Abstract
The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.
Highlights
Bluff-body stabilized turbulent gaseous jet diffusion flames have received renewed attention in recent years due to its practical applications such as, gas burners of industrial furnaces, gas turbine combustion chamber, ramjets and flaring of petroleum industry.The practical importance of the bluff-body stabilization process has raised a large number of theoretical and experimental studies over the years to identify the physical mechanisms for the stability limits at different geometries of bluff-body acting as flame holders
These results exposed that the properly trained artificial neural network (ANN) model has consistently performed more accurate prediction closer to experimentally measured ones compared to response surface methodology (RSM) model in all aspects hinting that ANN model was quite successful for both simulation and predicted values
This is expressed in the very high values of R2 & F ratio and the extremely low value of error indicators for the ANN results compared to that of RSM ones. This is more pronounced by comparing the results of the studied case a for the values of R2 (0.98312, 0.9683, 0.98745 in case of ANN compared to 0.9231, 0.86814, 0.93575 in case of RSM) and of F ratio (5658.57, 2922.47, 7580.67 in case of ANN compared to 540.154, 231.747, 864.077 in case of RSM) for D30, D60 & DFC respectively
Summary
The practical importance of the bluff-body stabilization process has raised a large number of theoretical and experimental studies over the years to identify the physical mechanisms for the stability limits at different geometries of bluff-body acting as flame holders. Several pioneering works have proposed overall flow field classifications based on the observed flame structure as follows: The effects of bluff-body lip thickness on physical parameters like flame length, radiant fraction, gas temperature and NOx emissions in (LPG-H2) jet diffusion flame were investigated experimentally [1]. The flame length was reduced, flame temperature was increased and NOx emission level was enhanced with increasing lip thickness of the bluff-body. Mishra and Kiran [2] presented another experimental study to investigate the effect of coaxial air velocity and lip thickness of the bluff-body on the stability limits of LPG diffusion flames. The flame stability was found to be improved for larger lip-thickness bluff-body because of the presence of lower pressure in the wake behind the bluff-body
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