Impact of filter type on filtered drag modeling: <i>A priori</i> analysis and coarse-grid simulation assessment

Despite their critical role, filter selection for drag modeling lacks systematic analysis and physics-informed guidance, leading to inconsistent choices and poorly understood impacts on model accuracy. This study systematically investigates the impact of filter type on the development and predictive accuracy of filtered drag models in gas–solid fluidized beds. Through a priori analysis of six representative filters, their ability to preserve mesoscale heterogeneities and maintain directional robustness was investigated. Coarse-grid simulations of bubbling and turbulent fluidized beds, spanning diverse particle properties and scales, reveal significant discrepancies in model performance. Maximum differences in mean absolute percentage error for solid volume fraction reach 52.6% (axial) and 31.5% (radial). Results indicate that Gaussian filters provide superior directional robustness and predictive accuracy, making them a preferable alternative to Box filters. This work offers practical guidance on filter selection for developing reliable filtered drag models and improving the accuracy of large-scale fluidized bed simulations.