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Research Papers: Bio-Heat and Mass Transfer

Simulation of Phase Transition During Cryosurgical Treatment of a Tumor Tissue Loaded With Nanoparticles Using Meshfree Approach

[+] Author and Article Information
Sonam Singh

Department of Mathematics,
Indian Institute of Technology Roorkee,
Roorkee 247667, Uttrakhand, India
e-mail: sonaiitr@gmail.com

R. Bhargava

Department of Mathematics,
Indian Institute of Technology Roorkee,
Roorkee 247667, Uttrakhand, India
e-mail: rbharfma@iitr.ac.in

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received August 22, 2013; final manuscript received August 11, 2014; published online October 21, 2014. Assoc. Editor: Cila Herman.

J. Heat Transfer 136(12), 121101 (Oct 21, 2014) (10 pages) Paper No: HT-13-1430; doi: 10.1115/1.4028730 History: Received August 22, 2013; Revised August 11, 2014

In medical world, the minimally invasive freezing therapy or cryosurgery is an efficacious treatment for complete and controlled eradication of tumor cells. Many difficulties are encountered in cryosurgery process such as inappropriate freezing may not completely destroy the target tumor tissue and excessive freezing may harm the surrounding healthy tissues due to release of high amount of cold from the freezing probe. In present study, the target tumor tissue is loaded with nanoparticles in order to improve the freezing capacity of probe and to regulate the orientation and size of ice-ball formed during cryosurgery. In this process, phase transformation occurs in the undesired tumor tissues. For simulation of phase transition in bio heat transfer equation, the fixed-domain, heat capacity method is used to take into account the latent heat of phase change. In this study, a meshfree numerical technique known as element free Galerkin method (EFGM) is employed to simulate the phase transition and temperature field for a biological tissue subjected to nanocryosurgery. The latest nanofluid model which includes the effects of particles size, concentration, and the interfacial layer at the particle/liquid interface is utilized and their impact on freezing process is investigated in detail.

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Figures

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Fig. 1

Geometry description of the problem

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Fig. 2

Nodal discretization of the computational domain (a) with 617 nodes and (b) with 1147 nodes

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Fig. 4

Comparison of temperature fields obtained at the tumor center with previously published results

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Fig. 5

Temperature distribution within liver tissue with 10% loading of Al2O3 nanoparticles (a) during 6 min and (b) during 20 min

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Fig. 3

Selection of nodes for construction of shape functions within support domain of a quadrature point

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Fig. 9

Temperature response at 8 mm away from tumor center during 10 min of cooling for different size Al2O3 nanoparticles with φ=0.10, δ = 10

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Fig. 10

Temperature response at 8 mm away from tumor center during 10 min of cooling with variation in nanolayer to base fluid conductivity ration (δ) for Al2O3 nanoparticles (φ=0.10, dp = 10 nm)

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Fig. 6

Effect of different nanoparticles on ice-ball formed and freezing rate during 5 min of cooling (a) without nanoparticles (b) with magnetite (Fe3O4) nanoparticles (c) with alumina (Al2O3) nanoparticles (d) with gold (Au) nanoparticles

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Fig. 7

Temperature response at the center of tumor (i.e., at point (0, 30)) during 20 min of cooling for different nanoparticles (Au, Al2O3, Fe3O4) with φ = 0.10,dp = 10 nm, δ = 10

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Fig. 8

Temperature response at 8 mm away from tumor center during 10 min of cooling for different concentration of Al2O3 nanoparticles with dp = 10 nm, δ = 10

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