Water flow boiling heat transfer in vertical minichannel

A novel scheme for simulating the effect of microstructure surface roughness on the heat transfer characteristics of subcooled flow boiling

https://iopscience.iop.org/article/10.1088/1402-4896/ac1f5d/pdf

Two-phase pressure drop analysis of swirl tube in one-side high heat load condition for plasma facing component application

Numerous studies have proposed to correlate experimental results, however there are still significant errors in those predictions. In this study, an artificial neural network (ANN) is considered for a two-phase flow pressure drop in microchannels incorporating four neural network structures: multilayer perceptron (MLP), radial basis function (RBF), general regression (GR), and cascade feedforward (CF). The pressure drop predication by ANN uses six inputs (hydraulic diameter of channel, critical temperature of fluid, critical pressure of fluid, acentric factor of fluid, mass flux, and quality of vapor). According to the experimental data, for each network an optimal number of neurons in the hidden layer is considered in the range 10–11. A committee neural network (CNN) is fabricated through the genetic algorithm to improve the accuracy of the predictions. Ultimately, the genetic algorithm designates a weight to each ANN model, which represents the relative contribution of each ANN in the pressure drop predicting process for a two-phase flow within a microchannel. The assessment based on the statistical indexes reveals that the results are not similar for all models; the absolute average relative deviation percent for MLP, CF, GR, and CNN were obtained to be equal to 10.89, 10.65, 7.63, and 5.79, respectively. The CNN approach is demonstrated to be superior to many ANN techniques, even with simple linearity in the model.

https://www.mdpi.com/2076-3417/10/15/5384/pdf

Using Committee Neural Network for Prediction of Pressure Drop in Two-Phase Microchannels

In this study, the two-phase pressure drop in a one-sided high-heat-loaded screw tube was analyzed under subcooled flow boiling conditions. When the loaded heat flux is gradually increased under one-sided heating conditions, although the pressure drop changes slightly in the single phase, it starts to increase when loading above the onset of significant void (OSV) heat flux. The effect of system parameters on the two-phase pressure drop in relation to the change of heat transfer mechanism or fluid properties was analyzed according to each variable change. In addition, this study evaluated the prediction performance of existing two-phase pressure drop multiplier correlations developed under subcooled flow boiling conditions. Because most existing correlations are based on a smooth channel with a small diameter, they tend to overpredict the experimental values. The correlation proposed by Ramesh showed the lowest average error rate (31.94%); however, the results were underpredicted for high-heat-flux conditions. To obtain more accurate results, we developed a Python code using an artificial intelligence regression method to proceed with the optimization process based on the experimental values. Using this code, we developed a novel two-phase pressure drop multiplier correlation for a one-side-heated screw tube that can be used in fusion divertors under high-heat-load conditions.

Bhaarath Ramesh

Papers

Subcooled flow boiling of water in a copper microchannel: Experimental investigation and assessment of predictive methods

On the prediction of pressure drop in subcooled flow boiling of water