Physics-informed neural networks for two-phase film boiling heat transfer

International Journal of Heat and Mass Transfer, Год журнала: 2025, Номер 241, С. 126680 - 126680

Опубликована: Янв. 10, 2025

Язык: Английский

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Discharge optimization in shell-and-tube latent heat storage systems using response surface methodology

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104157 - 104157

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction of ground movement-induced pipe responses considering variable PGD magnitudes using physics-informed neural networks and transfer learning

Engineering Structures, Год журнала: 2025, Номер 332, С. 120018 - 120018

Опубликована: Март 13, 2025

Язык: Английский

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Enhancing absorption performance in metal hydride hydrogen storage: Implementation of fins and a water jacket

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 116, С. 577 - 590

Опубликована: Март 14, 2025

Язык: Английский

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Recent advances of helical baffle heat exchangers: Principles, structural optimization, application and beyond

Applied Thermal Engineering, Год журнала: 2025, Номер unknown, С. 126390 - 126390

Опубликована: Апрель 1, 2025

Язык: Английский

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CFD-Driven Evaluation of Winglet Cant Angle: Insights into Aerodynamic and Aeroacoustic Performance

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104759 - 104759

Опубликована: Апрель 1, 2025

Язык: Английский

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Advances in boiling and evaporative heat transfer science and applications

International Journal of Heat and Mass Transfer, Год журнала: 2025, Номер unknown, С. 127047 - 127047

Опубликована: Апрель 1, 2025

Язык: Английский

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Physics-Informed Neural Networks in Polymers: A Review

Polymers, Год журнала: 2025, Номер 17(8), С. 1108 - 1108

Опубликована: Апрель 19, 2025

The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity multi-scale behavior. Traditional computational methods, while effective, often struggle balance accuracy with efficiency, especially when bridging the atomistic macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning governing physical laws system. This review discusses development application PINNs in context science. It summarizes recent advances, outlines key methodologies, analyzes benefits limitations using for property prediction, structural design, process optimization. Finally, it identifies current future research directions further leverage advanced modeling.

Язык: Английский

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Linking Airflow Dynamics to Infection Risk Reduction and Predictive Strategies

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 113027 - 113027

Опубликована: Май 1, 2025

Язык: Английский

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Surrogates for empirical parameters in the separated flow boiling model using physics informed neural networks: SFM-PINNs

Опубликована: Июнь 1, 2025

Язык: Английский

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