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This paper presents for the first time the analytical solution of the constrained minimization for the on‐line estimation of the electrical parameters of an induction motor. The method is fully described mathematically and its goodness is verified experimentally on a suitably set up test bench. This methodology permits the almost correct computation of all the so called K‐parameters, which is not always the case in current literature, thus resulting in the correct estimation of the electrical parameters.

The purpose of this paper is to present an improved approach to reactive power planning in electric power systems (EPS). It is based on minimization of a transmission network's active power losses. Several operating conditions have to be fulfilled to ensure stable operation of an EPS with minimal power losses. Some new limitations such as voltage instability detection and generator capability curve limit have been added to the existing method in order to improve the reliability of reactive power planning. The proposed method was tested on a model of the Slovenian power system. The results show the achievement of significant reduction in active power losses, while maintaining adequate EPS security.

Optimal voltage profile has to be found in order to determine minimal possible active power losses of EPS. The objective function, used to find the optimal voltage profile, has integer and floating point variables and is non‐differentiable with several local minima. Additionally, to ensure secure operation of EPS, several equality and inequality boundaries and limitations have to be applied. Differential evolution (DE) was used to solve the optimization problem.

Corresponding reactive power planning can significantly reduce active power losses in EPS. However, such planning can affect the security of EPS, therefore, several additional constrains have to be considered. The presented constrains considerably improve the operational security of EPS.

DE was used to solve the minimization problem. Although this method has proven to be fast and reliable, it is theoretically possible that the obtained solution is not global minimum.

Novel approach to voltage security constrained reactive power planning with additional nonlinear constrains, such as generator capability curves and voltage instability detection.

The collapse of a structure resulting from the instability of steel frames due to fire is the worst failure mode to consider in fire-structural engineering, and should be avoided. The purpose of this paper is to propose a new method for estimating the minimum possible duration of a fire event that could result in the instability of an unbraced steel frame.

The proposed method is in the form of a constrained minimization problem that determines the worst case fire scenario that can cause instability of a structure, and is solved using nonlinear constrained mathematical programming algorithms. The formulation is demonstrated via a numerical example.

For frames subjected to fire events modelled with monotonically increasing fire curves, the worst case fire causing instability of a frame is always one where all of the compartments catch fire at the same time. For frames subjected to fire events where fire curves decay, the minimization problem must be solved rigorously. The results are significantly affected by the fire curves and amount of insulation applied to each member.

The proposed method is an extension of a method previously established by Xu et al. (2018) to assess the stability of unbraced steel frames subjected to elevated member temperatures. The previous method does not consider fire duration and heat transfer mechanics, which are included in the proposed method. The proposed method is potentially useful for designers in conducting fire scenario analysis in the performance-based design of structures.

The purpose of this analysis is to explain why labor shortages may have appeared during this pandemic. Interestingly, in this COVID-19 pandemic period, the labor supply shortage could very well become more easily explained than under the traditional portrayal of consumer economic behavior. The matter seemingly lends itself to provocative empirical inquiry.

From this model, it can be shown that the consumer’s labor supply curve is negatively sloped and, indeed, could even assume the form of a rectangular hyperbola. Applying this model in the labor market could explain the labor shortage in the USA during the COVID-19 pandemic.

Arguably, rational consumer behavior can take the form, under a variety of circumstances (including cultural), for consumers/households that have achieved a “comfortable” standing of living/utility level, involve the minimization of work effort to achieve that utility level. In other words, constrained utility maximization is not the only rational form of consumer economic behavior. When the former behavior prevails over the latter, there are myriad implications. These do include an inverse relationship between work effort and wage rate, i.e. a negatively sloped labor supply curve.

This paper departs from the conventional treatment of deriving the supply curve of labor based on constrained utility maximization. Instead, it acknowledges that consumers may have a target standard of living and seek to minimize the cost of achieving that given living standard.

The use of three non‐linear mathematical programming techniques for the optimization of structural design problems is discussed. The methods — sequential linear programming, the feasible direction method and the sequential unconstrained minimization technique — are applied to a portal frame problem to enable a study of their convergence efficiency to be studied. These methods are used for both the sizing of the structural members and determining the optimum roof pitch. The sequential linear programming method is shown to be particularly efficient for application to structural design problems. Some comments on the development of computer software for structural optimization are also given.

We show that a social planner who seeks to allocate a given sum in order to reduce efficiently the social stress of a population, as measured by the aggregate relative deprivation of the population, pursues a disbursement procedure that is identical to the procedure adhered to by a Rawlsian social planner who seeks to allocate the same sum in order to maximize the Rawlsian maximin-based social welfare function. Thus, the constrained minimization of aggregate relative deprivation constitutes an economics-based rationale for the philosophy-based constrained maximization of the Rawlsian social welfare function.

This paper aims to present a new iterative procedure for the 3D representation of focusing magnetic fields in TWTs generated by PPMs, by using equivalent sources and optimisation algorithms.

In the integrated optimisation strategy general models for magnetic sources are employed and local and global inverse problems are iteratively solved for the minimization of the representation error.

The results obtained show that the target accuracy is reached with a low computational effort, employing a minimum number of equivalent sources.

The procedure is robust and converges for all the examined magnetic field configurations.

Different from other approaches, the procedure presented here can be directly applied to a variety of different models for magnetic sources.

The purpose of this paper is to present an approach for structural weight minimization under von Mises stress constraints and self-weight loading based on the topological derivative method. Although self-weight loading topology has been the subject of intense research, mainly compliance minimization has been addressed.

The resulting minimization problem is solved with the help of the topological derivative method, which allows the development of efficient and robust topology optimization algorithms. Then, the derived result is used together with a level-set domain representation method to devise a topology design algorithm.

Numerical examples are presented, showing the effectiveness of the proposed approach in solving a structural topology optimization problem under self-weight loading and stress constraint. When the self-weight loading is dominant, the presence of the regularizing term in the formulation is crucial for the design process.

The novelty of this research work lies in the use of a regularized formulation to deal with the presence of the self-weight loading combined with a penalization function to treat the von Mises stress constraint.

The real challenge in the solution of contact problems is the lack of an optimal adaptive scheme. As the contact zone is a priori unknown, successive refinement and iterative method are necessary to obtain a high-accuracy solution. The purpose of this paper is to provide an optimal adaptive scheme based on second-generation finite element wavelets for the solution of non-linear variational inequality of the contact problem.

To generate an elementary multi-resolution mesh, the authors used hierarchical bases (HB) composed of Lagrange finite element interpolation functions. These HB functions are customized using second-generation wavelet techniques for a fast convergence rate. At each step of the algorithm, the active set method along with mesh adaptation is used for solving the constrained minimization problem of contact case. Wavelet coefficients-based error indicators are used, and computation is focused on mesh zones with a high error indication. The authors take advantage of the wavelet transform to develop a parameter-free adaptive scheme to generate an appropriate and optimal mesh.

Adaptive wavelet Galerkin scheme (AWGS), a newly developed method for multi-scale mesh adaptivity in this work, is a combination of the second-generation wavelet transform and finite element method and significantly improves the accuracy of the results without approximating an additional problem of error estimation equations. A comparative study is performed taking a solution on a highly refined mesh and results are generated using AWGS.

The proposed adaptive technique can be utilized in the simulation of mechanical and biomechanical structures where multiple bodies come into contact with each other. The algorithm of the method is easy to implement and found to be successful in producing a sufficiently accurate solution with relatively less number of mesh nodes.

Although many error estimation techniques have been developed over the past several years to solve contact problems adaptively, because of boundary non-linearity development, a reliable error estimator needs further investigation. The present study attempts to resolve this problem without having to recompute the entire solution on a new mesh.

Historically, the idea of using electrostatic phenomena to produce motion has long stimulated the activity of scientists. Although the power generated by electrostatic motors is modest, the absence of windings and ferromagnetic material makes this kind of device competitive for applications characterized by low levels of torque and reduced volumes. During last years a renewed attention appeared towards electrostatic devices in the microscopic scale; their fabrication has been possible thanks to the technology for Si‐integrated‐circuits. In particular, electrostatic micromotors have an increasing role as position actuators when submillimetric movements are required. Methodologies of numerical simulation applied to microdevices are a helpful tool for the designer, who should fulfil criteria often in mutual clash like electromechanical response and fabrication cost. More generally, procedures of automated optimal design are now available, tackling the design problem as the constrained minimization of an objective function suitably set up.