Advanced computational fluid dynamics for catalytic reactor design, multiphysics modelling, optimization, and digital transformation

Computational fluid dynamics (CFD) is transitioning from a predictive flow-simulation technique into a central enabling technology for the design, intensification, scale-up, and digitalization of catalytic reactors. This review critically synthesizes advances reported across fixed-bed, packed-bed, fluidized-bed, trickle-bed, slurry bubble-column, monolithic, membrane, and microreactor systems, spanning chemical, petrochemical, environmental, and energy applications. Rather than treating reactor configurations and numerical methods independently, the review establishes a multiscale framework linking hydrodynamics, heterogeneous reaction kinetics, porous-media transport, heat and mass transfer, radiation, catalyst-scale phenomena, and reactor-scale performance. Particular emphasis is placed on the evolving integration of Large Eddy Simulation, Direct Numerical Simulation, Population Balance Models, artificial intelligence, Physics-Informed Neural Networks, digital twins, and high-performance computing. Their capabilities are critically compared in resolving conversion, selectivity, pressure drop, thermal gradients, species transport, catalyst utilization, and multiphase interactions, with explicit consideration of the trade-offs among predictive fidelity, computational cost, scalability, and industrial relevance. The influence of turbulence closure, mesh resolution, reaction-mechanism complexity, reactor geometry, boundary conditions, parameter uncertainty, and experimental validation on model credibility is systematically assessed. Key unresolved barriers include multiscale coupling, catalyst deactivation, numerical stiffness, uncertainty propagation, computational expense, and reliable scale-up from laboratory reactors to industrial systems. Emerging opportunities in AI-assisted CFD, GPU-accelerated simulation, uncertainty-aware modelling, autonomous optimization, and continuously updated digital twins are critically evaluated. The review ultimately proposes a pathway toward physics-informed, data-augmented, and decision-oriented CFD frameworks capable of accelerating the development of safer, more efficient, flexible, and sustainable catalytic reactor systems.