Untrained neural network for linear tomographic absorption spectroscopy

Linear tomographic absorption spectroscopy(LTAS) is a non-destructive diagnostic technique widely employed for gas sensing.The inverse problem of LTAS represents a classic example of an ill-posed problem. Linear iterative algorithms are commonly employed to address such problems, yielding generally poor reconstruction results due to the incapability to incorporate suitable prior conditions within the reconstruction process. Data-driven deep neural networks(DNN) have shown the potential to yield superior reconstruction results;however, they demand a substantial amount of measurement data that is challenging to acquire.To surmount this limitation, we proposed an untrained neural network(UNN) to tackle the inverse problem of LTAS. In conjunction with an early-stopping method based on running variance, UNN achieves improved reconstruction accuracy without supplementary training data. Numerical studies are conducted to explore the optimal network architecture of UNN and to assess the reliability of the early-stopping method. A comparison between UNN and superiorized ART(SUP-ART) substantiates the exceptional performance of UNN.

Volumetric imaging of flame refractive index, density, and temperature using background-oriented Schlieren tomography

Volumetric imaging represents one of the major development trends of flow diagnostics due to both the advancement in hardware and the requirement for more information to further understand complicated turbulent and/or reactive flows. Backgroundoriented Schlieren tomography(BOST) has become increasingly popular due to its experimental simplicity. It has been demonstrated to be capable of simultaneously recovering the distributions of refractive index, density, and temperature of flows.However, its capability in thermometry has only been demonstrated under the axisymmetric assumption, which greatly limits its applicability. In this work, we dedicated to developing a cost-effective BOST system for the simultaneous retrieval of refractive index, density, and temperature distributions for the asymmetric flame. A few representative tomographic inversion algorithms were assessed as well. Both numerical and experimental demonstrations were conducted and the results show that our implemented BOST can successfully reconstruct the three-dimensional temperature distribution with a satisfactory accuracy.

Benchmark evaluation of tomographic algorithms for simultaneous reconstruction of temperature and volume fraction fields of soot and metal-oxide nanoparticles in non-uniform flames

With the growing applications of nanofluid flame, the monitoring and controlling of its combustion process is of paramount importance. Thus, it is necessary to develop diagnosing methods which can simultaneously image important parameters such as temperature and volume fractions of soot, metal-oxide nanoparticles. Tomographic emission spectroscopy is an effective method which has been proposed for this purpose. However, the inversion process was only reported with least-squares QR decomposition(LSQR) so far and there are numerous well-established reconstruction algorithms which have not been utilized yet.Thus, this work aims to perform systematic comparative studies on several representative algorithms for the inversion process. In the simulative studies, algorithms including Tikhonov regularization, algebraic reconstruction technique(ART), LSQR,Landweber algorithm, maximum likelihood expectation maximization(MLEM), and ordered subset expectation maximization(OSEM) were discussed. The effects of the number of iterations, the signal-to-noise ratio, and the number of projections and the calibration error in projection angles on the performance of the algorithms were investigated. Advice on selecting the suitable algorithms under different application conditions is then provided according to the extensive numerical studies.

Development of a single-camera volumetric thermometry for gas flows based on space division multiplexing

Optical imaging methods have been widely applied in combustion diagnostics due to their high sensitivity, versatility, non-intrusiveness, and amenability for implementation in harsh environment [1,2]. Compared with traditional intrusive diagnostics, such as thermometry with thermocouples and manometry with pressure transducers, planar imaging methods provide richer insights into fluid dynamics and complex combustion phenomena.

Three-dimensional concentration field imaging in a swirling flame via endoscopic volumetric laser-induced fluorescence at 10-kHz-rate

Optical combustion diagnostics are indispensable tools to investigate flame dynamics and excavate in-situ quantities of flames,such as velocity,temperature,concentration of key intermediates and pressure without introducing probe perturbation.For example,velocity and flame temperature can be obtained using techniques such as particle image velocimetry,coherent anti-Stokes Raman scattering spectroscopy,Rayleigh scattering and tunable diode laser absorption spectroscopy,respectively.