Robust interpolation for dispersed gas-droplet flows using statistical learning with the fully Lagrangian approach

DOI10.1002/FLD.5225MaRDI QIDQ6570886

O. D. Rybdylova, C. P. Stafford

Publication date: 10 July 2024

Published in: International Journal for Numerical Methods in Fluids (Search for Journal in Brave)

zbMATH Keywords

kernel regressionpolydisperse dropletsfully Lagrangian approach

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Numerical analysis (65-XX)

Cites Work

• Smoothed particle hydrodynamics and magnetohydrodynamics
• Eulerian models for turbulent spray combustion with polydispersity and droplet crossing
• Investigation of regions of unbounded growth of the particle concentration in disperse flows
• Voronoi fluid particle model for Euler equations
• An overview of smoothed particle hydrodynamics for simulating multiphase flow
• Kernel regression with sparse metric learning
• Segregation of particles in incompressible random flows: singularities, intermittency and random uncorrelated motion
• The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields
• A model for the investigation of the second-order structure of caustic formations in dispersed flows
• A Lagrangian approach to droplet condensation in atmospheric clouds
• Full Lagrangian methods for calculating particle concentration fields in dilute gas-particle flows
• Lagrangian modelling of dust admixture in gas flows
• The Elements of Statistical Learning

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Development of the full Lagrangian approach for modeling dilute dispersed media flows (a review)

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