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This paper aims to propose a numerical methodology to reduce the number of computations required to optimally design the rotors of synchronous reluctance machines (SynRMs) with multiple barriers.

Two objectives, average torque and torque ripple, have been simulated for thousands of SynRM models using 2D finite element analysis. Different rotor topologies (i.e. number of flux barriers) were statistically analyzed to find their respective design correlation for high average torque solutions. From this information, optimal geometrical constraints were then found to restrict the design space of multiple-barrier rotors.

Statistical analysis of two considered SynRM case studies demonstrated a design similarity between the different number of flux barriers. Upon setting the optimal geometrical constraints, it was observed that the design space of multiple-barrier rotors reduced by more than 56 per cent for both models.

Using the proposed methodology, optimal geometrical constraints of a multiple-barrier SynRM rotor can be found to restrict its corresponding design space. This approach can handle the curse of dimensionality when the number of geometric parameters increases. Also, it can potentially reduce the number of initial samples required prior to a multi-objective optimization.

Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Two different devices, a core-type single-phase transformer and a motor-drive system, are used to show the usefulness and generalizability of the proposed approach. Using a finite element solver, a large database of design possibilities is created by varying design parameters, i.e. the geometrical and control parameters of the systems. Design rules are then extracted by performing a statistical analysis and exploring optimal and sub-optimal designs considering various targets such as efficiency, torque ripple and power factor.

It is demonstrated that the correlation of the design parameters influences the way the data-driven approach must be made. Also, guidelines for defining new design constraints, which can lead to a more efficient optimization routine, are introduced for both case studies.

Using the proposed approach, new design guidelines, which are generally not obtainable by the classical design methods, are introduced. Also, the proposed approach can potentially deal with different parameter–objective correlations, as well as different number of connected systems. This approach is applicable regardless of the device type.

Acoustic noise is a crucial performance index in the design of electrical machines. Due to the challenges associated with modelling a complete motor, the stator is often used to estimate the sound power in the prototyping stage. While this approach greatly reduces lengthy simulations, the actual sound power of the motor may not be known. But, from the acoustic noise standpoint, not much is known about the correlation between the stator and complete motor. This paper, therefore, aims to use the sound pressure levels of the stator and the full motor to investigate the existence of correlations in the interior permanent magnet synchronous motor.

A multiphysics simulation framework is proposed to evaluate the sound pressure levels of multiple motor geometries in a given design space. Then, a statistical analysis is performed on the calculated sound pressure levels of each geometry over a selected speed range to compare the correlation strength between the stator and the full model.

It was established that the stator and the complete motor model are moderately correlated. As such, a reliance on the stator sound power for design and optimization routines could yield inaccurate results.

The main contribution involves the use of statistical tools to study the relationship between sound pressure levels associated with the stator geometry and the complete electric motor by increasing the motor sample size to capture subtle acoustic correlation trends in the design space of the interior permanent magnet synchronous motor.

The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device.

An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors.

The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device.

The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.

The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.

Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.

Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

The purpose of this study is to propose a control scheme based on state estimation algorithm to improve zero or low-voltage ride-through capability of permanent magnet synchronous generator (PMSG) wind turbine.

Based on the updated grid codes, during and after faults, it is necessary to ensure wind energy generation in the network. PMSG is a type of wind energy technology that is growing rapidly in the network. The control scheme based on extended Kalman filter (EKF) is proposed to improve the low voltage ride-through (LVRT) capability of the PMSG. In the control scheme, because the state estimation algorithm is applied, the requirement of DC link voltage measurement device and generator speed sensor is removed. Furthermore, by applying this technique, the extent of possible noise on measurement tools is reduced.

In the proposed control scheme, zero or low-voltage ride-through capability of PMSG is enhanced. Furthermore, the requirement of DC link voltage measurement device and generator speed sensor is removed and the amount of possible noise on the measurement tools is minimized. To evaluate the ability of the proposed method, four different cases, including short and long duration short circuit fault close to PMSG in the presence and absence of measurement noise are studied. The results confirm the superiority of the proposed method.

This study introduces EKF to enhance LVRT capability of a PMSG wind turbine.

When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate resources and personnel to provide patients with care. The purpose of this study is to create a model that is more practical in the real world. So the concept of “predicting the resource and personnel shortages” has been used in this research. Their model helps to predict the resource and personnel shortages during a mass casualty event. In this paper, to deal with the shortages, some temporary emergency operation centers near the hospitals have been created, and extra patients have been allocated to the operation center nearest to the hospitals with the purpose of improving the performance of the hospitals, reducing congestion in the hospitals and considering the welfare of the applicants.

The authors research will focus on where to locate health-care facilities and how to allocate the patients to multiple hospitals to take into view that in some cases of emergency situations, the patients may exceed the resource and personnel capacity of hospitals to provide conventional standards of care.

In view of the fact that the problem is high degree of complexity, two multi-objective meta-heuristic algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), were proposed to solve the model where their performances were compared in terms of four multi-objective metrics including maximum spread index (MSI), spacing (S), number of Pareto solution (NPS) and CPU run-time values. For comparison purpose, paired t-test was used. The results of 15 numerical examples showed that there is no significant difference based on MSI, S and NPS metrics, and NRGA significantly works better than NSGA-II in terms of CPU time, and the technique for the order of preference by similarity to ideal solution results showed that NRGA is a better procedure than NSGA-II.

The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals.

Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research.

This paper strategy is used in all of the health-care centers, such as hospitals, clinics and emergency centers when dealing with disasters and encountering with the heavy and considerable demands of injured patients and inadequate resources and personnel to provide patients with care.

This paper attempts to shed light onto the formulation and the solution of a three-objective optimization model. The first part of the objective function attempts to maximize the covered population of injured patients, the second objective minimizes the distance between hospitals and temporary emergency operation centers and the third objective minimizes the distance between the warehouses and temporary centers.

Turbomachinery, including pumps, are mainly designed to extract/produce energy from/to the flow. A major challenge in the numerical simulation of turbomachinery is the inlet flow rate, which is routinely treated as a known boundary condition for simulation purposes but is properly a dependent output of the solution. As a consequence, the results from numerical simulations may be erroneous due to the incorrect specification of the discharge flow rate. Moreover, the transient behavior of the pumps in their initial states of startup and final states of shutoff phases has not been studied numerically. This paper aims to develop a coupled procedure for calculating the transient inlet flow rate as a part of the solution via application of the control volume method for linear momentum. Large eddy simulation of a four-blade axial hydraulic pump is carried out to calculate the forces at every time step. The sharp interface immersed boundary method is used to resolve the flow around the complex geometry of the propeller, stator and the pipe casing. The effect of the spurious pressure fluctuations, inherent in the sharp interface immersed boundary method, is damped by local time-averaging of the forces. The developed code is validated by comparing the steady-state volumetric flow rate with the experimental data provided by the pump manufacturer. The instantaneous and time-averaged flow fields are also studied to reveal the flow pattern and turbulence characteristics in the pump flow field.

The authors use control volume analysis for linear momentum to simulate the discharge rate as part of the solution in a large eddy simulation of an axial hydraulic pump. The linear momentum balance equation is used to update the inlet flow rate. The sharp interface immersed boundary method with dynamic Smagorinsky sub-grid stress model and a proper wall model is used.

The steady-state volumetric flow rate has been computed and validated by comparing to the flow rate specified by the manufacturer at the simulation conditions, which shows a promising result. The instantaneous and time averaged flow fields are also studied to reveal the flow pattern and turbulence characteristics in the pump flow field.

An approach is proposed for computing the volumetric flow rate as a coupled part of the flow solution, enabling the simulation of turbomachinery at all phases, including the startup/shutdown phase. To the best of the authors’ knowledge, this is the first large eddy simulation of a hydraulic pump to calculate the transient inlet flow rate as a part of the solution rather than specifying it as a fixed boundary condition. The method serves as a numerical framework for simulating problems incorporating complex shapes with moving/stationary parts at all regimes including the transient start-up and shut-down phases.

The purpose of this paper is to investigate the relationship between cash holdings, investment opportunities and financial constraint with audit fees in Iran.

In order to collect data, all manufacturing companies listed on the Tehran Stock Exchange are used to test the hypotheses during 2008–2015. Panel data and combined data regression model were used for data analysis. Tests were performed using R statistical software.

The results obtained from the statistical analysis of research hypotheses indicated that there is a significant relationship between cash holdings and audit fees. Furthermore, the relationship between cash holdings, financial constraints and audit fees was significant. In addition, there was no significant relationship between cash holdings, investment opportunities and audit fees.

The current study employed a unique topic in terms of a developing country, and the results may give strength to other developing nations.