only in the direction y, with permeability of 0.001mm 2 [7] and coefficient of resistance of 0.5m -1 [5].…”

Section: Methodsmentioning

confidence: 99%

Proposal for improving of paddy rice drying process in dryers inclined pools

Duarte¹,

Gamboa²,

López³

2021

Sci. tech

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The operation of inclined type dryers for paddy rice is studied to improve their operation and efficiency, considering the variables of drying time, humidity distribution in the rice layer after drying and air velocities within the plenum. Simulating the distribution and velocities of the air flow before crossing the rice layer, the outlet ducts to the plenum are relocated and dampers are placed to distribute the flow evenly. On the other hand, it is proposed to replace the flat-type mesh with a zigzag-type mesh. To verify the proposed modifications, a scale model was built to determine the humidity and drying time variation inside the rice layer, obtaining more uniform moisture percentages within the rice layer and a decrease in drying time.

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“…The rice layer was model as a porous medium with direction of flow only in the direction y, with permeability of 0.001mm 2 [7] and coefficient of resistance of 0.5m -1 [5].…”

Section: Methodsmentioning

confidence: 99%

Proposal for improving of paddy rice drying process in dryers inclined pools

Duarte¹,

Gamboa²,

López³

2021

Sci. tech

View full text Add to dashboard Cite

show abstract

“…Li et al (2023) considered soybeans as spherical particles, and conducted a numerical simulation solution to the convective drying of grain piles. Yuan et al (2016) developed a multi-scale drying model of grain pile using a pore network approach. However, the established multi-layer structured network model represents the regular arrangement of pores and differs significantly from the complex pore structure of actual porous media.…”

Section: Introductionmentioning

confidence: 99%

Study on heat and moisture transfer of nonspherical rice during hot‐air drying in thin‐layer grain pile

Li,

Yang,

Wang

et al. 2024

J Food Process Engineering

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To investigate fluid flow and coupled heat‐moisture transfer in the hot‐air drying of grain piles, this study integrated the discrete‐element method with computational fluid dynamics. A model for a thin‐layer grain pile, consisting of nonspherical rice particles represented by 13 spheres, was developed. Using a heat‐moisture transfer model based on fluid–solid coupling, the drying process with hot air was simulated and validated against experimental data. Finally, by controlling the drying temperature and the initial moisture content of rice, the variations in temperature and moisture content during the hot‐air drying process were explored. The numerical results indicated that the nonuniform pore structure within the grain pile, gaps on the surface of nonspherical rice, and the wall effect led to complex airflow patterns, resulting in the formation of dead zones within the grain pile. The oscillation frequency and amplitude of radial porosity near the wall were relatively large, and the minimum porosity occurred at a radial distance of 1/2dg. Heat and moisture transfer rates between the rice and drying air were influenced by the temperature and moisture concentration differences. During the drying process, variations in temperature and moisture content were observed among rice particles. Limited by the moisture diffusion coefficient, there existed a notable disparity in moisture content between the interior and surface of the rice. The results also showed that the curvature of the drying curve in the grain pile varied and was influenced by the drying conditions, especially under the high‐temperature drying condition of 60°C.Practical ApplicationsIn this study, a grain pile model for nonspherical rice particles composed of 13 spheres was developed based on the discrete‐element method and computational fluid dynamics. Furthermore, in accordance with the heat and mass transfer mechanism of fluid–solid coupling, a heat‐moisture transfer model for the hot‐air drying of the grain pile was constructed. This was done to deeply explore the internal fluid dynamic characteristics and the heat‐moisture transfer within the grain pile during the drying process. The newly established model can be widely applied to numerical simulation studies of drying and also has reference value for the design of drying systems.

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“…The results indicated that the pore area and pore diameter of the piled grain gradually decreased with increasing vertical pressure. Currently, numerous researchers are extensively utilizing numerical simulation methods to investigate porous media and model them with the assistance of rapidly advancing computer technology [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

Chen,

Li,

Zheng

et al. 2024

Applied Sciences

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The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.

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Multiscale and multilayer structural modeling and simulation on drying of grain packing porous media

Cited by 14 publications

References 33 publications

Proposal for improving of paddy rice drying process in dryers inclined pools

Proposal for improving of paddy rice drying process in dryers inclined pools

Study on heat and moisture transfer of nonspherical rice during hot‐air drying in thin‐layer grain pile

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

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