U-Net applied to retrieve two-dimensional temperature and CO[formula omitted] concentration fields of laminar diffusion flames

Abstract

Direct absorption spectroscopy measurement of the 4.3μm CO2 using interband cascade laser with a high spectral resolution, provides theoretical feasibility to retrieve the spatial distribution of CO2 temperature and concentration in flames. However, retrieving temperature and concentrations from the line-of-sight spectral measurements involves solving non-linear and ill-posed problems. Traditional reconstruction algorithms for axisymmetric flames require multiple line-of-sight measurements in both axial and radial directions. The complicated experimental system limits the reconstruction efficiency. Here, a novel reconstruction model is proposed and demonstrated based on the U-Net model, a fully convolutional network model composed by a encoder–decoder path forming a symmetric U-shaped architecture, which only requires measurements of the central axis of the flame to simultaneously and efficiently reconstruct the two-dimensional temperature and CO2 concentration fields for axisymmetric flames. The U-Net model is trained using datasets from simulated temperature, CO2 concentration and their corresponding spectral optical thickness. The proposed U-Net model has been applied to reconstruct the two-dimensional temperature and CO2 concentration fields of two previously measured laminar diffusion flames. Comparing with results from the traditional Abel inversion and two-line atomic fluorescence thermometry methods, the U-Net reconstruction model can better preserve the spatial continuity and correlation of flame temperature and CO2 concentration from information hidden in the flame spectral images, which considerably simplifies the measurement system and improves the reconstruction performance.