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
Physics-Informed Neural Operator for Learning Partial Differential Equations

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Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems

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Machine learning based damage state identification: A novel perspective on fragility analysis for nuclear power plants considering structural uncertainties

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Data-driven machine learning approach based on physics-informed neural network for population balance model

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Structured, sintered, and rastered strategies for fluid wicking in additively manufactured heat pipes

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Characterization of ventilated supercavitation regimes using Bayesian optimized Random Forest models

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Experimental study on the geyser boiling phenomena and periodic correlation in high temperature sodium heat pipes

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Fault analysis and fault degree evaluation via an improved ResNet method for aircraft hydraulic system

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A Hybrid Model Coupling Data and Hydraulic Transient Laws for Water Distribution Systems

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Fast solution of 3D transport processes using a physics-informed neural network with embedded transfer learning

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

article Full Text OpenAlex 928 FWCI83.65898876

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

article Full Text OpenAlex 375 FWCI191.79143332

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

article Full Text OpenAlex 347 FWCI177.9455277

Physics-informed neural networks for inverse problems in supersonic\n flows

article Full Text OpenAlex 258 FWCI132.30532031

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

article Full Text OpenAlex 253 FWCI129.74126372

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

article Full Text OpenAlex 218 FWCI20.94875491

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

article Full Text OpenAlex 215 FWCI72.70886364

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

article Full Text OpenAlex 205 FWCI21.35684754

A review of physics-based machine learning in civil engineering

review Full Text OpenAlex 194 FWCI18.36416826

PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation

article Full Text OpenAlex 191 FWCI67.96444332