Cavitation Model Calibration Using Machine Learning Assisted Workflow

Abstract

Conventional cavitation assessment methodology in industrial and scientific applications generally depends on cavitation models utilizing homogeneous mixture assumption. These models have been extensively assessed, modified and expanded to account for deficiencies of their predecessors. Unfortunately, none of the proposed models can be classified as the universal solution for all engineering applications, with usage mainly directed by experience or general availability of the models. In this study we propose a workflow through which the empirical constants governing the phase change of the Kunz mixture cavitation model can be calibrated for a given application or a series of problems, with machine learning as a tool for parameter estimation. The proposed approach was validated on a three-dimensional propeller test case with results in excellent agreement for the case in question. Results for thrust and torque were within 2% with cavity extents differing by up to 20%. This is a significant improvement when compared to previously proposed parameters. Despite the lack of generalization due to the limited nature of the dataset on which the model was trained, the proposed parameters entail acceptable results for similar cases as well. The overall methodology is applicable to other problems as well and should lead to more accurate cavitation predictions.

Impact of Cryogenics on Cavitation through an Orifice: A Review

“…Recently, Jin et al [33] designed a model to relate the values of the condensation and evaporation coefficients to the operating conditions, trying, in this way, to overcome the limitation of using empirical constants. Other examples of optimization of these empirical parameters are the surrogate-based sequential approximate optimization (SAO) method considered by Zhou et al [34] or the machine learning method employed by Sikirica et al [35]. Both were applied within the Kunz mixture cavitation model and provided better performances in cavitation prediction and simulation than the use of constant coefficients.…”

Numerical Modeling of the Behavior of Bubble Clusters in Cavitation Processes

“…Another approach to cavitation research is machine learning. However, the application of such a method to cavitation modeling has been mainly limited to the optimization of previous approaches to modified cavitation [50,51].…”