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. Similar content being viewed by others Abbreviations - \(\overline{X}\) : - Dimensional axial co-ordinate - \(\overline{Y}\) : - Dimensional transverse co-ordinate - \(\overline{U}\) : - Dimensional velocity along \(\overline{X}\)-direction - \(\overline{V}\) : - Dimensional velocity along \(\overline{Y}\)-direction - T: - Dimensional temperature - \(\bar C\) : - Dimensional concentration - \(\bar P\) : - Dimensional pressure - \(\Psi\) : - Non-dimensional shear stress - \(x\) : - Non-Dimensional axial co-ordinate - \(y\) : - Non-Dimensional transverse co-ordinate - \(u\) : - Non-Dimensional velocity along \(\overline{X}\)-direction - \(v\) : - Non-Dimensional velocity along \(\overline{Y}\)-direction - \(\theta\) : - Non-Dimensional temperature - \(\Omega\) : - Non-Dimensional concentration - \(\overline{p}\) : - Non-Dimensional pressure - \(\psi\) : - Non-dimensional shear stress - \(\rho _{e}\) : - Electrical charge density - \(\overline{p}\) : - Pressure - \(\rho\) : - Fluid density - \(E_{0}\) : - Axial electric field - \(c_{p}\) : - Specific heat - \(k\) : - Thermal conductivity - \(k_{t }\) : - Ratio of thermal diffusion - \(D_{B}\) : - Brownian Diffusion Coefficient - \(D_{T}\) : - Thermophoretic Diffusion Coefficient - \(D_{m}\) : - Mass diffusivity co-efficient - \(T_{m}\) : - Mean temperature - \(\overline{A}_{n}\) : - Rivlin-Erickson tensor - \(m_{e }\) : - Electroosmosis parameter - \(\lambda\,_{D }\) : - Debye length - \(\alpha\) : - Peristaltic wave number - \(U_{HS }\) : - Helmholtz-Smoluchowski velocity - \(Br\) : - Brinkmann number - \(Re\) : - Reynolds number - \(\mathrm{Pr}\) : - Prandtl number - \(\sigma\) : - Porous parameter - \(\varepsilon\) : - Amplitude ratio - \(Nt\) : - Non-dimensional thermophoresis parameter - \(Nb\) : - Non-dimensional Brownian motion parameter - \(Ec\) : - Eckert number

The authors acknowledge the research support received from Universiti Teknikal Malaysia Melaka. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. About this article Cite this article Anuradha, K.D., Dhinakaran, V., Viharika, J.U. et al. Use of artificial neural network optimization for MHD and non-Newtonian peritoneal Jeffery nanofluid dynamics on female reproductive health. J Biol Eng (2026). https://doi.org/10.1186/s13036-026-00690-5 Received: Accepted: Published: DOI: https://doi.org/10.1186/s13036-026-00690-5