Adjoined ISPH method and artificial intelligence for thermal radiation on double diffusion inside a porous L-shaped cavity with fins

• Published in: International journal of numerical methods for heat & fluid flow 2024-03, Vol.34 (4), p.1832-1857
• Main Authors: Elshehabey, Hillal M., Çolak, Andaç Batur, Aly, Abdelraheem
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Artificial intelligence; Artificial neural networks; Computer simulation; Darcy number; Datasets; Distribution; Double diffusion; Engineering; Fins; Fluid flow; Fluid mechanics; Fluxes; Heat conductivity; Hydrodynamics; Incompressible flow; Mass transfer; Mathematical models; Nanofluids; Nanoparticles; Neural networks; Parameters; Radiation; Smooth particle hydrodynamics; Specific heat; Temperature; Temperature distribution; Thermal radiation; Training; Velocity; Velocity distribution; Viscosity
• ISSN: 0961-5539; 1758-6585; 0961-5539
• DOI: 10.1108/HFF-11-2023-0677

Purpose The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins. Design/methodology/approach The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm. Findings The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers. Originality/value A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.