Prediction of combustion state through a semi-supervised learning model and flame imaging

Accurate prediction of combustion state is crucial for an in-depth understanding of furnace performance and optimize operation conditions. Traditional data-driven approaches such as artificial neural networks and support vector machine incorporate distinct features which require prior knowledge for feature extraction and suffers poor generalization for unseen combustion states. Therefore, it is necessary to develop an advanced and accurate prediction model to resolve these limitations. This study presents a novel semi-supervised learning model integrating denoising autoencoder (DAE), generative adversarial network (GAN) and Gaussian process classifier (GPC). The DAE network is established to extract representative features of flame images and the network trained through the adversarial learning mechanism of the GAN. Structural similarity (SSIM) metric is introduced as a novel loss function to improve the feature learning ability of the DAE network. The extracted features are then fed into the GPC to predict the seen and unseen combustion states. The effectiveness of the proposed semi-supervised learning model, i.e., DAE-GAN-GPC was evaluated through 4.2 MW heavy oil-fired boiler furnace flame images captured under different combustion states. The averaged prediction accuracy of 99.83% was achieved for the seen combustion states. The new states (unseen) were predicted accurately through the proposed model by fine-tuning of GPC without retraining the DAE-GAN and averaged prediction accuracy of 98.36% was achieved for the unseen states. A comparative study was also carried out with other deep neural networks and classifiers. Results suggested that the proposed model provides better prediction accuracy and robustness capability compared to other traditional prediction models

PIV study of vortex breakdown in low- and high-swirl flames in a model combustor

The present work is devoted to a comparative study of low- and high-swirl flows in a model lean combustor. Lean methane-air mixture with equivalence ratio of 0.6 burned at atmospheric pressure. A high-repetition stereoscopic PIV system was used for the velocity measurements both in the reacting and non-reacting flows. During analysis of the velocity data sets, the emphasis was put on application of statistical tools to build a reduced model of flows as stochastic dynamical systems. Proper orthogonal decomposition and dynamic mode decomposition routines were used in the study. It was observed that despite the combustion significantly affected the time-averaged characteristics of the strongly swirling flow, the dynamics of both flows were associated with a global helical instability mode, which corresponded to a strong precession of the vortex core. In contrast, velocity fluctuations above the nozzle exit of the studied low-swirl flows were associated with local instability modes. In particular, this result indicates that the lowswirl flame should be more sensitive to external flow perturbations for combustion control

Propagating helical waves as a building block of round turbulent jets

Turbulent jets are known to support large-scale vortical wave packets traveling downstream. We show that a propagating helical wave represents a common form of the "optimal" eigenfunction tracking these structures from the near to the far field of a round jet issuing from a pipe. Two first mirror-symmetric modes containing around 5% of the total turbulent kinetic energy capture all significant large-scale events and accurately replicate the full shear-layer dynamics of the azimuthal wave number m=1. A family of the most energy-containing traveling waves represents low wave numbers and is described in terms of "empirical" dispersion laws.ChemE/Transport Phenomen