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AI framework enhances SMR simulations for digital twins

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework combining reduced-order models (ROMs) with neural operators for computational fluid dynamics (CFD) simulations. This approach aims to enable real-time thermal-hydraulic simulations essential for digital twin technology in small modular reactors (SMRs). The study compares different neural operator architectures, including DeepONet and Fourier Neural Operator (FNO), and introduces a multi-scale technique to improve predictions of complex flow dynamics. AI

IMPACT This research could accelerate the development and deployment of digital twins for SMRs by enabling faster, more accurate simulations.

RANK_REASON The cluster contains an academic paper detailing a new AI-based modeling technique for a specific engineering application.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI framework enhances SMR simulations for digital twins

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Minseo Lee, Seongmin Oh, Chaehyeon Song, Bumjin Cho, Shilaj Baral, Sangam Khanal, Minseop Song, Joongoo Jeon ·

    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 ana…

  2. arXiv cs.LG TIER_1 English(EN) · Joongoo Jeon ·

    Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor

    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 direc…

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