Advanced thermal management in X-ray systems using magnetohydrodynamic nanofluids and Cattaneo-Christov heat flux model

Abstract

This research explores the utilization of magnetohydrodynamic nanofluids to enhance thermal control in high-energy systems, such as X-ray devices, where efficient heat dissipation is essential for optimal performance and lifespan. By integrating analytical and numerical approaches, the study examines heat transfer in nanofluids confined between parallel plates, incorporating thermal radiation and the Cattaneo-Christov heat flux model. This model offers a more precise representation of heat transfer compared to traditional Fourier's law, especially in scenarios involving rapid thermal fluctuations typical in X-ray equipment. The investigation employs similarity transformations to simplify the governing equations continuity, momentum, and energy transforming them into ordinary differential equations. These equations are then solved using the Homotopy Perturbation Method and the fourth-order Runge-Kutta technique. The study assesses the influence of various parameters, including magnetic field intensity, squeeze number, nanoparticle concentration, heat source, thermal relaxation, and radiation, on velocity and temperature profiles. The findings reveal that the Cattaneo-Christov model predicts lower temperature distributions compared to Fourier's law, which is crucial for accurate thermal management in X-ray systems. Increasing the magnetic field strength results in reduced velocity and temperature due to the Lorentz force. Notably, the inclusion of nanoparticles significantly enhances heat transfer, making nanofluids a promising solution for cooling high-energy systems in radiology and X-ray machines.

Keywords: Cattaneo-Christov; Heat transfer; Homotopy perturbation method; MHD; Nanofluid; X-ray device cooling.

Fig. 1

depicts the schematic representation and coordinate system for the flow setup under investigation.

Fig. 2

Dimensionless temperature distributions derived via the homotopy perturbation method (HPM) and numerical computation (NUM) are presented, highlighting the influence of the radiation parameter N and the concentration of nanoparticles ϕ.

Fig. 3

Dimensionless temperature distributions obtained through the homotopy perturbation method (HPM) and numerical techniques (NUM) illustrate the influence of the heat source coefficient and the thermal relaxation coefficient .

Fig. 4

Presents dimensionless velocity profiles derived from both the homotopy perturbation method (HPM) and numerical analysis (NUM), highlighting how the squeeze number and the magnetic parameter influence the results.

Fig. 5

Dimensionless temperature profiles obtained using the Homotopy Perturbation Method (HPM) and the numerical method (NUM), illustrating the effects of the squeeze number and the magnetic parameter .