Deep learning assisted pressure measurements using femtosecond laser-induced grating scattering technique

Abstract

The femtosecond laser-induced grating scattering (fs-LIGS) technique has recently been developed and applied for temperature and pressure measurements. In this work, we combined deep learning with the fs-LIGS technique to predict the gas-phase pressure from raw signals without data post-processing. Two deep learning models, a fully connected neural network and a convolutional neural network, were trained to master the hidden relationship between the features of the raw signal traces and the corresponding pressure under which the signal was recorded. Accurate pressure predictions by both models were achieved as mean percentage errors in model-predicted pressures compared to actual values within −4%–2%. These results suggest the feasibility of combining the deep-learning concept with the fs-LIGS technique for instantaneous pressure determination. Given the proper training of the models, this strategy could be extended to the simultaneous measurement of multiple thermodynamic quantities in real-time combustion and reacting flow diagnostics.