Development and assessment of performance of artificial neural networks for prediction of frictional pressure gradients during two-phase flow

This paper presents a discussion on several possibilities to predict the frictional pressure gradient during two-phase flow, with both the application of artificial intelligence and the implementation of conventional correlations and predictive methods. To this purpose, a huge database of approximately 8000 data points has been collected from 49 sources available in scientific literature, including 23 working fluids and the following ranges of parameters: mass fluxes from 32.7 to 2000 kg/m2s, saturation temperatures from -190°C to +120°C (reduced pressures from 0.021 to 0.780), tube diameters from 0.5 to 14.0 mm. This consolidated database has been used to train several artificial neural networks (ANNs), by using only two hidden layers (shallow neural networks) and evaluating the effect of: training and testing datasets choice (either test data included or outside the training domain), the number of neurons for each hidden layer (from 1 to 50), the type of output (either dimensional or non-dimensional), the type and number (from 1 to 22) of input parameters. The best results (MAPE of 16.8% and 88% of data within ±30%) have been obtained by using the liquid-only two-phase multiplier as non-dimensional output and 12 mixed input parameters. Compared to the statistics of well-established literature correlations for frictional pressure drop (best MAPE of 22% and 73% of data points predicted within a ±30% error range, provided by Mauro et al. mechanistic method), the ANN demonstrates therefore a higher general accuracy. However, the use of Artificial Neural Networks does not guarantee a physical trend, which is instead preserved with conventional prediction methods

Estimation of flow boiling heat transfer coefficient in enhanced tubes. Benchmark correlations and ANN approach

This paper addresses the critical gap in predicting the heat transfer coefficient during flow boiling in enhanced tubes, where the use of conventional correlations and predictive methods developed for smooth surfaces do not usually provide satisfactory results. For such purpose, a comprehensive database was collected from existing literature, including a wide range of operating conditions and enhanced tube geometries from several independent sources. The dataset includes mass flow rates spanning from 50 to 1000 kg/m2s, vapor qualities from the onset of boiling (x=0.0) to the dry-out occurrence and beyond (x=0.99), reduced pressures from 0.05 to 0.80, and tube diameters (measured up to the fin tip) from 0.7 to 11.9 mm, for a total amount of approximately 3000 data points. Existing flow boiling heat transfer coefficient predictive methods for enhanced tubes were implemented and tested with the present dataset, proving a limited accuracy for most of them mainly in case of testing beyond the specific parameter ranges they were developed for. Extrapolation frequently resulted in statistically poor or even non-physical outcomes. Several artificial neural network models were then developed, according to sensitivity analysis approach to look for potential input parameters and network structures. Specifically, two approaches were employed: a standard neural network model and a correlated informed neural network (CINN), integrating physical correlations into the network's architecture, thus informing the model with physical principles that govern the heat transfer process. Despite a lower overall accuracy, the correlated informed neural network demonstrated superior reliability than standard one, resulting in an instrument to improve the accuracy of existing correlations

Two-phase flow characteristics in singularities

Paper presented at the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16-18 July, 2012.This paper aims at presenting the latest scientific progress on two-phase flow in singularities through academic research at INSA Lyon as well as proposing some future possible important issues to be investigated. Flow regimes of third- and fourth-generation refrigerants in horizontal and vertical return bends as well as in a horizontal sudden contraction were experimentally investigated. The dynamical behavior of vapor bubbles or slugs in vertical downward flow return bend was reported. A simplified analysis of the forces acting on the bubble was proposed to better understand the vapour trajectory. Furthermore, void fraction was measured along the sudden contraction using an image analysis technique, which gives very original results. Such experimental studies also brought to the fore the upstream and downstream flow disturbances caused by such singularities as contractions and return bends and their impact on the hydrodynamic performance (e.g. pressure drop) of refrigerants. Especially, these disturbances can be analysed in terms of perturbation lengths up- and downstream of the singularities. Lastly, large pressure drop databases for R-410A, R-134a and HFO-1234yf were obtained. Experimental values of pressure drops in singularities were compared against different prediction methods from the literature without any satisfactory results. Finally, these databases were used to develop new twophase pressure drop prediction methods for such singularities as return bends and sudden contractions.dc201