Modeling, simulation and machine learning for rapid process control of multiphase flowing foods
From MaRDI portal
Publication:2021087
DOI10.1016/J.CMA.2020.113286zbMath1506.76194OpenAlexW3047768287MaRDI QIDQ2021087
Publication date: 26 April 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2020.113286
Learning and adaptive systems in artificial intelligence (68T05) Basic methods in fluid mechanics (76M99) Multiphase and multicomponent flows (76Txx)
Related Items (5)
Unsteady flow prediction from sparse measurements by compressed sensing reduced order modeling ⋮ AI in computational mechanics and engineering sciences ⋮ A multiresolution local-timestepping scheme for particle-laden multiphase flow simulations using a level-set and point-particle approach ⋮ Fast simulation of particulate suspensions enabled by graph neural network ⋮ Tool path optimization of selective laser sintering processes using deep learning
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- An introduction to computational micromechanics.
- Selection of relevant features and examples in machine learning
- A variational approach to the theory of the elastic behaviour of multiphase materials
- On the isotropic and anisotropic viscosity of suspensions containing particles of diverse shapes and orientations
- On simple scaling laws for pumping fluids with electrically-charged particles
- Analysis of Composite Materials—A Survey
- A Variational Approach to the Theory of the Effective Magnetic Permeability of Multiphase Materials
- An upper bound on the particle-laden dependency of shear stresses at solid–fluid interfaces
- Random heterogeneous materials. Microstructure and macroscopic properties
This page was built for publication: Modeling, simulation and machine learning for rapid process control of multiphase flowing foods