Gaussian Mixture Model-Based Temperature Modelling for Data-Driven Laser Absorption Spectroscopy Tomography

Laser Absorption Spectroscopy Tomography (LAST) is a non-intrusive and robust technique to image two-dimensional temperature distributions for turbulence diagnosis. Given an effective dataset, the deep learning technique has the potential to offer the temperature reconstruction with good fidelity in LAST. However, most existing deep learning-based algorithms utilize elemental Gaussian distributions for training dataset construction. These simulated temperatures cannot characterize the complex turbulence, thus leading to the reconstructions with inadequate structural details. To solve this issue, we introduce a temperature modelling method using Gaussian Mixture Model (GMM), named GMM-LAST, to simulate the dynamic characteristics of temperature distributions and maintain the inherent stochastic characteristics of the turbulence. Thus, the following deep learning-based model could learn the intricacies of real-world turbulent temperature distributions from the training set. GMM-LAST is evaluated by numerical simulation through both quantitative and qualitative analysis. The result indicates that GMM-LAST can simulate the dynamic temperature with high accuracy and sensitivity.