A Comprehensive Review of Flow Assurance in the Energy Transition: Flow Loop Platforms and AI-Driven Solutions for Hydrogen, CO₂, and Ammonia Transport

Flow assurance is a critical barrier to the safe and efficient transport of multiphase fluids across oil, gas, and emerging low-carbon energy systems. Persistent challenges, including hydrate formation, wax and asphaltene deposition, corrosion, and dynamic multiphase flow regimes, threaten the integrity of pipelines under high-pressure and high-temperature (HPHT) and chemically reactive conditions. Experimental flow loops offer a powerful platform for diagnosing and mitigating these issues. However, current systems are limited by rigid designs, insufficient sensing integration, and poor scalability to real-world conditions. The review presents a comprehensive, mechanism-driven analysis of flow loop applications in flow assurance, bridging experimental fluid dynamics, multiphysics modeling, and artificial intelligence (AI). A critical evaluation is provided of current designs, the epistemological limits of scaling laws, and blind spots in diagnosing coupled phenomena. Particular emphasis is given to hybrid experimental–AI workflows, physics–informed neural networks, and digital twins that enable real-time prediction, adaptive control, and closed-loop experimentation. The review culminates in a prescriptive vision for next-generation flow loop platforms tailored to hydrogen, carbon dioxide, and ammonia transport. These fluids are central to the global energy transition and demand experimental capabilities beyond current standards. By defining grand challenges and proposing a future-proof experimental roadmap, the review supports sustainable infrastructure design and contributes to the decarbonization and resilience of modern energy systems.