Results 201 to 210 of about 148,309 (340)

Lattice Boltzmann simulation for phase separation with chemical reaction controlled by ultrasound field. [PDF]

PLoS One
Wang H, Lu Y.
europepmc   +1 more source

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport. [PDF]

ACS Omega, 2023
Liu S, Barati R, Zhang C, Kazemi M.
europepmc   +1 more source

Solution of the Hirota equation using lattice-Boltzmann and the exponential function methods

, 2017
F. Fonseca
openalex   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Study on CO₂/CH₄ displacement process in shale microscale models with adsorption/desorption behavior by lattice Boltzmann method. [PDF]

Sci Rep
Zhang Y, Xu Y, Chen X, Pi Z, Xie Q, Liao K.   +5 more
europepmc   +1 more source

Fluid-structure interaction modeling of compliant aortic valves using the lattice Boltzmann CFD and FEM methods. [PDF]

Biomech Model Mechanobiol, 2023
Morany A, Lavon K, Gomez Bardon R, Kovarovic B, Hamdan A, Bluestein D, Haj-Ali R.   +6 more
europepmc   +1 more source

ANALYSIS OF FREE SURFACE FLOW IN PELTON TURBINE USING LATTICE BOLTZMANN METHOD

, 2021
Andrej Lipej
openalex   +2 more sources

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

A novel comprehensible non-intrusive sensitivity-driven additive Aerodynamic Shape Optimization (AASO) and its implementation using the Lattice Boltzmann Method (AASO-LBM). [PDF]

Sci Rep
Granados-Ortiz FJ.
europepmc   +1 more source

General Propagation Lattice Boltzmann Model for the Boussinesq Equation. [PDF]

Entropy (Basel), 2022
Yang W, Li C.
europepmc   +1 more source