No abstract

A supersonic ejector with desirable performance characteristics reduces the energy consumption rate of an ejector refrigeration system and increases its coefficient of performance (COP). In this paper, the effects of using different primary nozzles on the performance of a supersonic ejector of an ejector refrigeration system have been numerically studied, while the working fluid is steam. To this end, conical, Rao and parallel-flow primary nozzles with identical converging portions and equal exit area to throat area ratios have been tested. The diverging portion curves for the parallel-flow and Rao nozzles were derived using the method of characteristics. Using the Rao nozzle, the critical entrainment ratio and the critical back pressure were increased compared to the conical nozzle by 6.3% and 2.08%, respectively. It was also found that the physics of the internal flow of the ejector was changed by changing the diverging curve of the primary nozzle.

Flux-difference splitting procedure is developed for the hyperbolic part of the governing equations of turbulent reacting flow. As a primer computation of turbulent flame velocity in methanol mixtures for various turbulent kinetic energy is presented.

This study proposes a universal machine learning-based model to predict the adiabatic and condensing frictional pressure drop. For developing the proposed model, 11,411 data points of adiabatic and condensing flow inside micro, mini and macro channels are collected from 80 sources. The database consists of 24 working fluids, hydraulic diameters from 0.07 to 18 mm, mass velocities from 6.3 to 2000 Kg/m2s, and reduced pressures from 0.001 to 0.95. Using this database, four machine learning regression models, including “artificial neural network”, “support vector regression”, “gradient boosted regression”, and “random forest regression”, are developed and compared with each other. A wide range of dimensionless parameters as features, “two-phase friction factor” and “Chisholm parameter” are each considered separately as targets. Using search methods, the optimal values of important hyperparameters in each model are determined. The results showed that the “gradient boosted regression” model performs better than other models and predicts the frictional pressure drop with a mean absolute relative deviation of 3.24%. Examining the effectiveness of the new model showed that it predicts data with uniform accuracy over a vast range of variations of each flow parameter.