Bibliographic Details
| Title: |
Freeze Thickness Prediction of Fire Pipes in Low-Temperature Environment Based on CFD and Artificial Neural Network |
| Authors: |
Yubiao Huang, Jiaqing Zhang, Yu Zhong, Yi Guo, Yanming Ding |
| Source: |
Fire, Vol 8, Iss 2, p 65 (2025) |
| Publisher Information: |
MDPI AG, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Physics |
| Subject Terms: |
fire pipe, computational fluid dynamics, artificial neural network, freeze thickness, prediction, Physics, QC1-999 |
| More Details: |
In cold regions, fire pipes are highly susceptible to freezing, which can obstruct water flow and lead to pipe ruptures. Accurately predicting the freeze thickness is crucial to maintaining the functionality of fire protection systems. Traditional methods for predicting fire pipe freezing often rely on simplified models or time-consuming simulations, which limits their accuracy in complex environments. A model for predicting the freeze thickness of fire pipes under low-temperature conditions was developed by integrating Computational Fluid Dynamics with an Artificial Neural Network (ANN). The CFD model was validated to generate data for training and optimizing an ANN based on collected experimental data. The CFD results demonstrate a nonlinear relationship between the freeze thickness of the fire pipe, ambient temperature, and time. Afterwards, the optimal ANN topology, determined through hyperparameter tuning, was found to consist of 12 neurons, the trainlm training function, and tansig–purelin activation functions. Eventually, the ANN model achieved high prediction accuracy with a mean squared error (MSE) of 6.62 × 10−4 on the test set and a regression coefficient R greater than 0.98 across all datasets. Furthermore, the ANN model agrees closely with the simulated data, not only for trained temperature conditions (−5 °C to −50 °C) but also for unseen temperature conditions (−55 °C and −60 °C), indicating excellent generalization performance. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2571-6255 |
| Relation: |
https://www.mdpi.com/2571-6255/8/2/65; https://doaj.org/toc/2571-6255 |
| DOI: |
10.3390/fire8020065 |
| Access URL: |
https://doaj.org/article/5177b3ce99e54cae8ec476289638adf0 |
| Accession Number: |
edsdoj.5177b3ce99e54cae8ec476289638adf0 |
| Database: |
Directory of Open Access Journals |
