Pubblicazioni Scientifiche

Due to manufacturing and assembly variations, there are tolerances associated with the nominal dimensions of complex electromagnetic devices. A possible approach to take into account such tolerances in the early design phases is the “worst case tolerance analysis”, whose goal is to determine the effect of the largest variations on assembly dimensions on the product performance. On the other hand, when many tolerances must be considered, locating the worst case with combinatorial effects could get very time consuming, since performance assessment of such devices may require numerical field analysis. In the paper, a fast approach based on the performance sensitivity with respect to design parameters is presented, and applied to the tolerance analysis of Nuclear Magnetic Resonance (NMR) magnets.

The optimal design of electromagnetic devices is often performed by balancing conflicting objectives, including performance and other relevant quality figures such as cost and robustness. The balance of conflicting partial objectives can be tackled with the Pareto approach. Unfortunately, assessment of robustness typically implies a relevant increase in the computational cost of optimization procedures; in the paper, it is proposed an approach allowing to include robustness in Pareto optimization with a reduced increase in the computational cost.

Magnets for Nuclear Magnetic Resonance applications are designed to provide high level of magnetic flux density in a testing volume with the greatest level of field homogeneity. Anyway, the manufacturing and assembly tolerances cause unavoidable uniformity degradation, that is usually counteracted by correction coils. The measurement of the magnetic field is the final verification of the complex design and fabrication process of a magnetic system, aimed also at defining currents in correction coils to improve uniformity. Anyway, when seeking high accuracy, it is necessary to assess the effect on final uniformity of uncertainties in the measurement technique. In this paper, an approach is presented to assess impact of measurement error bars on final device performance, based on Worst Case Analysis, guaranteeing quick computational time.

In this paper we explore the feasibility of applying multi objective stochastic optimization algorithms to the optimal design of switching DC-DC converters, in this way allowing the direct determination of the Pareto optimal front of the problem. This approach provides the designer, at affordable computational cost, a complete optimal set of choices, and a more general insight in the objectives and parameters space, as compared to other design procedures. As simple but significant study case we consider a low power DC-DC hybrid control buck converter. Its optimal design is fully analyzed basing on a Matlab public domain implementations for the considered algorithms, the GODLIKE package implementing Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). In this way, in a unique optimization environment, three different optimization approaches are easily implemented and compared. Basic assumptions for the Matlab model of the converter are briefly discussed, and the optimal design choice is validated “a-posteriori” with SPICE simulations.