In this study, a particle swarm optimisation (PSO) algorithm coupled with finite-element method (FEM) is implemented to improve the performance of a cap-and-pin insulator by reducing the value of maximum electric fiel...
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In this study, a particle swarm optimisation (PSO) algorithm coupled with finite-element method (FEM) is implemented to improve the performance of a cap-and-pin insulator by reducing the value of maximum electric field strength. Two goals are set in this work: (i) optimisation of the tangential electric field distribution and magnitude (which is taken as the objectif function to be minimised) to reduce the risk of flashover due to surface pollution, and (ii) reduction of the creepage distance (by adopting adequate constraints) leading to a reduction of the surface area and consequently a decrease in the insulator weight. The investigation is divided into two parts;first, one-unit insulator is optimised with which a chain is constructed, and its performance compared with the reference insulator string. The second part considers a whole chain of four-unit insulator whose performance is optimised and compared to that of a reference insulator string. The main finding of this work indicates that, if an insulator string is constructed using an optimised cap-and-pin unit, the performance of the so-formed string is also optimised. The optimisation of a complete string leads to practically the same performance as that of a string obtained by assembling optimised insulator units.
The present paper aims to study the effect of cooling air inlet methods on gas turbine compressors on increasing their efficiency. After modeling gas turbine cycles with absorption and compression systems in the EES s...
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The present paper aims to study the effect of cooling air inlet methods on gas turbine compressors on increasing their efficiency. After modeling gas turbine cycles with absorption and compression systems in the EES software, these cycles' performance is investigated for all equipment of the cycle from thermodynamic, exergy, and exergoeconomic aspects. In the absorption system, the conventional solution of lithium bromide-water is used as a two-component fluid, and in the compression cycle, the R134a operating fluid is used. According to the results, with the rise in the system's inlet air temperature, the total output work of the gas turbine decreases. Based on the exergoeconomic analysis, the exergy destruction cost dominates the initial cost, resulting in the exergoeconomic factor's decline. Relationship predicted by Group Method of Data Handling (GMDH) to reduce the computation time of optimization The studied systems are then subjected to two-objective optimization by the Particle swarmalgorithm using MATLAB software. The objective functions are related to the exergy efficiency and total cost rate. The results reveal contradictory behavior in these two objective functions so that with the increase in the exergy efficiency, the total cost rate increases.
In the present investigation, the free convection energy transport was studied in a C-shaped tilted chamber with the inclination angle alpha that was filled with the MWCNT (MultiWall Carbon Nanotubes)-Fe3O4-H2O hybrid...
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In the present investigation, the free convection energy transport was studied in a C-shaped tilted chamber with the inclination angle alpha that was filled with the MWCNT (MultiWall Carbon Nanotubes)-Fe3O4-H2O hybrid nanofluid and it is affected by the magnetic field and thermal flux. The control equations were numerically resolved by the finite element method (FEM). Then, using the artificial neural network (ANN) combined with the particlesswarm optimization algorithm (PSO), the Nusselt number was predicted, followed by investigating the effect of parameters including the Rayleigh number (Ra), the Hartmann number (Ha), the nanoparticles concentration (phi), the inclination angle of the chamber (alpha), and the aspect ratio (AR) on the heat transfer rate. The results showed the high accuracy of the ANN optimized by the PSO algorithm in the prediction of the Nusselt number such that the mean squared error in the ANN model is 0.35, while in the ANN model, it was optimized using the PSO algorithm (ANN-PSO) is 0.22, suggesting the higher accuracy of the latter. It was also found that, among the studied parameters with an effect on the heat transfer rate, the Rayleigh number and aspect ratio have the greatest impact on the thermal transmission intensification. The obtained data also showed that a growth of the Hartmann number illustrates a reduction of the Nusselt number for high Rayleigh numbers and the heat transfer rate is almost constant for low Rayleigh number values.
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