ABSTRACT In this study, a fuzzy‐artificial neural network (ANN) method considering uncertainty in nanoparticles’ volume fraction has been presented to analyze the heat transfer phenomena in Carreau–Yasuda Fe 3 O 4 –Cu hybrid nanofluid through a porous Riga plate. Triangular fuzzy numbers with values 0, 0.05, 0.1 have been adopted for modeling uncertainty in nanoparticle volume fraction, whereas the α ‐cut technique has been used to obtain numerical solutions for uncertain scenarios. The study has found that the increase in nanoparticle volume fraction from 0.01 to 0.1 causes a significant enhancement in heat transfer rate, as the Nusselt number increases from about 0.306 to 0.417, indicating the better performance of hybrid nanofluids compared with single‐particle nanofluids. Moreover, fuzzy‐based calculations reveal that heat responses fall within certain boundaries, and there is a lower degree of fuzziness at higher α values. Furthermore, ANN‐based predictions are found to be highly consistent with the numerical results.
Hussain et al. (Fri,) studied this question.