Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
From MaRDI portal
Publication:2194845
DOI10.1016/j.camwa.2020.07.002zbMath1497.76112OpenAlexW3043024185MaRDI QIDQ2194845
Publication date: 7 September 2020
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.camwa.2020.07.002
neural networkmodel predictive controlmachine learningrecirculation zoneoptimal energy controltwo-phase flow modelling
Artificial neural networks and deep learning (68T07) Liquid-gas two-phase flows, bubbly flows (76T10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Intelligent systems for knowledge management
- On boundary conditions in the element-free Galerkin method
- Large eddy simulation of non-premixed pulverized coal combustion in corner-fired furnace for various excess air ratios
- POD-Galerkin reduced order methods for combined Navier-Stokes transport equations based on a hybrid FV-FE solver
- Machine Learning for Fluid Mechanics
- An approximate expression for the shear lift force on a spherical particle at finite reynolds number
- Optimal mixing in recirculation zones
- On the role of actuation for the control of streaky structures in boundary layers
- Multiphase Flow Dynamics 1
- Control and system identification of a separated flow
- A Family of Model Predictive Control Algorithms With Artificial Neural Networks
- Deep learning in fluid dynamics
- Turbulence Modeling in the Age of Data
- Incompressible flow along a corner
This page was built for publication: Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption