Leveraging Advanced Machine Learning to Predict Turbulence Dynamics from Temperature Observations at an Experimental Prescribed Fire

TL;DR

This paper demonstrates that machine learning models can effectively predict turbulent kinetic energy from temperature data collected during prescribed fires, offering new insights into fire environment dynamics and improving fire management strategies.

Contribution

The study introduces a novel approach using machine learning to predict turbulence from temperature observations in fire environments, enhancing understanding of fire dynamics.

Findings

Machine learning models accurately predict TKE from temperature data.

Temperature and TKE exhibit identifiable correlation patterns.

The approach advances fire environment modeling and management.

Abstract

This study explores the potential for predicting turbulent kinetic energy (TKE) from more readily acquired temperature data using temperature profiles and turbulence data collected concurrently at 10 Hz during a small experimental prescribed burn in the New Jersey Pine Barrens. Machine learning models, including Deep Neural Networks, Random Forest Regressor, Gradient Boosting, and Gaussian Process Regressor, were employed to assess the potential to predict TKE from temperature perturbations and explore temporal and spatial dynamics of correlations. Data visualization and correlation analyses revealed patterns and relationships between thermocouple temperatures and TKE, providing insight into the underlying dynamics. More accurate predictions of TKE were achieved by employing various machine learning models despite a weak correlation between the predictors and the target variable. The results demonstrate significant success, particularly from regression models, in accurately predicting the TKE. The findings of this study demonstrate a novel numerical approach to identifying new relationships between temperature and airflow processes in and around the fire environment. These relationships can help refine our understanding of combustion environment processes and the coupling and decoupling of fire environment processes necessary for improving fire operations strategy and fire and smoke model predictions. The findings of this study additionally highlight the valuable role of machine learning techniques in analyzing the complex large datasets of the fire environments, showcasing their potential to advance fire research and management practices.