Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

DOI10.1016/j.compfluid.2022.105611OpenAlexW4292034967WikidataQ114194199 ScholiaQ114194199MaRDI QIDQ2084088

Zexia Zhang, Ajay B. Limaye, Ali Khosronejad

Publication date: 17 October 2022

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.03858

zbMATH Keywords

large-eddy simulationflood flow predictionslarge-scale riversmachine-learning algorithm

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Cites Work

• RANS turbulence model development using CFD-driven machine learning
• A hybrid Cartesian/immersed boundary method for simulating flows with 3D, geometrically complex, moving bodies
• River flow estimation using adaptive neuro fuzzy inference system
• A numerical method for solving the 3D unsteady incompressible Navier-Stokes equations in curvilinear domains with complex immersed boundaries
• Machine Learning for Fluid Mechanics
• On the cause and characteristic scales of meandering and braiding in rivers
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
• Construction of reduced-order models for fluid flows using deep feedforward neural networks
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Deep learning in fluid dynamics

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