Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
arXiv:2605.30277v1 Announce Type: new Abstract: Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neural operator--based surrogates for CFD-level transient analysis of SMR-specific geometries have not been reported. This study presents an integrated fra
The increasing computational demands of advanced simulations for critical infrastructure like SMRs, coupled with advancements in AI-based surrogate modeling, necessitates the development of more efficient real-time analysis tools.
This development addresses a critical bottleneck in the deployment of digital twin technology for nuclear energy, potentially accelerating SMR safety and operational efficiency and reducing reliance on high-cost traditional simulations.
The ability to perform real-time, high-fidelity thermal-hydraulic simulations for SMRs using AI will significantly reduce operational costs and design timelines, making SMRs more viable for wider adoption.
- · Small Modular Reactor developers
- · AI software providers
- · Nuclear energy sector
- · Digital twin technology companies
- · Traditional CFD simulation providers (without AI integration)
- · Companies reliant on slow, high-cost simulation processes
Real-time AI-driven simulations improve the safety and operational efficiency of SMRs.
Accelerated SMR deployment contributes to addressing the global energy bottleneck by providing cleaner, more efficient power generation.
Increased adoption of SMRs shifts energy portfolios, potentially impacting geopolitical energy dependencies and contributing to decarbonization goals.
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