Artificial neural network based finite element analysis of irregular heat sink/source effects on Boger nanofluid flow in a triangular enclosure with a heated cylindrical obstacle using the Cattaneo–Christov heat flux model

Qadeer Raza, Shuke Li, Xiaodong Wang, Tahir Mushtaq, Bagh Ali and Nehad Ali Shah

Chaos, Solitons & Fractals, 2025, vol. 200, issue P2

Abstract: This research focuses on enhancing heat transfer within triangular enclosures, a key requirement for effective thermal management in compact electronic systems and solar thermal devices. The study investigates the thermal behavior of Boger nanofluid flow around a centrally located cylindrical obstacle under different thermal boundary conditions (cold, hot, and adiabatic). The model integrates copper nanoparticles to improve thermal conductivity and accounts for the effects of magnetic fields, thermal radiation, Cattaneo-Christov heat flux, and non-uniform heat sources/sinks. The governing continuity, momentum, and energy equations are transformed into a nondimensional form using appropriate similarity variables. A hybrid approach is employed, combining the finite element method (FEM) for numerical simulation with an Artificial Neural Network (ANN) trained using the Levenberg–Marquardt algorithm for predictive modeling. Simulations are conducted to assess the impact of various parameters, such as the type of cylindrical obstacle (cold, heated, or adiabatic), solvent fraction (20.0≤β1≤60.0), relaxation time ratio (1.0≤β2≤5.0), magnetic field strength (1.0≤M≤5.0), Darcy number (103≤Ra≤104), Rayleigh number (10−2≤Da≤1.0), thermal radiation parameter (50.0≤Rd≤150.0), thermal relaxation coefficient (3≤γ≤9), and non-uniform heat source/sink (100.0≤A∗≤1.6,0.0≤B∗≤300.0). The results demonstrate that increasing the Darcy and Rayleigh numbers significantly enhances convective flow and thermal transport, while changes in solvent fraction and relaxation time ratio lead to contrasting effects on flow field ad temperature distribution. The findings have practical relevance in the design of high-efficiency thermal enclosures for microelectronic cooling and solar energy harvesting systems, where accurate temperature control is essential.

Keywords: Finite element method; Artificial neural network; Triangular cavity; Cattaneo–Christov heat flux; Boger nanofluid; Magnetohydrodynamics; Irregular heat sink/source (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925010781
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p2:s0960077925010781

DOI: 10.1016/j.chaos.2025.117065

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().