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It is essential to investigate the light field camera parameters for the accurate flame temperature measurement because the sampling characteristics of the flame radiation can be varied with them. In this study, novel indices of the light field camera were proposed to investigate the directional and spatial sampling characteristics of the flame rad...
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... parameters L pm , L mv , L vl , L lo , and D can be determined by Eqs. (1)- (5) based on the parameters illustrated in Tables 1 and 2. The coordinate system utilized in this study is illustrated in Fig. 4, and the center of the CCD plane is considered as the origin point (0, 0, 0). The backward ray tracing model is used to trace the incident ray corresponding to an individual pixel based on the principle of geometric optics. [28] In the ray trac- ing process, the ray originating from an individual pixel passes through a specific point ...
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... characterize the directional sampling characteristics of the flame, the directional sampling indices including the cone angle of a single pixel (CA) and the sampling angle (SA) of the object side have been proposed and defined in this study. In Fig. 4, CA and SA are defined as the red cone and the blue cone, respectively. The cone angle of a single pixel (CA) is the solid angle corresponding to the rays collected by an individual LF sam- pling. Based on geometric optics, CA can be calculated ...
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... spatial resolution (SR) and the angle of view (AOV ) have been defined and illustrated in this study to characterize the LF spatial sampling. In the CCD sensor, a square pixel is conjugate to a square area on the object plane (in Fig. 4). The distance between two adjacent square areas is defined as the spatial resolution (SR). Based on the thin lens theory, [28] SR is calculated ...
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... parameters L pm , L mv , L vl , L lo , and D can be determined by Eqs. (1)-(5) based on the parameters illustrated in Tables 1 and 2. The coordinate system utilized in this study is illustrated in Fig. 4, and the center of the CCD plane is considered as the origin point (0, 0, 0). The backward ray tracing model is used to trace the incident ray corresponding to an individual pixel based on the principle of geometric optics. [28] In the ray tracing process, the ray originating from an individual pixel passes through a specific point of ...
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... characterize the directional sampling characteristics of the flame, the directional sampling indices including the cone angle of a single pixel (CA) and the sampling angle (SA) of the object side have been proposed and defined in this study. In Fig. 4, CA and SA are defined as the red cone and the blue cone, respectively. The cone angle of a single pixel (CA) is the solid angle corresponding to the rays collected by an individual LF sampling. Based on geometric optics, CA can be calculated ...
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... spatial resolution (SR) and the angle of view (AOV ) have been defined and illustrated in this study to characterize the LF spatial sampling. In the CCD sensor, a square pixel is conjugate to a square area on the object plane (in Fig. 4). The distance between two adjacent square areas is defined as the spatial resolution (SR). Based on the thin lens theory, [28] SR is calculated ...
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Citations
... Consequently, the modified reconstruction algorithm may not be ideal for improving the spatial information and, in some cases, it may increase the computational cost. Therefore, it is necessary to optimize the LFC parameters to obtain adequate spatial information of flame radiation [18] . So that the correlation between the adjacent sections can be reduced, and the depth resolution of LFSP can be improved. ...
... where , 0 is the spectral radiation intensity when the radiation is emitted, is the length of the path that the beam of radiation propagating through the flame medium, which can be solved by ray-tracing technique [18] . The attenuation coefficient is given by = , + , , where , and , are the absorption coefficient and the scattering coefficient, respectively. ...
... It can be seen that the depth resolution ( ) varies significantly with the LFCs. The depth resolution of the Raytrix R29 [18] is 2027.04 mm , which is worsened than the other LFCs. ...
The light field sectioning pyrometry (LFSP) has proven a significant advancement for in-situ measurement of flame temperature through a single light field camera. However, the spatial resolution of LFSP is limited, which severely inhibits the measurement accuracy. This paper aims to evaluate the spatial resolution of LFSP for flame temperature measurement quantitatively. A theoretical model of the spatial resolution is established based on optical parameters and point spread function of the light field camera. The spatial resolution is then numerically analyzed with different parameters of light field cameras. Based on the theoretical model, a novel cage-typed light field camera with a higher spatial resolution of LFSP is developed and experimentally evaluated. A significant improvement of spatial resolution about 17% and 50% in lateral and depth directions, respectively, is achieved. Results show that the spatial resolution is in good agreement with the theoretical model. The LFSP is then evaluated under different combustion cases and their temperatures are reconstructed.
... The determination of thermal boundary condition and/or physical properties often involves inverse heat transfer problems (IHTPs). During recent decades, lots of optimization methodologies have been developed to solve IHTPs, by which the thermal boundary condition or physical properties of participating medium are identified [9][10][11][12][13][14][15]. ...
A hybrid optimization technique, which combines decentralized fuzzy inference method (DFIM) and sequential quadratic programming (SQP) algorithm, is developed to reconstruct the thermal boundary condition and physical properties of participating medium simultaneously. The coupled radiation-conduction heat transfer in the medium is solved by finite volume method in combination with discrete ordinate method. The reconstruction task is formulated as an inverse problem which is solved by the hybrid DFIM-SQP technique from the knowledge of surface temperature and exit radiative intensity. Retrieval results demonstrate that the time-dependent heat flux, absorption coefficient, scattering coefficient and thermal conductivity of participating medium can be accurately retrieved by the present methodology. The proposed DFIM-SQP technique is more accurate and efficient in solving the simultaneous reconstruction problem than DFIM, conjugate gradient method, SQP, particle swarm optimization and krill herd algorithms.
... There are many kinds of techniques for flame optical diagnostics, such as laser-based diagnostics [8][9][10][11][12], radiation imaging method [13][14][15][16], which are precise and visualized [17,18]. In a noncontact flame detection device, the light field camera is applied to flame detection as a new type of radiative-imaging-based optical detection device [19,20], and its applicability has been initially proved [21][22][23][24]. The light field camera is provided with a microlens array between the main lens and the imaging screen so that the four-dimensional (4D) light field information of the object can be detected and recorded [25,26]. ...
The light field camera has emerged as an optical flame temperature detection device owing to the refocusing feature. Combining this feature with the deconvolution algorithm, a three-dimensional flame temperature distribution can be reconstructed. However, the accuracy of reconstructing the flame temperature is limited by the actual image position. Based on the established flame and light field camera model, the proposed Lucy-Richardson and Nearest Neighbor Filtering joint deconvolution algorithm is applied to sectioned temperature reconstruction. Thus, the effects of image position using light field camera on temperature reconstruction are verified. The relative error of flame temperature reconstruction is less than 10%, if the flame is imaged in the middle of the light field image. However, if the flame is imaged at the image edge, the edge distortion and defocused state of the camera increase the relative error to less than 11% and 14%, respectively. The effects of image positions are not too significant for temperature reconstruction using light field camera.
... Gradient-based methods are deterministic techniques with advantages of high efficiency and accuracy. Quite a few inverse design problems have been successfully solved using gradient-based methods [1,6,[22][23][24][25][26][27][28][29][30][31]. Howell et al. [6,22,23] applied Kiefer-Wolfowitz, steepest descent, Newton, conjugate gradient and quasi-Newton methods to optimize the geometry of 2D radiative enclosures to produce desired heat flux and temperature distributions on a region of the enclosure surface. ...
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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 accurately. However, the angular arrangement of the multi-light field imaging technique is a crucial factor that affects the accuracy of the temperature reconstruction. In this study, a method is proposed by integrating a Quantum-behaved Particle Swarm Optimization algorithm to optimise the angular arrangement of the multi-light field imaging technique and to achieve optimal accuracy of the flame temperature reconstruction. The proposed method is evaluated through numerical and experimental studies. The proposed method is also evaluated under different angular arrangements of the multi-light field camera system. Numerical results demonstrate that the optimal angular arrangement provides better reconstruction accuracy in comparison with different angular arrangements. The experimental results of the reconstructed temperature distributions of ethylene-air bimodal diffusion flames show that the proposed method has good applicability.
Light field (LF) imaging, as an emerging temperature measurement technique, can simultaneously capture the intensity and direction information of radiation in the combustion flow field and has recently attracted extensive attention. However, the dense sampling of the LF introduces a large amount of redundant information and makes the plenoptic data susceptible to noise pollution, which can significantly affect the performance of 3D temperature field reconstruction. To address this problem, a novel approach, i.e., light field compression and noise reduction (LFCNR), is proposed to extract the main features of the projection matrix and separate the noise-related and signal-related subspaces of the plenoptic data, thus improving the reconstruction accuracy and efficiency. The performance of the proposed LFCNR method and the effect of the hyper-parameters on reconstructed quality are investigated thoroughly. It has been observed that the proposed LFCNR-based method for 3D temperature tomography imaging has higher accuracy, more efficiency, and better anti-noise ability compared to the well-established method. LFCNR can improve the reconstructed temperature accuracy by approximately 20%, and the calculation time is shortened to 10%. Priori smoothing is introduced into the optimization objective to constrain the fluctuation of the retrieval temperature, called LFCNR-PS, which can further improve the reconstructed accuracy of axisymmetric and non-axisymmetric fields about 1.79% and 1.23%. Furthermore, experiments of the LFCNR-based method are carried out to reconstruct the 3D temperature field of ethylene diffusion flames. The reconstructed temperature slices demonstrate that the flame topology can be clearly and rapidly identified at different depths. The efficiency and robustness of the proposed method can be applied to the harsh environment, which will be beneficial to the reliable operation of thermal equipment, the study of turbulent chemical reaction mechanisms, fuel utilization, and the optimization design of the engine combustion chamber.
Flame temperature measurement through a light field camera shows an attractive research interest due to its capabilities of obtaining spatial and angular rays' information by a single exposure. However, the sampling information collected by the light field camera is vast and most of them are redundant. The reconstruction process occupies a larger computing memory and time-consuming. We propose a novel approach i.e., feature rays under-sampling (FRUS) to reduce the light field sampling redundancy and thus improve the reconstruction efficiency. The proposed approach is evaluated through numerical and experimental studies. Effects of under-sampling methods, flame dividing voxels, noise levels and light field camera parameters are investigated. It has been observed that the proposed approach provides better anti-noise ability and reconstruction efficiency. It can be valuable not only for the flame temperature reconstruction but also for other applications such as particle image velocimetry and light field microscope.
Light-field imaging technique provides a new approach to non-intrusive temperature measurement, which can achieve 3D measurement via single detector equipment. We consider wave characteristics of radiation rays in the light-field imaging model in this work. We propose an improved light-field imaging model of an emitting and absorbing medium by combining wave optics theory and radiation transfer process in semitransparent media. The combination of Fresnel diffraction theory and phase conversion principles of the lens provides a theoretical basis for a description of radiation ray propagation in space and across the lens. The point spread function of the light-field camera can be calculated and used to attain a light-field image in accordance with the convolution imaging model. The light-field image of the flame is regarded as input to reconstruct the soot temperature field of a 3D flame. Inverse problem is formulated as a linear optimization problem and solved by Landweber algorithm. Simulated reconstruction results show that axisymmetric and non-axisymmetric temperature distribution can be reconstructed successfully, thereby verifying the applicability of the presented method. The effect of the measurement noise and the Tikhonov regularization on the reconstructed quality of the temperature are investigated as well. The results show that the temperature can be reconstructed with acceptable accuracy under different noise levels. Tikhonov regularization method is proven available in this problem.
