Abstract:
Optimizing the thermal conductivity of nanofluids is essential for advanced heat
transfer applications. This study explores an artificial intelligence-based
approach, using genetic algorithms to optimize the nanoparticle volume frac-
tion in nanofluids. Leveraging the modified Maxwell-Garnett model, this re-
search incorporates the effects of Brownian motion as well as key physical
parameters, such as material conductivity, particle size, and temperature. The
genetic algorithm, implemented with the DEAP library, is specifically designed
to fine-tune the parameters adaptively, using appropriate genetic operators like
crossover, mutation, and selection
