U-Net applied to retrieve two-dimensional temperature and CO2 concentration fields of laminar diffusion flames
Direct absorption spectroscopy measurement of the 4. 3 μ m CO 2 using interband cascade
laser with a high spectral resolution, provides theoretical feasibility to retrieve the spatial …
laser with a high spectral resolution, provides theoretical feasibility to retrieve the spatial …
Physics-trained neural network for sparse-view volumetric laser absorption imaging of species and temperature in reacting flows
A deep learning method for laser absorption tomography was developed to effectively
integrate physical priors related to flow-field thermochemistry and transport. Mid-fidelity …
integrate physical priors related to flow-field thermochemistry and transport. Mid-fidelity …
Three-dimensional rapid visualization of flame temperature field via compression and noise reduction of light field imaging
ZT Niu, H Qi, JW Shi, YT Ren, MJ He, W Zhou - … Communications in Heat …, 2022 - Elsevier
Light field (LF) imaging, as an emerging temperature measurement technique, can
simultaneously capture the intensity and direction information of radiation in the combustion …
simultaneously capture the intensity and direction information of radiation in the combustion …
2D mid-infrared laser-absorption imaging for tomographic reconstruction of temperature and carbon monoxide in laminar flames
This manuscript presents the design and initial application of a mid-infrared laser-absorption-
imaging (LAI) technique for two-dimensional (2D) measurements and tomographic …
imaging (LAI) technique for two-dimensional (2D) measurements and tomographic …
Optimum angular arrangement of a multi-light field imaging technique for flame temperature reconstruction
A burner array produces a multi-modal flame temperature field and a compact combustion
region. A multi-light field imaging technique can retrieve the multi-modal flame temperature …
region. A multi-light field imaging technique can retrieve the multi-modal flame temperature …
Reconstructing temperature fields from OH distribution and soot volume fraction in turbulent flames using an artificial neural network
X Nie, W Zhang, X Dong, PR Medwell, GJ Nathan… - Combustion and …, 2024 - Elsevier
We present a methodology based on artificial neural networks for reconstructing flame
temperature fields from planar distributions of hydroxyl (OH) radicals and soot volume …
temperature fields from planar distributions of hydroxyl (OH) radicals and soot volume …
Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography
J Si, G Li, Y Cheng, R Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
As an in situ combustion diagnostic tool, tunable diode laser absorption spectroscopy
(TDLAS) tomography has been widely used for imaging 2-D temperature distributions in …
(TDLAS) tomography has been widely used for imaging 2-D temperature distributions in …
Reconstruction and simulation of temperature and CO2 concentration in an axisymmetric flame based on TDLAS
Abstract Two-dimensional (2D) distributions of temperature and species concentration are
crucial information to understand PAH inception and soot formation in diffusion flames. We …
crucial information to understand PAH inception and soot formation in diffusion flames. We …
Efficient two-dimensional scalar fields reconstruction of laminar flames from infrared hyperspectral measurements with a machine learning approach
The latest hyperspectral measurements of combustion flames by Rhoby et al.(2014)
provided extensive spatially and spectrally resolved information of flame radiation, which …
provided extensive spatially and spectrally resolved information of flame radiation, which …
Deep neural network inversion for 3D laser absorption imaging of methane in reacting flows
Mid-infrared laser absorption imaging of methane in flames is performed with a learning-
based approach to the limited view-angle inversion problem. A deep neural network is …
based approach to the limited view-angle inversion problem. A deep neural network is …