Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network

Ye, Fangping; Wheeler, Craig; Chen, Bin; Hu, Jiquan; Chen, Kaikai; Chen, Wei

doi:10.1016/j.apt.2018.11.005

Cited by 50 publications

(16 citation statements)

References 29 publications

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“…Whether under static (baffle lift test) or dynamic conditions (rotary drum test), the error between the comparison tests is lower than 5%, which is also in line with the error criteria mentioned by Ye et al [19]. Simultaneously, we found that although the relative error verified by the baffle lift test is small, it is between 0.77% and 4.86%.…”

Section: Development and Application Of The Bp Prediction Modelsupporting

confidence: 90%

“…To reasonably obtain a feasible solution that satisfies the mapping relation and to achieve a target value of the stacking angle mean of the actual test, which is listed in Appendix A, Table A2), the discrete outputs of the predicted output are compared and eliminated. The difference between the output value of the BP prediction model and that of the calibration experiment is less than 5% [19], and the condition is satisfied. These conditions can be expressed as:AORTol=Object|outputs−targetstargets|≤5%,Subject to outputs=ffalse(inputsfalse), inputsmin≤inputs≤inputsmax.…”

Section: Development and Application Of The Bp Prediction Modelmentioning

confidence: 99%

“…Herein, the BP neural network was trained in conjunction with the improved FBS-104 powder meter test. In engineering applications [19], the correlation coefficient (R 2 ) is usually greater than 90% before the neural network can be used as the predictive model; thus, the correlation coefficient can be considered to be an evaluation criterion. Figure 2b denotes the application of the BP neural network after training.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

Fan

Pan

et al. 2019

Materials

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The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson–Kendall–Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters’ range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.

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Section: Development and Application Of The Bp Prediction Modelsupporting

confidence: 90%

Section: Development and Application Of The Bp Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

Fan

Pan

et al. 2019

Materials

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“…Friction coefficients (Table 1) have a direct physical representation; however, in DEM software these parameters are integrated into DEM codes of contact models and may affect the behavior of bulk materials in different ways, that is, they are code dependent [6,7]. Since the values of the friction coefficients (DEM parameters) significantly affect the behavior of bulk materials, in order to build an adequate model, they have to be calibrated [8][9][10]. Many researchers offer their approaches and solutions for the DEM parameters calibration.…”

Section: Introductionmentioning

confidence: 99%

Universal Approach for DEM Parameters Calibration of Bulk Materials

et al. 2021

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DEM parameters calibration is the most important step in preparing a DEM model. At the same time, the lack of a universal approach to DEM parameters calibration complicates this process. The paper presents the author’s approach to creating a universal calibration approach based on the physical meaning of the friction coefficients and conducting symmetrical experiments at full scale and in a simulation, as well as the implementation of the approach in the form of a physical test rig. Several experiments were carried out to determine the DEM parameters of six material–boundary pairs. The resulting parameters were adjusted using a refinement experiment. The results confirmed the adequacy of the developed approach, as well as its applicability in various conditions. The limitations of both the approach itself and its specific implementation in the form of a test rig were identified.

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“…The basic input values that are set in the simulations include density, shear modulus and Poisson's ratio [7,8]. Once these values are set, it is necessary to determine interaction coefficients (static and rolling friction, restitution), which can significantly influence the static and dynamic flow, and are very often used as calibration parameters [9,10].…”

Section: Introductionmentioning

confidence: 99%

DEM Investigation of the Influence of Particulate Properties and Operating Conditions on the Mixing Process in Rotary Drums: Part 1—Determination of the DEM Parameters and Calibration Process

et al. 2020

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This paper’s goal was to select methods and a calibration procedure which would lead to the determination of relevant parameters of a discrete element method (DEM) and virtual material creation. Seven particulates were selected with respect to their shape (spherical and non-spherical), size and density. The first calibration experiment involved “packing test” to determine the shape accuracy and bulk density of virtual packed particulates. The series of simulations were compared with real experiments, and the size, shape and density of virtual particles were optimized. Using three apparatuses, the input parameter values were experimentally determined for a contact model that defines the behavior of particulates in DEM simulations. The research part of the paper examines the influence of factors such as particle number; pile formation method; and the method of evaluation of the angle of repose on the process of the calibration of virtual material. The most reproducible results were achieved by the “pilling” method and by the rotating drum—both evaluated by the geometric method. However, it is always advisable to make an overall visual comparison of the slope shape between the calibration simulation and the experimental curves. The bowl’s diameter to particle size ratio should be greater than 25, and the calibration experiment should contain approximately 4000 particles to ensure representative results during angle of repose calibration experiment.

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Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network

Cited by 50 publications

References 29 publications

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

Universal Approach for DEM Parameters Calibration of Bulk Materials

DEM Investigation of the Influence of Particulate Properties and Operating Conditions on the Mixing Process in Rotary Drums: Part 1—Determination of the DEM Parameters and Calibration Process

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