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Convective drying of wood chips:Accelerating coupled DEM-CFD simulations with parametrized reduced single particle models

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Particuology 2024年第1期84卷 158-167页

作者： lucas reineking Jonas Fischer Ali Mjalled Enric Illana Siegmar Wirtz Viktor Scherer Martin Monnigmann Automatic Control and System Theory Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany Energy Plant Technology Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany

The simulation of industry-scale reactive bulks is challenging due to the complex interaction between fluid and *** particles in the bulk and their interaction with the fluid flow can be described by combined Discrete Element Method-Computational Fluid Dynamics(DEM-CFD)***,the computational cost of the Finite Volume(FV)methods deployed in these models can become prohibi-tively expensive,especially for high inner-particle *** particle Reduced Models(RMs)can be used to achieve both fast and accurate descriptions of the processes in each *** an example of bulk systems comprising heat and mass transfer,we compared FV and RM simulations for the drying of wood chips in a bulk reactor.A manifold-based nonlinear interpolation was applied to resolve changing boundary conditions for the *** simulations showed that RMs provide accurate values for the thermodynamic state variables of the ***,the time required for the bulk simulation was reduced by 67%with the *** is evident that simulations with high inner-particle resolution can be accelerated by RMs if manifold-based nonlinear interpolation is used to address changing boundary conditions.

关键词： Reduced model Reactive bulk Discrete element method Manifold-based interpolation Drying of porous mediaDEM-CFD

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NeuroPNM:Model reduction of pore network models using neural networks

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Particuology 2024年第3期86卷 239-251页

作者： Robert Jendersie Ali Mjalled Xiang Lu lucas reineking Abdolreza Kharaghani Martin Monnigmann Christian Lessig Otto-von-Guericke-Universitat Magdeburg Universitatsplatz 2Magdeburg39106Germany Ruhr-Universitat-Bochum Universitatstraβe 150Bochum44801Germany

Reacting particle systems play an important role in many industrial applications,for example biomass drying or the manufacturing of *** numerical modeling and simulation of such systems is therefore of great importance for an efficient,reliable,and environmentally sustainable operation of the *** complex thermodynamical,chemical,and flow processes that take place in the particles are a particular challenge in a ***,typically a large number of particles is involved,rendering an explicit treatment of individual ones impossible in a reactor-level *** approach for overcoming this challenge is to compute effective,physical parameters from single-particle,high-resolution *** can be combined with model reduction methods if the dynamical behaviour of particles must be *** network models with their unrivaled resolution have thereby been used successfully as high-resolution models,for instance to obtain the macroscopic diffusion coeffcient of *** parameter identification and model reduction have recently gained new impetus by the dramatic progress made in machine learning in the last *** report results on the use of neural networks for parameter identification and model reduction based on three-dimensional pore network models(PNM).We believe that our results provide a powerful complement to existing methodologies for reactor-level simulations with many thermally-thick particles.

关键词： Porenetwork models Neural networks Parameter estimation Reduced order model

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