Effect of transmitter position on the torque generation of a magnetic resonance based motoring system

Strongly coupled magnetic resonance is most often used to transfer electrical power from a transmitter to a resonant receiver coil to supply devices over an air gap. In this work, the induced current in two receiver coils (stator and rotor) is used to generate torque on the rotor coil. The effect of the transmitter position relative to the stator and rotor receiver coils on the torque generation is studied in detail, both in simulation and experimentally. Results show a 36% to 37% gain in peak torque when properly varying the stator orientation for a given transmitter distance

Computational analysis of the effect of superparamagnetic nanoparticle properties on bioheat transfer in magnetic nanoparticle hyperthermia

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Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study

Accurate estimation of the human head conductivity is important for the diagnosis and therapy of brain diseases. Induced Current - Magnetic Resonance Electrical Impedance Tomography (IC-MREIT) is a recently developed non-invasive technique for conductivity estimation. This paper presents a formulation where a low number of material parameters need to be estimated, starting from MR eddy-current field maps. We use a parameterized frequency dependent 4-Cole-Cole material model, an efficient independent impedance method for eddy-current calculations and a priori information through the use of voxel models. The proposed procedure circumvents the ill-posedness of traditional IC-MREIT and computational efficiency is obtained by using an efficient forward eddy-current solver

Modeling transcranial magnetic stimulation from the induced electric fields to the membrane potentials along tractography-based white matter fiber tracts

Objective. Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well. Approach. The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics. Main results. This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient- and case-specific results. Significance. The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter- and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions

A robust inverse approach for estimating the magnetic material properties of an electromagnetic device with minimum influence of the uncertainty in the geometrical parameters

The magnetic properties of the magnetic circuit of an electromagnetic device (EMD) can be identified by solving an inverse problem, where sets of measurements are properly interpreted using a forward numerical model of the device. However, the uncertainties of the geometrical parameter values in the forward model result in recovery errors in the reconstructed material parameter values. This paper proposes a novel inverse approach technique, in which the propagations of the uncertainties in the model are limited. The proposed methodology adapts the cost function that needs to be minimized with respect to the uncertain geometrical model parameters. We applied the methodology onto the identification of the magnetizing B-H curve of a switched reluctance motor (SRM) core material. The numerical results show a significant reduction of the recovery errors in the identified magnetic material parameter values

A priori error estimation of magnetic material characteristics using stochastic uncertainty analysis

By interpreting electromagnetic or mechanical measurements with a numerical model of the considered electromagnetic device, magnetic properties of the magnetic circuit of that device can be estimated by solving an inverse numerical electromagnetic problem. Due to measurement noise and uncertainties in the forward model, errors are made in the reconstruction of the material properties. This paper describes the formulation and implementation of the error estimation and the prediction of which measurements that need to be carried out for accurate magnetic material characterization. Stochastic uncertainty analysis, based on Cramér-Rao bound (CRB), is introduced and applied to the magnetic material haracterization of a Switched Reluctance Motor (SRM) starting from mechanical (torque) and local magnetic measurements. The traditional CRB method that estimates the error due to measurement noise is extended with the incorporation of stochastic uncertain geometrical model parameters

An inverse approach for magnetic material characterization of an EI core electromagnetic inductor

In this paper, the complete magnetic material characteristic, hysteretic and anhysteretic, is reconstructed for an EI electromagnetic inductor. The material identification process, including air gap assessment, is carried out using a coupled experimental-numerical inverse technique, based on a set of well chosen global and local magnetic measurements. It is shown that a higher accuracy is obtained when local measurements are performed in regions with less stray fields, and the air gap assessment is strongly improved by the use of local magnetic measurements