This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

The heat transfer performances of ionic liquids [C4mpyrr][NTf2] and ionanofluids with Al2O3 nanoparticles under a laminar flow regime, and with constant heat flux on the tube wall is numerically modeled and analyzed for three values of initial/inlet temperature and for two Reynolds numbers. Heat transfer characteristics were considered by analyzing the temperature distribution along the upper wall, as well as by analyzing the Nusselt number and heat transfer coefficient. The results obtained numerically were validated using Shah’s equation for ionic liquid. Thermophysical properties were temperature-dependent, and obtained by curve-fitting the experimental values of the thermophysical properties. Furthermore, the same set of results was calculated for the ionic liquid and ionanofluids with constant thermophysical properties. It is concluded that the assumption that thermophysical properties are constant has a significant influence on the heat transfer performance parameters of both ionic liquid and ionanofluids, and therefore such assumptions should not be made in research.