Geometric calibration of focused light field camera for 3-D flame temperature measurement

Focused light field camera can be used to measure three-dimensional (3-D) temperature field of a flame because of its ability to record intensity and direction information of each ray from flame simultaneously. This work aims to develop a suitable geometric calibration method of focused light field camera for 3-D flame temperature measurement. A modified method based on Zhang's camera calibration is developed to calibrate the camera and the measurement system. A single focused light-field camera is used to capture images of bespoke calibration board for calibration in this study. Geometric parameters including intrinsic (i.e., camera parameters) and extrinsic (i.e., camera connecting with the calibration board) of the focused light field camera are calibrated to trace the ray projecting onto each pixel on CCD (charge-coupled device) sensor. Instead of using line features, corner point features are directly utilized for the calibration. The characteristics of focused light field camera including one 3-D point corresponding to several image points and matching main lens and microlens f-numbers, are used for calibration. Results with a focused light field camera are presented and discussed. Preliminary 3-D temperature distribution of a flame is also investigated and presented

Simultaneous measurement of flame temperature and absorption coefficient through LMBC-NNLS and plenoptic imaging techniques

It is important to identify boundary constraints in the inverse algorithm for the reconstruction of flame temperature because a negative temperature can be reconstructed with improper boundary constraints. In this study, a hybrid algorithm, a combination of Levenberg-Marquardt with boundary constraint (LMBC) and non-negative least squares (NNLS), was proposed to reconstruct the flame temperature and absorption coefficient simultaneously by sampling the multi-wavelength flame radiation with a colored plenoptic camera. To validate the proposed algorithm, numerical simulations were carried out for both the symmetric and asymmetric distributions of the flame temperature and absorption coefficient. The plenoptic flame images were modeled to investigate the characteristics of flame radiation sampling. Different Gaussian noises were added into the radiation samplings to investigate the noise effects on the reconstruction accuracy. Simulation results showed that the relative errors of the reconstructed temperature and absorption coefficient are less than 10, indicating that accurate and reliable reconstruction can be obtained by the proposed algorithm. The algorithm was further verified by experimental studies, where the reconstructed results were compared with the thermocouple measurements. The simulation and experimental results demonstrated that the proposed algorithm is effective for the simultaneous reconstruction of the flame temperature and absorption coefficient

Evidence supported by Mendelian randomization: impact on inflammatory factors in knee osteoarthritis

BackgroundPrior investigations have indicated associations between Knee Osteoarthritis (KOA) and certain inflammatory cytokines, such as the interleukin series and tumor necrosis factor-alpha (TNFα). To further elaborate on these findings, our investigation utilizes Mendelian randomization to explore the causal relationships between KOA and 91 inflammatory cytokines.MethodsThis two-sample Mendelian randomization utilized genetic variations associated with KOA from a large, publicly accessible Genome-Wide Association Study (GWAS), comprising 2,227 cases and 454,121 controls of European descent. The genetic data for inflammatory cytokines were obtained from a GWAS summary involving 14,824 individuals of European ancestry. Causal relationships between exposures and outcomes were primarily investigated using the inverse variance weighted method. To enhance the robustness of the research results, other methods were combined to assist, such as weighted median, weighted model and so on. Multiple sensitivity analysis, including MR-Egger, MR-PRESSO and leave one out, was also carried out. These different analytical methods are used to enhance the validity and reliability of the final results.ResultsThe results of Mendelian randomization indicated that Adenosine Deaminase (ADA), Fibroblast Growth Factor 5(FGF5), and Hepatocyte growth factor (HFG) proteins are protective factors for KOA (IVWADA: OR = 0.862, 95% CI: 0.771–0.963, p = 0.008; IVWFGF5: OR = 0.850, 95% CI: 0.764–0.946, p = 0.003; IVWHFG: OR = 0.798, 95% CI: 0.642–0.991, p = 0.042), while Tumor necrosis factor (TNFα), Colony-stimulating factor 1(CSF1), and Tumor necrosis factor ligand superfamily member 12(TWEAK) proteins are risk factors for KOA. (IVWTNFα: OR = 1.319, 95% CI: 1.067–1.631, p = 0.011; IVWCSF1: OR = 1.389, 95% CI: 1.125–1.714, p = 0.002; IVWTWEAK: OR = 1.206, 95% CI: 1.016–1.431, p = 0.032).ConclusionThe six proteins identified in this study demonstrate a close association with the onset of KOA, offering valuable insights for future therapeutic interventions. These findings contribute to the growing understanding of KOA at the microscopic protein level, paving the way for potential targeted therapeutic approaches

Flame temperature reconstruction through multi-plenoptic camera technique

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only irregular but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only adequate flame radiative information but also reconstruct complex flame temperature accurately. In this paper, a novel multi-plenoptic camera imaging technique is proposed which is not only provide adequate flame radiative information from two different directions but also reconstruct the complex flame temperature distribution accurately. An inverse algorithm i.e., Non-Negative Least Squares is used to reconstruct the flame temperature. The bimodal asymmetric temperature distribution is considered to verify the feasibility of the proposed system. Numerical simulations and experiments were carried out to evaluate the performance of the proposed technique. Simulation results demonstrate that the proposed system is able to provide higher reconstruction accuracy although the reconstruction accuracy decreases with the increase of noise levels. Meanwhile, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality even with higher noise levels. The proposed technique is further verified by experimental studies. The experimental results also demonstrate that the proposed technique is effective and feasible for the reconstruction of flame temperature. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex flame temperature fields more precisely

Tomographic Reconstruction of Light Field PIV Based on Backward Ray-Tracing Technique

The calculation of the weight matrix is one of the key steps of the tomographic reconstruction in the light field particle image velocimetry (light field PIV) system. At present, the existing calculation method of the weight matrix in light field PIV based on the forward ray-tracing technique (named as Fahringer’s method) is very time-consuming. To improve the computational efficiency of the weight matrix, this paper presents a computational method for the weight matrix based on the backward ray-tracing technique in combination with Gaussian function (named as Gaussian function method). An Expectation-Maximization (EM) algorithm is employed for the reconstruction of the 3D particle field, and a summed line-ofsight (SLOS) estimation is further used to accelerate the reconstruction process. The computational accuracy and efficiency of the weight matrix, the reconstruction quality of the 3D particle field, and the velocity field accuracy by Gaussian function method are numerically investigated. Finally, experiments are carried out to verify the feasibility of the weight matrix by Gaussian function method. Numerical results illustrated that Gaussian function method can improve the computational efficiency of the weight matrix by more than 10 times. SLOS is capable of further accelerating the computational efficiency of the overall reconstruction process including the pre-determination, the calculation of the weight matrix and the reconstruction. The velocity field accuracy by Gaussian function method is almost the same as that by Fahringer’s method. The experimental results of the 3D-3C velocity field of a laminar flow further verify the feasibility of the computational method for the weight matrix based on Gaussian function

Flow characteristics in pressurized oxy-fuel fluidized bed under hot condition

Pressurized oxy-fuel fluidized bed (POFB) combustion is regarded as a promising technology for carbon capture from coal-fired power plants. High pressure and temperature conditions have important impacts on the flow characteristic of fluidized bed, and understanding them will help to optimize the design and operation of the POFB boiler. In this work, experiments were carried out in two pressurized fluidized bed (PFB) devices (a hot PFB and a “visual PFB”) both operated under high temperature (20-800 °C) and high pressure conditions (0.1-1.0 MPa). Four parameters including the minimum fluidization velocity (umf), the minimum bubbling velocity (umb), bubble diameter (Db) and bubble frequency (f) were examined in this study. Results showed that the umf decreases with rising pressure and temperature. Based on our results a formula was fitted for calculating the minimum fluidization velocity in PFB, with a relative error less than 15%. With the increase of fluidization number (w), the bubble size and tail vortex increased gradually, the bubbles tended to merge, and the shape of bubbles became more irregular. The Db decreases with the increase of temperature and pressure at the same w. The f increases with increased w, while it decreased with the increase of temperature and pressure

An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control and environmental protection. This study proposes a novel ensemble deep learning model for exhaust emissions (NOx and CO2) prediction. In this ensemble learning model, the stacked denoising autoencoder is established to extract the deep features of flame images. Four forecasting engines include artificial neural network, extreme learning machine, support vector machine and least squares support vector machine are employed for preliminary prediction of NOx and CO2 emissions based on the extracted image deep features. After that, these preliminary predictions are combined by Gaussian process regression in a nonlinear manner to achieve a final prediction of the emissions. The effectiveness of the proposed ensemble deep learning model is evaluated through 4.2Â MW heavy oil-fired boiler flame images. Experimental results suggest that the predictions are achieved from the four forecasting engines are inconsistent, however, an accurate prediction accuracy has been achieved through the proposed model. The proposed ensemble deep learning model not only provides accurate point prediction but also generates satisfactory confidence interval

Approach to select optimal cross-correlation parameters for light field particle image velocimetry

The light field particle image velocimetry (LF-PIV) has shown a great potential for three-dimensional (3D) flow measurement in space-constrained applications. Usually, the parameters of the cross-correlation calculation in the LF-PIV are chosen based on empirical analysis or introduced from conventional planar PIV, which lowers the accuracy of 3D velocity field measurement. This study presents an approach to selecting optimal parameters of the cross-correlation calculation and thereby offers systematic guidelines for experiments. The selection criterion of the interrogation volume size is studied based on the analysis of the valid detection probability of the correlation peak. The optimal seeding concentration and the size of tracer particles are then explored through synthetic Gaussian vortex field reconstruction. The optimized parameters are employed in a cylinder wake flow measurement in a confined channel. A comparative study is conducted between the LF-PIV and a planar PIV system. Results indicate that the LF-PIV along with the optimized parameters can measure the 3D flow velocity of the cylinder wakes accurately. It has been observed that the mean and max errors of velocity decrease by 32.6% and 18.8%, respectively compared to the related LF-PIV techniques without consideration of optimal parameters. Therefore, it is suggested that the optimized cross-correlation parameters in the LF-PIV can improve the accuracy of 3D flow measurement

Three-dimensional temperature field measurement of flame using a single light field camera

Compared with conventional camera, the light field camera takes the advantage of being capable of recording the direction and intensity information of each ray projected onto the CCD (charge couple device) sensor simultaneously. In this paper, a novel method is proposed for reconstructing three-dimensional (3-D) temperature field of a flame based on a single light field camera. A radiative imaging of a single light field camera is also modeled for the flame. In this model, the principal ray represents the beam projected onto the pixel of the CCD sensor. The radiation direction of the ray from the flame outside the camera is obtained according to thin lens equation based on geometrical optics. The intensities of the principal rays recorded by the pixels on the CCD sensor are mathematically modeled based on radiative transfer equation. The temperature distribution of the flame is then reconstructed by solving the mathematical model through the use of least square QR-factorization algorithm (LSQR). The numerical simulations and experiments are carried out to investigate the validity of the proposed method. The results presented in this study show that the proposed method is capable of reconstructing the 3-D temperature field of a flame