![]() ![]() This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. TRANSFORMICE BASE SOFTWAREThe application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. Regarding that DNS is commonly implemented over a specific range of operating conditions, an enhanced HDC via refitting more elaborate high-fidelity DNS data (εs=, Res=) from literature is proposed and analyzed.įor the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. Moreover, the HDCs from DNS of static particles agree better with the benchmark data from DNS of dynamic gas-particle flows at very low Reynolds numbers for εs>0.05∼0.10 while Wen-Yu drag is more applicable for the remaining range. Quantitative comparisons directly reveal that there are significant differences between the commonly-practiced Wen-Yu drag closure and the direct numerical simulation (DNS) based HDCs, especially for moderate and dense gas-particle flows. This work investigates the effects of sub-input HDCs on filtered mesoscale predictions using highly-resolved simulations. The embedded microscopic homogeneous drag closure (HDC) is of key importance in determining the reliability and accuracy of such simulations. TRANSFORMICE BASE FULLAlthough the robust DNS can capture the full details of flow systems, the continuum two-fluid model (TFM)-based E-E and discrete E-L simulations are much more efficient to capture the local hydrodynamics.įiltered mesoscale model can be formulated from highly-resolved continuum or discrete simulations. Many numerical and theoretical methods have been thus developed to accurately understand and model the complex two-phase dynamics such as the direct numerical simulation (DNS) (Xiong et al., 2012 Liu et al., 2017), Eulerian-Lagrangian (E-L) (Deen et al., 2007 Mu et al., 2020 Wu et al., 2020), and Eulerian-Eulerian (E-E) methods (Igci et al., 2008 Cloete et al., 2017 Schneiderbauer et al., 2017), the energy minimization multi-scale (EMMS) (Yang et al., 2003 Lu et al., 2009 Chen et al., 2013 Nikolopoulos et al., 2021) and the gradient-based method Zhu et al., 2018). Here, the mesoscale denotes the characteristic scale of the dynamic inhomogeneous structures (e.g., clusters, streamers, bubbles and bubble-like voids) and bridges the microscale particle element and the macroscale flow system (Li and Huang, 2018 Ma et al., 2019 Wang et al., 2020). Finally, the applicability of conventional and data‐driven models coupled with coarse‐grid computational fluid dynamics simulations for pilot‐scale (reactive) gas‐particle flows is validated comprehensively. Comparative analysis is also conducted between existing HTC corrections and our work. Moreover, compared with conventional correlations, DDM predictions agree better with filtered resolved data. We further find that the filtered HTC correction critically depends on the added filtered temperature difference marker while the filtered reaction rate correction shows weak dependence on the additional markers. Results reveal that the filtered drag correction is nearly independent of filter size when including the filtered gas phase pressure gradient. The dataset used for developing the DDM is filtered from highly‐resolved simulations closed by our recently formulated microscopic drag and heat transfer coefficients (HTCs). ![]() This study presents conventional and artificial neural network‐based data‐driven modeling (DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and reaction rate in gas‐particle flows. ![]()
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