Deep Learning-Based Prediction of Unsteady Reynolds-Averaged Navier-Stokes Solutions for Vertical-Axis Turbines

The following study investigates the effectiveness of a deep learning-based method for predicting the flow field and flow-driven rotation of a vertical-axis hydrokinetic turbine operating in previously unseen free-stream velocities. A Convolutional Neural Network (CNN) is trained and tested using th...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2023-01, Vol.16 (3), p.1130
Main Authors: Dorge, Chloë, Bibeau, Eric
Format: Article
Language:English
Subjects:
Angular velocity
array optimization
Arrays
Boundary conditions
CFD
Deep learning
Design and construction
Efficiency
Error reduction
Flow velocity
Fluid flow
Foils
Free flow
Machine learning
Neural networks
Power
Predictions
Pressure
Reynolds averaged Navier-Stokes method
Rotors
Simulation
turbine interaction
Turbines
URANS
Velocity
vertical-axis turbine
Viscosity
Vortices
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The following study investigates the effectiveness of a deep learning-based method for predicting the flow field and flow-driven rotation of a vertical-axis hydrokinetic turbine operating in previously unseen free-stream velocities. A Convolutional Neural Network (CNN) is trained and tested using the solutions of five two-dimensional (2-D), foil-resolved Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, with free-stream velocities of 1.0, 1.5, 2.0, 2.5, and 3.0 m/s. Based on the boundary conditions of free-stream velocity and rotor position, the flow fields of x-velocity, y-velocity, pressure, and turbulent viscosity are inferred, in addition to the angular velocity of the rotor. Three trained CNN models are developed to evaluate the effects of (1) the dimensions of the training data, and (2) the number of simulations used as training cases. Reducing data dimensions was found to diminish mean relative error in predictions of velocity and turbulent viscosity, while increasing it in predictions of pressure and angular velocity. Increasing the number of training cases from two to three was found to reduce relative error for all predicted unknowns. With the best achieved CNN model, the variables of x-velocity, y-velocity, pressure, turbulent viscosity, and angular velocity were inferred with mean relative errors of 6.93%, 9.82%, 10.7%, 7.48%, and 0.817%, respectively.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16031130