Use of artificial neural network optimization for MHD and non-Newtonian peritoneal Jeffery nanofluid dynamics on female reproductive health.

The dynamics of peritoneal fluid has vital applications in various physiological functions, particularly in female reproductive physiology, which modulates the transport of gametes within the fallopian tubes. This research aims to study the interactive operating factors of peritoneal fluid flow, heat transfer, and mass transport under various physiological and pathological conditions. In this regard, the Jeffrey fluid model is applied to examine the non-Newtonian behavior of peritoneal fluid and its responses to thermal and magnetic influences. The study extends to consider thermophoresis and Brownian motion of nanoparticles in nanofluids with the purpose of enhancing thermal conductivity and optimizing fluid properties toward better reproductive health. Magnetic field effects on fluid dynamics, using magnetohydrodynamics, are also explored in relation to possible therapeutic interventions for endometriosis and tubal factor infertility. Numerical solutions and graphical interpretations are used to illustrate the impact of salient parameters such as Grashof numbers, Brownian motion, and shear-dependent viscosity on the fluid behavior. The results deepen the current understanding of the mechanics of peritoneal fluids and provide potential improvements in fertility treatments and biomedical applications. The proposed model can be applied to develop a non-invasive diagnostic technique for detection of endometriosis, to optimize infertility treatments and to design biomedical devices for peritoneal fluid manipulation in female reproductive therapy.