18 research outputs found
Flame monitoring of a model swirl injector 1D tunable diode laser absorption spectroscopy tomography
Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging
Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been
widely used for in situ combustion diagnostics, yielding images of both species
concentration and temperature. The temperature image is generally obtained from
the reconstructed absorbance distributions for two spectral transitions, i.e.
two-line thermometry. However, the inherently ill-posed nature of tomographic
data inversion leads to noise in each of the reconstructed absorbance
distributions. These noise effects propagate into the absorbance ratio and
generate artefacts in the retrieved temperature image. To address this problem,
we have developed a novel algorithm, which we call Relative Entropy Tomographic
RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation
is introduced for high-fidelity temperature image retrieval from jointly
reconstructed two-line absorbance distributions. We have carried out numerical
simulations and proof-of-concept experiments to validate the proposed
algorithm. Compared with the well-established Simultaneous Algebraic
Reconstruction Technique (SART), the RETRO algorithm significantly improves the
quality of the tomographic temperature images, exhibiting excellent robustness
against TDLAS tomographic measurement noise. RETRO offers great potential for
industrial field applications of TDLAS tomography, where it is common for
measurements to be performed in very harsh environments.Comment: Preprint submitted to IEEE Transactions on Instrumentation and
Measuremen
Two-dimensional temperature measurement in a high temperature and high pressure combustor using CT-TDLAS with a wide scanning laser at 1335-1375nm
Tunable diode laser absorption spectroscopy (TDLAS) technology is a developing method for temperature and species concentration measurements with the features of non-contact, high precision, high sensitivity, etc. The difficulty of two-dimensional (2D) temperature measurement in actual combustors has not yet been solved because of pressure broadening of absorption spectra, optical accessibility, etc. In this study, the combination of computed tomography (CT) and TDLAS with a wide scanning laser at 1335-1375nm has been applied to a combustor for 2D temperature measurement in high temperature of 300-2000K and high pressure of 0.1-2.5MPa condition. An external cavity type laser diode with wide wavelength range scanning at 1335-1375nm was used to evaluate the broadened H2O absorption spectra due to the high temperature and high pressure effect. The spectroscopic database in high temperature of 300-2000K and high pressure of 0.1-5.0MPa condition has been revised to improve the accuracy for temperature quantitative analysis. CT reconstruction accuracy was also evaluated in different cases, which presented the consistent temperature distribution between CT reconstruction and assumed distributions. The spatial and temporal distributions of temperature in the high temperature and high pressure combustor were measured successfully by CT-TDLAS using the revised spectroscopic database
Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography
As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption
Spectroscopy (TDLAS) tomography has been widely used for imaging of
two-dimensional temperature distributions in reactive flows. Compared with the
computational tomographic algorithms, Convolutional Neural Networks (CNNs) have
been proofed to be more robust and accurate for image reconstruction,
particularly in case of limited access of laser beams in the Region of Interest
(RoI). In practice, flame in the RoI that requires to be reconstructed with
good spatial resolution is commonly surrounded by low-temperature background.
Although the background is not of high interest, spectroscopic absorption still
exists due to heat dissipation and gas convection. Therefore, we propose a
Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses
efficiently the training and learning resources for temperature imaging in the
RoI with good spatial resolution, and (b) reconstructs the less spatially
resolved background temperature by adequately addressing the integrity of the
spectroscopic absorption model. In comparison with the traditional CNN, the
newly introduced pseudo inversion of the RoI sensitivity matrix is more
penetrating for revealing the inherent correlation between the projection data
and the RoI to be reconstructed, thus prioritising the temperature imaging in
the RoI with high accuracy and high computational efficiency. In this paper,
the proposed algorithm was validated by both numerical simulation and lab-scale
experiment, indicating good agreement between the phantoms and the
high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen
A stability and spatial-resolution enhanced laser absorption spectroscopy tomographic sensor for complex combustion flame diagnosis
A novel stable laser absorption spectroscopy (LAS) tomographic sensor with enhanced stability and spatial resolution is developed and applied to complex combustion flame diagnosis. The sensor reduces the need for laser collimation and alignment even in extremely harsh environments and improves the stability of the received laser signal. Furthermore, a new miniaturized laser emission module was designed to achieve multi-degree of freedom adjustment. The full optical paths can be sampled by 8 receivers, with such arrangement, the equipment cost can be greatly reduced, at the same time, the spatial resolution is improved. In fact, 100 emitted laser paths are realized in a limited space of 200mm×200 mm with the highest spatial resolution of 1.67mm×1.67 mm. The stability and penetrating spatial resolution of the LAS tomographic sensor were validated by both simulation and field experiments on the afterburner flames. Tests under two representative experiment states, i.e., the main combustion and the afterburner operation states, were conducted. Results show that the error under the main combustion state was about 4.32% and, 5.38% at the afterburner operation state. It has been proven that this proposed sensor can provide better tomographic measurements for combustion diagnosis, as an effective tool for improving performances of afterburners
CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography
Chemical Species Tomography (CST) has been widely used for in situ imaging of
critical parameters, e.g. species concentration and temperature, in reactive
flows. However, even with state-of-the-art computational algorithms the method
is limited due to the inherently ill-posed and rank-deficient tomographic data
inversion, and by high computational cost. These issues hinder its application
for real-time flow diagnosis. To address them, we present here a novel
CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and
simultaneous imaging of species concentration and temperature. CSTNet
introduces a shared feature extractor that incorporates the CST measurement and
sensor layout into the learning network. In addition, a dual-branch
architecture is proposed for image reconstruction with crosstalk decoders that
automatically learn the naturally correlated distributions of species
concentration and temperature. The proposed CSTNet is validated both with
simulated datasets, and with measured data from real flames in experiments
using an industry-oriented sensor. Superior performance is found relative to
previous approaches, in terms of robustness to measurement noise and
millisecond-level computing time. This is the first time, to the best of our
knowledge, that a deep learning-based algorithm for CST has been experimentally
validated for simultaneous imaging of multiple critical parameters in reactive
flows using a low-complexity optical sensor with severely limited number of
laser beams.Comment: Submitted to IEEE Transactions on Neural Networks and Learning
System