5 papers
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
Minseo Lee, Seongmin Oh, Chaehyeon Song +5
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…
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
Shilaj Baral, Youngkyu Lee, Sangam Khanal +1
Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical drift during long-term rollouts.…
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
Jeesuk Shin, Donggyun Seo, Sihyeong Yu +1
Severe accident analysis using system-level codes such as MELCOR is indispensable for nuclear safety assessment, yet the computational cost of repeated simulations poses a signific…
Engineering application of physics-informed neural networks for Saint-Venant torsion
Su Yeong Jo, Sanghyeon Park, Seungchan Ko +4
The Saint-Venant torsion theory is a classical theory for analyzing the torsional behavior of structural components, and it remains critically important in modern computational des…
Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
Sangam Khanal, Shilaj Baral, Joongoo Jeon
CFD acceleration for virtual nuclear reactors or digital twin technology is a primary goal in the nuclear industry. This study compares advanced convolutional neural network (CNN)…