Prediction of the indoor airflow temperature distribution with a heat source using a multilayer perceptron

Few studies have considered using artificial neural networks to predict the temperature distribution inside a room with a heat source, even though such an approach is potentially much faster than numerical analysis. In this study, a multilayer perceptron (MLP) model was developed and applied to pred...

Full description

Saved in:
Bibliographic Details
Published in:Journal of mechanical science and technology 2023, 37(2), , pp.1011-1025
Main Authors: Kim, Sun Jae, Pandey, Sudhanshu, Ha, Man Yeong
Format: Article
Language:English
Subjects:
Accuracy
Air flow
Artificial neural networks
Computational efficiency
Computational fluid dynamics
Computing costs
Computing time
Control
Dynamical Systems
Engineering
Heat
Industrial and Production Engineering
Mathematical models
Mechanical Engineering
Multilayer perceptrons
Numerical analysis
Original Article
Temperature distribution
Vibration
기계공학
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Few studies have considered using artificial neural networks to predict the temperature distribution inside a room with a heat source, even though such an approach is potentially much faster than numerical analysis. In this study, a multilayer perceptron (MLP) model was developed and applied to predicting the temperature distribution in a room with a heat source, where the input variables were the x- and y-coordinates and the distances from the bottom of the wall to the airflow inlet and outlet. The MLP model was trained and evaluated to optimize the hyperparameters (i.e., batch size, node, learning rate, and number of layers) for the prediction accuracy. When compared with a computational fluid dynamics (CFD) simulation, the MLP model showed comparable prediction accuracy, but the computational time was about 11 s compared with 27 min for the CFD simulation. Thus, the MLP model can predict the temperature distribution in a room with an internal heat source with high accuracy and very low computational cost.
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-023-0140-3