Abstract
This study was devoted to introducing a new method for a priori prediction of aspiration pressure buildup in closed coupled atomization (CCA) nozzles. There have been considerable controversies about increasing or decreasing the aspiration pressure for a reliable operation of CCA nozzles, mainly because of the complex nature of CCA process. Here for the first time, we applied an artificial neural network (ANN) based machine learning algorithm for the prediction of aspiration pressure in close-coupled HPGA nozzles. An analytical model equation was obtained based on the largest experimental dataset from the literature and proved to be useful for prediction of non-dimensionalized aspiration pressure with R2 of 0.98. But, its prediction accuracy of absolute aspiration pressures was degraded with a decrease of R2 score to 0.73 and an average prediction error of 17 %, mainly due to the limitation of literature data. Based on parametric study and a sensitivity test, protrusion length of CCA nozzles and Re number were found to be relatively significant as compared to the apex angles. Finally, we provided a comprehensive contour map to facilitate the conceptual design and operation of CCA nozzles to minimize the aspiration pressure.
Published Version
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