IEEE Transactions on Applied Superconductivity · 2004 · 15 citations · 11 references
ArchitectureElectrical EngineeringDesign OptimizationElectromagneticsElectromagnetismEvolutionary OptimizationGenetic AlgorithmsMagnetic Resonance ImagingMagnetic Resonance Imaging PhysicsCurrent DensityElectromagnetic DesignGenetic Algorithm Based DesignGenetic Algorithms For DesignMagnet Design OptimizationMagnetic Field StrengthMagnetic ResonanceOptimal Design
Designs of magnetic resonance imaging (MRI) main magnets obtained from both a functional method and a genetic algorithm method have been compared. While most features in the two approaches are similar, there are several important differences. The functional method leads to fewer coil bundles and a reduced total current, i.e., total ampere turns, (e.g., 6-8 MA) that can be as much as 70% of the total current found with the genetic analysis. While the conclusion about stress is that it is a sensitive function of the choice of wire current density, the designs found with the functional method have a larger hoop stress than that of the genetic design, which may require new or refined manufacturing techniques. Furthermore, the functional approach requires much less computing power (i.e., a personal computer is quite sufficient) while the genetic algorithm method in general demands massively parallel computing techniques. However, in order to search for the optimal magnetic resonance design at a given field strength, it is likely that a combination of these two methods will lead to the best results.
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Numerical recipes in Pascal: the art of scientific computing
Choice Reviews Online · 1990 · 11.9K citations
G. Morrow · IEEE Transactions on Applied Superconductivity · 2000 · 53 citations