专题：Nuclear Engineering Thermal-Hydraulics

This cluster of papers focuses on nuclear thermal hydraulics, particularly in the context of passive systems. It covers topics such as direct contact condensation, natural circulation loops, safety assessment, large eddy simulation, uncertainty evaluation, T-junction mixing, stability behavior, and reliability evaluation.
最新文献
Higher-order-ReLU-KANs (HRKANs) for Solving Physics-informed Neural Networks (PINNs) More Accurately, Robustly and Faster

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An Integrated Deterministic and Probabilistic Safety Assessment of Multi-Unit Small Modular Reactors Considering the Degradation of Shared Safety Features

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A hybrid data-physics framework with conformal GNN for enhanced damage identification

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A PINN methodology for temperature field reconstruction in the PIV measurement plane: Case of Rayleigh–Bénard convection

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Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

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Experimental investigation of axial and radial void fraction profiles in subcooled downward flow using neutron radiography

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Benchmarking Current Practices in Probabilistic Fault Displacement Hazard Analysis for Nuclear Installations

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Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab

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Predicting boiling heat flux, heat transfer coefficient, and regimes Non-intrusively using external acoustics and deep learning

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Stabilization of sawteeth instability by short gas pulse injection in ADITYA-U tokamak.

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近5年高被引文献
Physics-Informed Neural Networks for Heat Transfer Problems

article Full Text OpenAlex 776 FWCI58.902

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations

article Full Text OpenAlex 276 FWCI32.214

U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow

article Full Text OpenAlex 265 FWCI30.562

Physics-informed neural networks for inverse problems in supersonic flows

article Full Text OpenAlex 217 FWCI24.78

CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method

article Full Text OpenAlex 202 FWCI22.892

POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

article Full Text OpenAlex 199 FWCI15.427

Physics-informed machine learning for reduced-order modeling of nonlinear problems

article Full Text OpenAlex 187 FWCI15.727

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

article Full Text OpenAlex 184 FWCI27.565

A review of physics-based machine learning in civil engineering

review Full Text OpenAlex 169 FWCI1.974

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

article Full Text OpenAlex 159 FWCI12.922