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This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

Electrodynamic bearings (EDBs) are a promising way to support rotors passively with no friction. In particular, heteropolar EDBs could allow for combining the motor and guiding functions, thereby optimizing the use of permanent magnets. Despite this advantage, few efforts have been dedicated to the evaluation and optimization of the performance of heteropolar EDBs. In this paper, the performance of a yokeless topology of heteropolar EDB is evaluated and optimized. This is done by evaluating the parameters of a parametric dynamical model of the EDB using a twodimensional analytical model of the field distribution in the bearing. Compared to existing EDBs, the present one is shown to achieve a reasonable stiffness to permanent magnet volume ratio at high speeds. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study different permanent magnet (PM) bearings geometries based on PM rings with a minimum volume of PM for a given load. Simple and general scaling laws (SCLs) are proposed for each bearing geometry to predict its behavior. These SCLs allow a fast sizing of those geometries, as a function of the number of PM rings, the load reference, the air gap, and the remanent flux density. Those SCLs are derived for optimized topologies without any constraint on the geometrical parameters, as well as for optimized geometries with a constraint on the PM dimensions. For both cases, SCLs for the thrust-bearing stiffness are also proposed. [ABSTRACT FROM AUTHOR]