The paper presents the design of a new device to heat a magnetic nanofluid in-vivo. The optimal design of the device has been carried by coupling Finite Elements (FE) solutions and various multi-objective optimization algorithms based on Non-dominated Sorting Genetic Algorithms (NSGA). The theoretical heating rate of the nanofluid, as resulting from the analytical solution that describes the heating rate in adiabatic conditions, is compared to the one calculated in a FE model that takes into account more real-life thermal conditions.
Multi-objective design of a magnetic fluid hyperthermia device
Forzan M.;Sieni E.;
2016-01-01
Abstract
The paper presents the design of a new device to heat a magnetic nanofluid in-vivo. The optimal design of the device has been carried by coupling Finite Elements (FE) solutions and various multi-objective optimization algorithms based on Non-dominated Sorting Genetic Algorithms (NSGA). The theoretical heating rate of the nanofluid, as resulting from the analytical solution that describes the heating rate in adiabatic conditions, is compared to the one calculated in a FE model that takes into account more real-life thermal conditions.
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