In this study, an analytical technique for the rotor geometry optimization based on lumped magnetic parametric approach is used to design a two-pole, three-phase, 7.5-kW line-start permanent magnet (LSPM) synchronous motor. The permanent magnet shape substantially affects the air-gap flux density distribution, back electromotive force (EMF) as well as the copper loss, which have a great impact on the performance characteristics of the permanent magnet synchronous motors. Principal advantages involve in adjusting the rotor shape are to achieve the effective air-gap flux density and optimize the fundamental component of the back EMF with low harmonic content for minimum ripple torque. Therefore, to enhance the efficiency (η) and power factor, an optimized slot shape considering various design parameters is selected for the permanent magnet of the rotor in the prototype LSPM machine. A linear saturated lumped magnetic parametric model is developed to exhibit magnetic characteristics, and analytical equations are acquired under the open-circuit condition without considering the slotting effect for design simplicity. The influence of design variables on the air-gap flux density distribution and the flux leakage is investigated precisely using an analytical circuit model. A parametric study of the prototype model demonstrates that the steady-state performance of the LSPM motor are significantly influenced by the design variables. The inductance saliency ratio and electromagnetic torque components are carefully analyzed in terms of their effects on the load characteristics of the LSPM motor in order to determine the optimal shape of the PM slots and the magnetic flux barriers. The validity of the proposed method has been checked by evaluating the numerical solution of the analytical model using a two-dimensional finite element method.INDEX TERMS Analytical method, air-gap flux density, FEM, lumped parametric model, parametric study, rotor design.

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The present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work. All the cases studied were Square Concrete Slabs (SCS). The critical parameters used as input for the study were column dimension, cs, depth of the slab, ds, shear span ratio, a v s / d , longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, the compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus. Seven ANN models were trained using different combinations of input parameters and different points of hidden neurons with different activation functions. The results exhibited that SCS-4 was the most optimized ANN model, having the maximum value of R (89%) with the least values of MSE (0.62%) and MAE (6.2%). It did not only reduce the error but also predicted accurate results with the least quantity of input parameters. The predictions obtained from the studied models (i.e., CDCs, CFP, and ANN) exhibited that results obtained using the ANNs model correlated well with the experimental data. Furthermore, the FEM results for the selected cases show the closer result to the ANN predictions.

This paper used a lumped magnetic parametric approach-based analytical technique to design the 7.5 kW, three-phase line start permanent magnet (LSPM) motor. In order to enhance the efficiency and power factor (PF) of the prototype LSPM machine, an optimized slot shape of rotor permanent magnet (PM) was selected. The lumped magnetic circuit model is developed to present the magnetic characteristics, and analytical expressions are derived under the open circuit condition. The impact of the design variables on the analytical model air gap flux density distribution and magnetic flux leakage is studied for the LSPM. The design variables have a significant influence on the steady-state performance characteristics of the LSPM motor. Therefore, to verify the output results of the purposed model, the two-dimensional finite element analysis (FEA) was evaluated for the numerical solution of the analytical model.