Electrohydrodynamic crystalline anisotropic solidification based on a hybrid lattice Boltzmann model DOI

Yinnan Zhang,

V. Dzanic, Jiachen Zhao

и другие.

International Communications in Heat and Mass Transfer, Год журнала: 2025, Номер 165, С. 109059 - 109059

Опубликована: Май 14, 2025

Язык: Английский

Prediction of runoff in the upper reaches of the Hei River based on the LSTM model guided by physical mechanisms DOI Creative Commons
Huazhu Xue, Chao Guo, Guotao Dong

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102218 - 102218

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

An Isogeometric Analysis Method with Semi-Implicit Cbs Procedures for Incompressible Laminar Viscous Flows DOI
Gang Wang, Shiwei Tian, Hong Yin

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

STAMNet—A spatiotemporal attention module and network for upscaling reactive transport simulations of the hyporheic zone DOI
Marc Berghouse, Rishi Parashar

Advances in Water Resources, Год журнала: 2025, Номер unknown, С. 104951 - 104951

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Deep learning applications in radiative magnetohydrodynamic bioconvection within a vertical wavy porous structure DOI

S. Sarthak,

D. Srinivasacharya

Physics of Fluids, Год журнала: 2025, Номер 37(4)

Опубликована: Апрель 1, 2025

This study provides an in-depth analysis of the combined effects spatially stationary surface waves, fluid inertia, and thermal radiation on natural convection along a heated vertical wavy surface. The is embedded within porous medium saturated with electrically conducting while also examining intricate dynamics magnetohydrodynamic (MHD) bioconvection. Conducted boundary layer regime at high Darcy–Rayleigh number, research employs coordinate transformation to reformulate governing equations into nonsimilar form. nonlinear coupled differential are solved using advanced framework deep learning, specifically through physics-informed neural networks (PINNs). investigates impact critical factors such as learning rates, neuron configurations, conditions, activation functions, number collocation training data points convergence rate accuracy PINNs. Additionally, it analyzes behavior loss function residuals various points. Comprehensive histograms generated illustrate dimensionless velocity, temperature, concentration, motile micro-organism density. Furthermore, evaluates influence key physical parameters local Nusselt Sherwood numbers, well density number. By leveraging techniques, this work valuable insights MHD bioconvection, demonstrating efficacy PINNs in modeling complex heat, mass, transfer phenomena media under radiation.

Язык: Английский

Процитировано

0

Corrosion modeling of Magnesium and its alloys: A short review DOI Creative Commons

A. Ortiz-Ozuna,

Marvin Montoya-Rangel, Homero Castaneda

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112491 - 112491

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Physics-Informed Neural Networks for the Structural Analysis and Monitoring of Railway Bridges: A Systematic Review DOI Creative Commons
Yuniel Ernesto Martínez Pérez,

Luis Rojas,

Álvaro Peña

и другие.

Mathematics, Год журнала: 2025, Номер 13(10), С. 1571 - 1571

Опубликована: Май 10, 2025

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address this gap, systematic review was conducted across Scopus and Web of Science (2020–2025), filtering records by relevance, journal impact, language. From an initial pool, 120 articles were selected categorised into nine thematic clusters that encompass computational frameworks, hybrid integration conventional solvers, domain decomposition strategies. Through natural language processing (NLP) trend mapping, evidences growing but fragmented research landscape. demonstrate promising capabilities load distribution modelling, structural health monitoring, failure prediction, particularly under dynamic train loads on multi-span bridges. However, methodological gaps persist large-scale simulations, plasticity experimental validation. Future work should focus scalable PINN architectures, refined modelling inelastic behaviours, real-time data assimilation, ensuring robustness generalisability through interdisciplinary collaboration.

Язык: Английский

Процитировано

0

Electrohydrodynamic crystalline anisotropic solidification based on a hybrid lattice Boltzmann model DOI

Yinnan Zhang,

V. Dzanic, Jiachen Zhao

и другие.

International Communications in Heat and Mass Transfer, Год журнала: 2025, Номер 165, С. 109059 - 109059

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0