Numerical Simulation of Phase Change Dual-Phase-Lag Bioheat Model With Nanocryosurgery Using Radial Basis Function Meshfree Approach

Abstract

The current study is concerned with the numerical simulation of the phase change process in a two-dimensional (2D) dual-phase-lag (DPL) bioheat model applied to nanocryosurgery. A Gaussian radial basis function (RBF) meshfree approach coupled with a Crank–Nicolson type of time discretization is employed on an irregular soft tissue domain. The simulation considers the introduction of three types of nanoparticles (NPs)—gold, alumina oxide, and iron oxide into the cryosurgical process. Temperature profiles were computed for situations both with and without the incorporation of nanoparticles, and the freezing interface was analyzed under different conditions. The results demonstrate the significant influence of nanoparticles on enhancing the freezing process, leading to a more controlled and effective cryoablation. The inclusion of nanoparticles not only accelerates the freezing front but also provides a more uniform temperature distribution within the target tissue. This study highlights the advantages of using a meshfree RBF approach in handling complex geometries, alongside the potential of nanoparticle-enhanced cryosurgery to improve clinical outcomes. These findings contribute valuable insights into the optimization of cryosurgical techniques and the development of more effective cancer treatments.