High-resolution reconstruction of turbulent flames from sparse data with physics-informed neural networks

Abstract

Accurate and detailed data are vital for fundamental understanding of turbulent combustion. However, studies of turbulent combustion often suffer from measurement sparsity or high simulation cost. In the present work, for the first time, a physics-informed neural networks (PINNs) framework is established for three-dimensional high-resolution reconstruction of turbulent combustion with synthetic sparse data. The performance of the PINNs is evaluated on two different configurations of turbulent flames without and with a mean shear, including freely propagating planar premixed combustion and slot-jet premixed combustion. The reconstructed fields of velocity, temperature, and species mass fractions are compared with the high-fidelity direct numerical simulation (DNS) data, and both qualitative and quantitative analyses are performed for the model performance evaluation. The results show that by constraining with the residuals of the governing equations and limited information of sparse data, the proposed PINNs can recover the majority of flow and flame structures in a physics-informed way, even when noise has been added to the sparse data. It is noted that the effectiveness of the models can be influenced by various factors such as the size of the sparse dataset, noise levels, and complexity of turbulence/flame interactions. These factors should be carefully evaluated and addressed to ensure reliable predictions. Overall, this study highlights the potential of PINNs for data assimilation and provide new insights for the development of physics-informed methods in combustion research.Novelty and significanceIn the present work, a physics-informed neural networks (PINNs) framework for turbulent combustion has been established and the possibility of PINNs for high-resolution reconstruction of various field data in turbulent combustion was explored, which is the first of its kind. The velocity, temperature and species mass fractions were simultaneously reconstructed from limited pointwise sparse data, which were compared with the high-fidelity direct numerical simulation data with good agreements. The proposed PINNs have the capability to recover the flow and flame structures for turbulent flames without and with mean shear layers. Various factors such as the size of the sparse dataset, noise level, and complexity of turbulence/flame interactions on the model performance were also assessed. The study highlights the potential of PINNs for data assimilation and provide new insights for the development of physics-informed methods in combustion research.