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 purpose of the present study is to simulate erosion on the aluminium plate with a cylindrical hole caused by solid particles after passing through 90° elbow, using the Computational Fluid Dynamics (CFD), the Discrete Phase Model (DPM), and erosion equations. Discrete trajectories of solid particles are calculated using the Lagrangian approach, while the simulation of the fluid was obtained by solving the fluid motion equation using the Eulerian approach. Supplementary sub-models are incorporated into the software to enhance the accuracy of particle trajectory calculations within the simulated geometry. These sub-models include collisions of solid particles with walls (stochastic model) and erosion model. The numerical simulation results obtained in this paper were compared with the existing experimental results from the group of authors, demonstrating a good match. The paper provides the main characteristics of the mathematical model, along with the interpretation of results and a discussion, with the key findings highlighted in the conclusion. The findings indicate that erosion process is significantly influenced by both the particle impact velocity and impact angle, which are key parameters in many erosion equa - tions. Furthermore, it is observed that the velocity of the particles is consistently lower than the mean velocity of the air. Additionally, the angle at which the particles impact the aluminium plate is not always exactly 90° due to multiple collisions with the wall, signifying that the particles do not move exclusively vertically.

This paper is effectively a scenario analysis of the energy system of Bosnia and Herzegovina (BiH) from the perspective of the possible future reduction of greenhouse gas (GHG) emissions in the power generation sector, with the aim to become climate neutral by 2050, in compliance with the Green Agenda for the Western Balkan. According to the data from 2016, the share of power generation in the total GHG emissions in BiH was approximately 50%. By using the LEAP (Long-range Energy Alternatives Planning) energy model, two scenarios—the “gradual transition scenario” and the “climate neutral” scenario—have been analyzed for the period 2018–2050, and each scenario included decarbonization measures such as the extensive use of Renewable Energy Sources (RES). Unlike the climate neutral scenario, the gradual transition scenario includes the replacement of certain parts of the old, currently-in-operation Coal-fired Power Plants (CFPPs) with the new CFPP, which is more efficient. In the climate-neutral scenario, that part of the existing CFPPs is replaced by a mix of RESs. The results from the first scenario suggest that the share of CFPPs in electricity generation has gradually decreased from 69.3% to 16.3% in 2050, and CO2 emissions from the power generation sector in 2050 will be 2.2 million tons—roughly 83.5% less than in 2014. According to the second scenario, the emphasis is strongly on the growth and promotion of RESs, which have significantly taken over the roles of major producers of electricity, encouraging the low-carbon development of BiH. Analysis results show that, in 2050, there will be no CO2 emissions from power generation. It can be concluded that specifically designed energy models for the optimization of capacities and CO2 emissions through convergence towards RESs could be an optimistic and promising option for BiH to become climate neutral while meeting increasing energy demands. The results show the required RES capacities needed for achieving climate-neutral power generation by 2050, with the current rate level of power generation. Based on the results, RES investment needs can be estimated. Overall, the results of the scenarios can be used for the strategic planning of the power generation sector in BiH until 2050.

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.