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This paper presents an experimental methodology to measure the height of the flame using convolution image processing and statistical analysis. The experimental setup employs a burner with four circularly arranged nozzles. Six different volumetric fuel flows were employed, and flame images were captured from three different visualization planes utilizing a three high-definition camera array, a thermal imaging camera and an image-processing algorithm. The flame height was indirectly measured using pixel quantification and conversion through a reference length. Although the fuel flow was the most significant factor, the visualization plane and the image source were also found to be particularly relevant, since certain flame features were only perceivable depending on the approach. The measurements were compared to different existing theoretical correlations, yielding an overall adjustment ranging from 3.25 to 3.97cm. The present methodology yields an overall statistical tolerance of 1.27 cm and an expanded uncertainty of 0.599 cm. Furthermore, the thermal imaging has revealed a consistent difference in the overall luminous observable flame of 2.54 cm. For this particular burner configuration, correlations were derived by statistical modelling, which explain the flame height fluctuations with an average setting of 97.23%.
This paper presents a flame-height correlation for laminar to transition-to-turbulent regime diffusion flames. Flame-height measurements are obtained by means of numerical and experimental studies in which three high definition cameras were employed
The mixing process of multiple jets of liquefied petroleum gas and air in a diffusion flame is numerically analysed. The case study considers a four-port array burner where the fuel is injected by four peripheral nozzles and mixed with the surroundin
In this visualisation, the transition from laminar to turbulent flow is characterised by the intermittent ejection of wall fluid into the outer stream. The normalised thickness of the viscous flow layer reaches an asymptotic value but the physical th
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of a convolutional neural network and multi-layer perceptrons is considered in this study
This study concerns wavepackets in laminar turbulent transition in a Blasius boundary layer. While initial amplitude and frequency have well-recognized roles in the transition process, the current study on the combined effects of amplitude, frequency