Levenberg-Marquardt recurrent neural network for heat transfer in ternary hybrid nanofluid flow with nonlinear heat source-sink

• Published in: Advances in mechanical engineering Vol. 17; no. 6
• Main Authors: Mahariq, Ibrahim, Ullah, Kashif, Fiza, Mehreen, Jan, Aasim Ullah, Ullah, Hakeem, Islam, Saeed, Al-Mekhlafi, Seham M.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2025; Sage Publications Ltd; SAGE Publishing
• Subjects: Accuracy; Back propagation networks; Cooling systems; Flow characteristics; Fluid flow; Heat flux; Heat sources; Heat transfer; Industrial applications; Magnetohydrodynamics; Management systems; Manganese zinc ferrites; Nanofluids; Neural networks; Nonlinear differential equations; Partial differential equations; Radiation; Recurrent neural networks; Silver; Temperature distribution; Thermal analysis; Thermal management; Thermal radiation; Thermal relaxation; Training evaluation; Two dimensional flow; CCHF; THNF; MnZn Fe 2 O 4 and Cu nanoparticles; Ag; non-linear thermal radiation
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/16878132251341968

This study focuses on the application of ternary hybrid nanofluids (manganese zinc ferrite, copper, and silver) over a spinning disk, which has significant implications for thermal management, biomedical devices, aerospace, and industrial cooling systems. Due to the antibacterial and antifungicidal properties of silver (Ag) nanoparticles, this research also has potential applications in the food industry for sterilization and preservation. Motivated by these developments, this study investigates the Steady two-dimensional Ternary Hybrid Nanofluid Flow (STDTHNFF) problem, incorporating a nonlinear heat source-sink and Fourier heat flux model (HSFHFM) over a spinning disk. A key novelty of this work is the inclusion of a new heat source term, enhancing the thermal analysis by capturing additional energy variations. The study extensively analyzes the effects of heat sources, thermal radiation, the thermal relaxation parameter, and magnetohydrodynamic (MHD) effects, providing a more comprehensive understanding of fluid flow and heat transfer mechanisms in rotating systems. The governing coupled nonlinear partial differential equations (NLPDEs) are transformed into a dimensionless form using relevant similarity transformations. The Recurrent Neural Network-Levenberg-Marquardt Method (RNN-LMM) is employed for backpropagation, providing an efficient and accurate computational approach for solving the problem. A numerical stochastic approach is applied to evaluate training (TR), mean square errors (MSE), performance (PF), and data fitting (FT). Validation is conducted using error histograms (EH) and regression (RG) tests, ensuring high accuracy ranging from E-2 to E-7. The results demonstrate that the RNN-LMM approach effectively predicts flow characteristics with high accuracy. Graphs and numerical data reveal the influence of heat sources, thermal radiation, MHD effects, and thermal relaxation on flow behavior. The findings confirm that ternary hybrid nanofluids (THNF) enhance heat transfer rates, making them promising for industrial and engineering applications. The study highlights that heat sources significantly impact temperature distribution and heat transfer. The results of the RNN-LMM approach were compared with previous literature and found to closely align with published studies. Furthermore, these findings play a crucial role in improving thermal management systems and processes for advanced engineering and industrial applications.