Modeling of industrial multiphase reactors

Industrial multiphase reactors remain among the most challenging systems to model due to their complexity, multiscale coupling, and persistent uncertainties in turbulence, interphase transport, and constitutive closures. While traditional approaches combining first-principles physics, empirical correlations, and numerical pra have enabled substantial progress, fundamental limitations persist. This perspective outlines how advances in artificial intelligence (AI), high-performance computing, and, eventually, quantum computing (QC) can steer multiphase modeling toward industry-ready predictive capability with an accuracy unthinkable today. AI enables more generalizable, physics-constrained closures, while graphics processing units (GPUs) and exascale platforms already enable industry-scale simulations at unprecedented fidelity. Although QC is a longer-term prospect, hybrid quantum–classical approaches offer pathways to address complexities beyond classical limits. These developments promise to transform modeling workflows and engineering practice, with direct implications for scale-up, reliability, sustainability, and cost reduction. We highlight key research priorities, including multiphase-aware turbulence models, AI-assisted closures, hybrid solvers, computing architectures, and rigorous verification, validation, and uncertainty quantification. • Key gaps remain in multiscale coupling, e.g., turbulence, interphase transfer. • AI-physics hybrids facilitate transferable closures, e.g., drag, stress, and turbulence. • Exascale architectures bring plant-scale, high-fidelity multiphase CFD within reach. • Quantum computing promises breakthroughs, pending hardware and algorithm advances. • Faster, easier tools risk misuse — domain expertise and VVUQ remain non-negotiable.