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
Uncertainty Quantification for Transient Thermal Management

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The influence of cooling temperature on the thermal-dynamic performance of wettability-driven pulsating heat pipes

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Review of predictive capabilities of JRC-JCS model in engineering practice

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Development of delayed neutron precursor and decay heat models for molten salt reactors using runge–kutta discontinuous Galerkin methods

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Tube plugging effects on the thermal–hydraulic characteristics of a CAP1400 steam generator with a three-dimensional primary–secondary coupled heat-transfer model

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Data-driven empirical correlation framework for full-cycle vapor bubble dynamics in nucleate flow boiling

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Analysis of fracture conditions of Cr-coated Zr alloy claddings under LOCA conditions calculated using FEMAXI fuel performance code

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Experimental investigation on HFE-7100 flow boiling in aluminum open microchannels heat sink

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Flow boiling characteristics of silicon-based jet/bifurcated expanding microchannel heat sink

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Experimental Research Progress on Gas–Liquid Flow and Heat Transfer Characteristics in Micro Pulsating Heat Pipes

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近5年高被引文献
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow

article Full Text OpenAlex 426 FWCI48.75

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

article Full Text OpenAlex 388 FWCI45.25

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

article Full Text OpenAlex 294 FWCI33.4001

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

article Full Text OpenAlex 294 FWCI33.125

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

article Full Text OpenAlex 244 FWCI21.7992

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

article Full Text OpenAlex 221 FWCI58.2658

Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

article Full Text OpenAlex 201 FWCI23.1307

Physics-Informed Neural Operator for Learning Partial Differential Equations

article Full Text OpenAlex 197 FWCI52.3227

Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring

review Full Text OpenAlex 181 FWCI64.6496

A Review of Physics-Informed Machine Learning in Fluid Mechanics

review Full Text OpenAlex 179 FWCI29.7825