Velocity Reconstruction in Puffing Pool Fires with Physics-Informed Neural Networks

TL;DR

This paper demonstrates that physics-informed neural networks can accurately reconstruct velocity fields in puffing pool fires from limited measurements, even with noisy data, aiding fire research where measurements are challenging.

Contribution

The study applies a physics-informed neural network approach to reconstruct unmeasured velocity fields in fire simulations, showing robustness and potential for experimental use.

Findings

PINNs accurately reconstruct velocity fields from limited data

Reconstruction remains robust with noisy measurements

Fewer measured quantities still yield reliable velocity inference

Abstract

Pool fires are canonical representations of many accidental fires, which can exhibit an unstable unsteady behaviour, known as puffing, which involves a strong coupling between the temperature and velocity fields. Despite their practical relevance to fire research, their experimental study can be limited due to the complexity of measuring relevant quantities in parallel. In this work, we analyse the use of a recent physics-informed machine learning approach, called Hidden Fluid Mechanics (HFM), to reconstruct unmeasured quantities in a puffing pool fire from measured quantities. The HFM framework relies on a Physics-Informed Neural Network (PINN) for this task. A PINN is a neural network that uses both the available data, here the measured quantities, and the physical equations governing the system, here the reacting Navier-Stokes equations, to infer the full fluid dynamic state. This framework is used to infer the velocity field in a puffing pool fire from measurements of density, pressure and temperature. In this work, the dataset used for this test was generated from numerical simulations. It is shown that the PINN is able to reconstruct the velocity field accurately and to infer most features of the velocity field. In addition, it is shown that the reconstruction accuracy is robust with respect to noisy data, and a reduction in the number of measured quantities is explored and discussed. This study opens up the possibility of using PINNs for the reconstruction of unmeasured quantities from measured ones, providing the potential groundwork for their use in experiments for fire research.