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Optimizing heat and mass transfer of Nano-Enhanced Phase Change Material (NEPCM) within complex geometries has numerous practical applications across various industries. These include enhancing the performance of heat exchangers, improving the efficiency of solar energy systems, optimizing the cooling of electronic devices, designing more efficient energy systems, and enhancing the processing efficiency in food industry applications. The incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence (AI) are joined to handle the thermosolutal convection of NEPCM inside an arc shape. Different lengths of heat/mass sources are considered in this work. The embedded tree fin is maintained at low temperature and concentration with zero velocities. AI predicted accurately the mean Nusselt and Sherwood numbers by integration with the ISPH simulations that have been done. Heat and mass transmission were investigated for various lengths of partial heat/mass sources, fractional time derivative, Frank-Kamenetskii number, Hartmann number, Rayleigh number, buoyancy ratio parameter, Soret number, Lewis number, and Dufour number. Expanded lengths of heat/mass sources in arc borders improved heat and mass transport inside an arc shape. The fractional time derivative helps in reducing the computing time necessary to achieve a steady state. The Rayleigh number supports the buoyancy forces that raise the velocity field and temperature and concentration distributions inside an arc shape. By including an arc-shaped cooling tree fin, the magnetic field, Lewis number, and buoyancy ratio characteristics are reduced. The Frank-Kamenetskii number contributes to better temperature dispersion. Examples of heat/mass transfer in an arc form with a tree fin include heat exchangers, solar cells, energy systems, electronic device cooling, and food processors.
Alhejaili et al. (Thu,) studied this question.
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