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A supervised machine learning approach for predicting variable drag forces on spherical particles in suspension

Journal Article · · Powder Technology

Not Available

Sponsoring Organization:
USDOE
OSTI ID:
1636746
Journal Information:
Powder Technology, Journal Name: Powder Technology Journal Issue: C Vol. 345; ISSN 0032-5910
Publisher:
ElsevierCopyright Statement
Country of Publication:
Netherlands
Language:
English

References (24)

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Heat transfer in an assembly of ellipsoidal particles at low to moderate Reynolds numbers journal November 2017
Drag law for monodisperse gas–solid systems using particle-resolved direct numerical simulation of flow past fixed assemblies of spheres journal November 2011
Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels journal October 2016
A new approach for conjugate heat transfer problems using immersed boundary method for curvilinear grid based solvers journal June 2014
Mixing and segregation in a bidisperse gas–solid fluidised bed: a numerical and experimental study journal February 2004
The influence of binary drag laws on simulations of species segregation in gas-fluidized beds journal June 2008
Evaluation of drag correlations using particle resolved simulations of spheres and ellipsoids in assembly journal May 2017
Variation of drag, lift and torque in a suspension of ellipsoidal particles journal July 2018
Moderate-Reynolds-number flows in ordered and random arrays of spheres journal November 2001
On the periodic fundamental solutions of the Stokes equations and their application to viscous flow past a cubic array of spheres journal February 1959
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance journal October 2016
Application of neural networks to turbulence control for drag reduction journal June 1997
Large eddy simulation for predicting turbulent heat transfer in gas turbines
  • Tafti, Danesh K.; He, Long; Nagendra, K.
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 372, Issue 2022 https://doi.org/10.1098/rsta.2013.0322
journal August 2014
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data journal March 2017
Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow journal October 2016
Study of Fluid Structure Interaction Using Sharp Interface Immersed Boundary Method
  • He, Long; Joshi, Keyur; Tafti, Danesh
  • ASME 2016 Fluids Engineering Division Summer Meeting collocated with the ASME 2016 Heat Transfer Summer Conference and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels, Volume 1A, Symposia: Turbomachinery Flow Simulation and Optimization; Applications in CFD; Bio-Inspired and Bio-Medical Fluid Mechanics; CFD Verification and Validation; Development and Applications of Immersed Boundary Methods; DNS, LES and Hybrid RANS/LES Methods; Fluid Machinery; Fluid-Structure Interaction and Flow-Induced Noise in Industrial Applications; Flow Applications in Aerospace; Active Fluid Dynamics and Flow Control — Theory, Experiments and Implementation https://doi.org/10.1115/FEDSM2016-7861
conference December 2016

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