ASSESSMENT OF CAVITATION MODELS IN THE PREDICTION OF CAVITATION IN NOZZLE FLOW

Cavitation inside fuel injector nozzles has been linked not only to erosion of the solid surface, but also to improved spray atomization. To quantify the effects of the resulting occurrences, the prediction of cavitation through computational modeling is vital. Homogeneous mixture methods (HMM) make use of a variety of cavitation sub-models such as those developed by Kunz, Merkle, and Schnerr-Sauer, to describe the phase change from liquid to vapor and vice-versa in the fluid system. The aforementioned cavitation models all have several free-tuning parameters which have been shown to affect the resulting prediction for vapor volume fraction. The goal of the current work is to provide an assessment of the Kunz and Schnerr-Sauer cavitation models. Validation data have been obtained via experiments which employ both acoustic techniques (passive cavitation detection, or PCD) and optical techniques (optical cavitation detection, or OCD). The experiments provide quantitative information on cavitation inception and qualitative information as to overall vapor fraction as a function of flow rate, and nozzle geometry. It is shown that inception is fairly well captured but the amount of vapor predicted is far too low. A sensitivity analysis on the tuning parameters in the cavitation models leads to some explainable trends, however, several parameter sweeps results in outlier predictions. Recommendations for their usability and suggestions for improvement are presented.