Ying Jin

Combustion diagnostics play an essential role in energy engineering, transportation, and aerospace industries, which has great potential in combustion efficiency improvement and polluting emission control. The three-dimensional (3D) visualization of the combustion field and the measurement of key physical parameters such as temperature, species concentration, and velocity during the combustion process are important topics in the field of combustion diagnostics. Benefiting from the non-contact and non-intrusive advantages of the optical detection method as well as the advantages of the 3D full-field measurement of the measured field by computational tomography, flame chemiluminescence tomography (FCT) has the ability to realize non-intrusive and instantaneous 3D quantitative measurement and 3D full-field visualization of key physical parameters in the combustion process, which has crucial research significance in combustion diagnostics. In this study, we review the progress of FCT technique. First, we provide an extensive review of practical applications of FCT in state-of-the-art combustion diagnostics and research. Then, the basic concepts and mathematical theory of FCT are elaborated. Finally, we introduce the conventional reconstruction algorithm and proceed to more popular artificial intelligence-based algorithms.

Flame chemiluminescence tomography (FCT) plays an important role in combustion monitoring and diagnostics due to the easy implementation and non-intrusion. However, on account of the high data throughput and the inefficiency of the conventional iteration methods, the 3D reconstructions in FCT are typically conducted off-line and time-consuming. In this work, we present a 3D rapid FCT reconstruction system based on convolutional neural networks (CNN) model for practical combustion measurement, which has the ability to reconstruct 3D flame distribution rapidly after training process. First, the numerical simulation has been performed by creating three cases of phantoms which are designed to mimic the 3D conical flame. Next, after the evaluation of loss function and training time, the optimal CNN architecture has been determined and certificated quantitatively. Finally, a real time FCT system consisting of 12 color CCD cameras is realized and multispectral separation algorithm is adopted to extract CH* and C2* components. Certificated by practical measurements testing, the proposed CNN model is able to reconstruct 3D flame structure from real time captured projections with credible accuracy and structure similarity. Furthermore, compared with conventional iteration reconstruction method, the proposed CNN model shows better performance on obviously improving reconstruction speed and it is expected to achieve 3D rapid monitoring of flames.

Synthetic aperture particle image velocimetry (SAPIV) provides a non-invasive means of revealing the physics of complex flows using a compact camera array to resolve the 3D flow field with high temporal and spatial resolution. Intensity-threshold-based methods of reconstructing the flow field are unsatisfactory in nonuniform illuminated fluid flows. This article investigates the characteristics of the focused particles in re-projected image stacks, and presents a convolutional neural network (CNN)-based particle field reconstruction method. The CNN architecture determines the likelihood of each area containing focused particles in the re-projected 3D image stacks. The structural similarity between the images projected by the reconstructed particle field and the images captured from the cameras is then computed, allowing in-focus particles to be extracted. The feasibility of our method is investigated through synthetic simulations and experiments. The results show that the proposed technique achieves remarkable performance, paving the way for non-uniformly illuminated particle field applications in 3D velocity measurements.

Particle image velocimetry (PIV) is a necessary flow field diagnostic technique that provides instantaneous velocimetry information non-intrusively. Three-dimensional (3D) PIV methods can supply the full understanding of a 3D structure, the complete stress tensor, and the vorticity vector in the complex flows. In synthetic aperture particle image velocimetry (SAPIV), the flow field can be measured with large particle intensities from the same direction by different cameras. During SAPIV particle reconstruction, particles are commonly reconstructed by manually setting a threshold to filter out unfocused particles in the refocused images. In this paper, the particle intensity distribution in refocused images is analyzed, and a SAPIV particle field reconstruction method based on an adaptive threshold is presented. By using the adaptive threshold to filter the 3D measurement volume integrally, the three-dimensional location information of the focused particles can be reconstructed. The cross correlations between images captured from cameras and images projected by the reconstructed particle field are calculated for different threshold values. The optimal threshold is determined by cubic curve fitting and is defined as the threshold value that causes the correlation coefficient to reach its maximum. The numerical simulation of a 16-camera array and a particle field at two adjacent time events quantitatively evaluates the performance of the proposed method. An experimental system consisting of a camera array of 16 cameras was used to reconstruct the four adjacent frames in a vortex flow field. The results show that the proposed reconstruction method can effectively reconstruct the 3D particle fields.

The species concentrations of flame chemiluminescence play important role in combustion diagnostics, such as CH* and C2* of hydrocarbon flame, which can provide specific characteristics in combustion control and monitoring. In order to realize both CH* and C2* chemiluminescence intensity detection in propane-air diffusion flame simultaneously, we present three-dimensional dynamic flame detecting method for species concentration determination. Firstly, quantitative flame chemiluminescence multispectral separation technique based on color cameras coupled with double-channel bandpass filters is adopted for dual channel signal division. Next, flame chemiluminescence tomography combining with multi-directional simultaneous capturing is proposed for real time three dimensional observations and detection in flame. Moreover, the proposed technique can quantitatively provide comparison of species intensity between CH* and C2* for further analysis. Considering its credible detecting accuracy and simple requirements, it is believed the proposed technique can be widely used in combustion diagnostics.

Flame tomography of chemiluminescence is a necessary combustion diagnostic technique that provides instantaneous 3D information on flame structure and excited species concentrations. During combustion diagnostics, imaging overexposure always causes missing information, which obviously decreases the accuracy in further reconstructions. In order to compensate imaging overexposure, a hybrid algorithm combining weight correction and Tikhonov’s regularization is proposed in this paper. The intensity of the overexposure region can be estimated via the accumulation of weight coefficients. Meanwhile, Tikhonov’s regularization is utilized to ameliorate the quality of reconstruction. The numerical simulation quantitatively evaluates the performance of the hybrid algorithm. Additionally, an experiment system consisting of 12 cameras was established to reconstruct the 3D combustion structure of axisymmetric flame with different exposure time settings. This work further investigates dynamic nonaxisymmetric propane diffusion flame. The obtained results show that the hybrid algorithm can effectively reveal the flame structure less influenced by imaging overexposure and achieve better results.