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<p>Scaling up Solar-to-hydrogen systems from lab prototypes to industrial applications presents significant challenges, largely because complex multiphysics phenomena control reactor performance. While material innovations continue to advance, transitioning to commercial hydrogen production requires reactor-scale engineering and optimization methods that can optimize heat/mass transport, radiation distribution, and reaction kinetics at scale. Computational fluid dynamics (CFD) has proven particularly powerful for these tasks. For this purpose, this review covers current developments in multiphysics CFD modeling for photo-driven and solar-thermochemical hydrogen production approaches. The modeling framework incorporates multiple physical phenomena like multiphase flow (Eulerian-Eulerian), radiation transport (P1, discrete ordinates, Monte Carlo methods), kinetics of model (e.g. Langmuir-Hinshelwood or biokinetics), electrochemical (Butler-Volmer), and other governing equation of dominant physics. This review finds that while current CFD methods successfully identify critical performance bottlenecks such as catalyst illumination in photocatalytic systems, bubble management in photoelectrochemical devices, and thermal stress in thermochemical reactors, significant predictive gaps remain. Next-generation platforms will include CFD-embedded digital twins, physics-informed machine learning, and quantum computing technologies that can overcome current multiscale simulation and dynamic optimization limits. This evolution will reduce computational costs, improve predictions, accelerate design cycles, and reduce the necessity for expensive experimental work.</p>

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Multiphysics CFD Modeling of Solar-To-Hydrogen Reactors: a Comprehensive Review of Recent Advances, Case Studies, and Future Challenges

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Mahdi Alibeigi,
  • Mehdi Mehrpooya,
  • Tianbiao He

摘要

Scaling up Solar-to-hydrogen systems from lab prototypes to industrial applications presents significant challenges, largely because complex multiphysics phenomena control reactor performance. While material innovations continue to advance, transitioning to commercial hydrogen production requires reactor-scale engineering and optimization methods that can optimize heat/mass transport, radiation distribution, and reaction kinetics at scale. Computational fluid dynamics (CFD) has proven particularly powerful for these tasks. For this purpose, this review covers current developments in multiphysics CFD modeling for photo-driven and solar-thermochemical hydrogen production approaches. The modeling framework incorporates multiple physical phenomena like multiphase flow (Eulerian-Eulerian), radiation transport (P1, discrete ordinates, Monte Carlo methods), kinetics of model (e.g. Langmuir-Hinshelwood or biokinetics), electrochemical (Butler-Volmer), and other governing equation of dominant physics. This review finds that while current CFD methods successfully identify critical performance bottlenecks such as catalyst illumination in photocatalytic systems, bubble management in photoelectrochemical devices, and thermal stress in thermochemical reactors, significant predictive gaps remain. Next-generation platforms will include CFD-embedded digital twins, physics-informed machine learning, and quantum computing technologies that can overcome current multiscale simulation and dynamic optimization limits. This evolution will reduce computational costs, improve predictions, accelerate design cycles, and reduce the necessity for expensive experimental work.