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.

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A recent study addressed the modelling challenges of Alpha* gas condensate field. Alpha gas condensate field has a gas in-place of about 1 TCF, and both condensate and black oil production in addition. The field has been producing from two reservoirs S-I and D-I, for the past 26 years. Alpha field is sub-divided into two segments called the Central Area and the Northern Area which are separated by a fault as shown in Figure 2. * Not its real name.

Purpose The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs. Design/methodology/approach Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints. Findings All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error. Research limitations/implications The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm. Practical implications The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases. Originality/value Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.