Predicting Heat Release Rate from Fire Video Data Part 1. Application of Deep Learning Techniques

Published

May 2, 2024

Author(s)

Kuldeep Prasad

Abstract

A novel approach for estimating transient heat release rate from fire images and video using deep learning techniques is presented. The heat release rate (HRR) is the most critical parameter in characterizing the fire hazard and thermal effects of a burning item. It is an effective indicator of the fire growth rate and fire size that is used extensively in both building fire safety design and firefighting operations. However, for outdoor fires, heat release rate measurements are usually not available due to lack of equipment. The goal of this report is to develop and demonstrate a novel technique "image calorimetry" for predicting heat release rate using video data and recurrent neural network models. The proposed methodology only requires video camera data and can be readily extended to outdoor fire experiments, with or without ambient wind. Results from the trained neural network model show excellent comparison between predicted and temporally evolving heat release rate measurements, with an overall accuracy score of 0.93.

Citation

NIST Interagency/Internal Report (NISTIR) - 8521

Report Number

8521

NIST Pub Series

NIST Interagency/Internal Report (NISTIR)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.IR.8521

Local Download

Keywords

Data Intensive Research, Deep Learning, Fire Calorimetry Database, Fire Heat Release Rate, Wildland Urban Interface.

Citation

Prasad, K. (2024), Predicting Heat Release Rate from Fire Video Data Part 1. Application of Deep Learning Techniques, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8521, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957695 (Accessed May 18, 2024)