Development and validation of a magneto-hydrodynamic solver for blood flow analysis

Kainz, Wolfgang; Guag, Joshua; Benkler, S.; Szczerba, Dominik; Neufeld, Esra; Krauthamer, Victor; Myklebust, Joel B.; Bassen, H.; Chang, Isaac; Chavannes, N.; Kim, J H; Sarntinoranont, Malisa; Kuster, Niels

doi:10.1088/0031-9155/55/23/005

Cited by 10 publications

(13 citation statements)

References 9 publications

(10 reference statements)

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“…Similar studies were reported by Mekheimer [20] and Pandey and Tripathi [21]. The numerical solution of magnetohydrodynamic (MHD) signal produced by moving conductive blood flow in the great vessels of the heart, under a static magnetic field done by Kainz et al [22]. Das and Saha [23] studied magnetohydrodynamic pulsating blood flow in a rough thin-walled distensible conduit.…”

Section: Introductionsupporting

confidence: 56%

Parametric Analysis of Entropy Generation in Magneto-Hemodynamic Flow in a Semi-Porous Channel with OHAM and DTM

Rashidi

Parsa

Bég

et al. 2014

Applied Bionics and Biomechanics

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The magneto-hemodynamic laminar viscous flow of a conducting physiological fluid in a semi-porous channel under a transverse magnetic field has been analyzed by the optimal Homotopy Analysis Method (OHAM) and Differential Transform Method (DTM) under physically realistic boundary conditions first. Then as the main purpose of this study the important designing subject, entropy generation of this system, has been analyzed. The influence of Hartmann number (Ha) and transpiration Reynolds number (mass transfer parameter, Re) on the fluid velocity profiles in the channel are studied in detail first. After finding the fluid velocity profiles, graphical results are presented to investigate effects of the Reynolds number, Hartmann number,x-velocity of the moving plate, suspension height and dimensionless horizontal coordinate on the entropy generation.

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Section: Introductionsupporting

confidence: 56%

Parametric Analysis of Entropy Generation in Magneto-Hemodynamic Flow in a Semi-Porous Channel with OHAM and DTM

Rashidi

Parsa

Bég

et al. 2014

Applied Bionics and Biomechanics

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“…An anatomical model was functionalized with data from MRI flow velocimetry in 2D cross sections, which was then used as a transient boundary condition for blood flow simulations in the aorta and vena cava compartments. Subsequently, the ECG signal distortion due to the magneto-hemodynamic effect (present during high magnetic field exposure) was modeled using coupled EM simulations (Kainz et al, 2010 ), in order to assess its suitability as non-invasive biomarker for blood-flow features. The co-registered flow velocimetry data was used in the same study to validate the computational fluid-dynamics predictions, while co-registered surface potential measurement data served to validate the electric potential modeling predictions.…”

Section: Methods and Resultsmentioning

confidence: 99%

Functionalized Anatomical Models for Computational Life Sciences

Neufeld

Lloyd

Schneider³

et al. 2018

Front. Physiol.

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The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.

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“…For establishing EP studies in a clinical MR environment, the investigation and characterization of intracardiac MHD potentials which can be also recorded by EP catheters is important. Thus, compared to previous studies standard clinical equipment was used in this work to acquire MHD potentials simulated in vitro. It is very difficult to accurately reproduce intracardiac flow by experimental models.…”

Section: Discussionmentioning

confidence: 99%

“…The MHD effect is sufficiently large to distort the surface ECG and it also alters intracardiac EGMs as shown by Schmidt in his oral presentation of abstract Tse et al . A recently published study by Kainz et al 2010 compared the MHD effect observed in a tube placed in a low magnetic field of 0.2 T with results obtained by simulations, as well as with an analytical equation, to validate their numerical algorithm for calculating MHD voltages. Recently, in a phantom study properties of the MHD effect predicted by theory such as its velocity dependency, orientation of the electrodes with respect to the external magnetic field of 7 T, and the dependency on the electrode distance were investigated .…”

Section: Introductionmentioning

confidence: 98%