Abstract
A reliable method was developed to validate results of a gas-solid bubble fluidized bed model with discrete element method (DEM) through comparison of corresponding pressure fluctuations experimental data. Attractors of two independent pressure signals, evaluation series of DEM model and reference time series of measured pressure signals, were compared in the state-space domain using the S-statistic. Comparison between two reconstructed attractors of evaluation and reference series was performed based on the null hypothesis. The null hypothesis that the evaluation and reference time series originate from the same dynamic sources is rejected if the two series significantly differ. To prove the power of the method, the S-statistic was estimated for obtained experimental data under the same operating conditions. In addition, experimental and model pressure fluctuations were decomposed into 9 levels using wavelet transform to study the validity of the model in a broad range of frequencies. Results indicated that the model results were consistent with experiments.
Highlights
In recent years, two main categories of CFD models such as two fluid models [1], discrete particle models [2, 3] have been conducted to study the hydrodynamic behavior of particulate flows
By means of Multi-resolution analysis (MRA), the pressure fluctuations of the model and experiment were divided into multi-scale signals called macro, mezzo and micro scales which describe the behavior of the original signal [13]
In this study, the pressure signals obtained from a discrete element method (DEM) model and experiment were compared by the S-statistic
Summary
Two main categories of CFD models such as two fluid models [1], discrete particle models [2, 3] have been conducted to study the hydrodynamic behavior of particulate flows. Validation of a DEM Modeling of Gas-Solid Fluidized Bed using the S-statistic in the State-Space Domain et al [5] introduced the S-statistic based on a general distance concept between two delay vectors that provides a consistent test for the null hypothesis. They demonstrated that two sets of independent vectors were obtained from the same probability or not. The null hypothesis is rejected if the discriminating statistic of two signals is significantly different
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