Efficient interpolation of precomputed kinetic data employing reduced multivariate Hermite Splines

Increasing Computational Efficiency of CFD Simulations of Reactive Flows at Catalyst Surfaces through Dynamic Load Balancing

We propose a numerical strategy based on dynamic load balancing (DLB) aimed at enhancing the computational efficiency of multiscale CFD simulation of reactive flows at catalyst surfaces. Our approach employs DLB combined with a hybrid parallelization technique, integrating both MPI and OpenMP protocols. This results in an optimized distribution of the computational load associated with the chemistry solution across processors, thereby minimizing computational overheads. Through assessments conducted on fixed and fluidized bed reactor simulations, we demonstrated a remarkable improvement of the parallel efficiency from 19 to 87% and from 19 to 91% for the fixed and fluidized bed, respectively. Owing to this improved parallel efficiency, we observe a significant computational speed-up of 1.9 and 2.1 in the fixed and fluidized bed reactor simulations, respectively, compared to simulations without DLB. All in all, the proposed approach is able to improve the computational efficiency of multiscale CFD simulations paving the way for a more efficient exploitation of high-performance computing resources and expanding the current boundaries of feasible simulations.

Computational Fluid Dynamics for Fixed Bed Reactor Design

Flow, heat, and mass transfer in fixed beds of catalyst particles are complex phenomena and, when combined with catalytic reactions, are multiscale in both time and space; therefore, advanced computational techniques are being applied to fixed bed modeling to an ever-greater extent. The fast-growing literature on the use of computational fluid dynamics (CFD) in fixed bed design reflects the rapid development of this subfield of reactor modeling. We identify recent trends and research directions in which successful methodology has been established, for example, in computer generation of packings of complex particles, and where more work is needed, for example, in the meshing of nonsphere packings and the simulation of industrial-size packed tubes. Development of fixed bed reactor models, by either using CFD directly or obtaining insight, closures, and parameters for engineering models from simulations, will increase confidence in using these methods for design along with, or instead of, expensive pilot-scale experiments.

Advances in fixed-bed reactor modeling using particle-resolved computational fluid dynamics (CFD)

In 2006, Dixon et al. published the comprehensive review article entitled “Packed tubular reactor modeling and catalyst design using computational fluid dynamics.” More than one decade later, many researchers have contributed to novel insights, as well as a deeper understanding of the topic. Likewise, complexity has grown and new issues have arisen, for example, by coupling microkinetics with computational fluid dynamics (CFD). In this review article, the latest advances are summarized in the field of modeling fixed-bed reactors with particle-resolved CFD, i.e. a geometric resolution of every pellet in the bed. The current challenges of the detailed modeling are described, i.e. packing generation, meshing, and solving with an emphasis on coupling microkinetics with CFD. Applications of this detailed approach are discussed, i.e. fluid dynamics and pressure drop, dispersion, heat and mass transfer, as well as heterogeneous catalytic systems. Finally, conclusions and future prospects are presented.