| description abstract | Cryogenic fluid management is highly crucial for NASAs space missions and one challenge identified by NASA is the tank fill process for pure cryogenic fluids. To date, no predictive tool can accurately estimate the pool boiling curve during this process. We aim to address this gap by first accurately predicting critical heat flux. We use both traditional machine learning and physics-assisted machine learning methods to predict critical heat flux on a large dataset of 1322 datapoints from 53 published studies with a wide range of operational conditions and surface properties. While the traditional machine learning approach used no prior knowledge in modeling, the physics-assisted machine learning approach assumes the correlation form of the Zuber instability model (Zuber, N., 1958, “On the stability of boiling heat transfer,” ASME Trans. ASME., 80(3), pp. 711–714) and predicts the multiplier for the hydrodynamic instability term to estimate the critical heat flux. Two groups of input features are compared including one with fluid-only properties and another with both fluid and solid surface properties. Various feature selection methods are employed to reduce the redundant features, and multiple machine learning models are used for modeling the dataset. Physics-assisted machine learning methods predict much better than traditional machine learning methods. The inclusion of solid properties further improves the predictions of critical heat flux across all models using physics-assisted method. Machine learning models are more stable when using physics-assisted methods compared to traditional methods. The best predictions are obtained using the extreme gradient boosting model based on the physics-assisted machine learning method resulting in a mean absolute percentage error of 8.88% which is far better than the semi-empirical correlations. | |
