The study proposes a data-driven model which combines the Dynamic Mode Decomposition with multi-linear interpolation to predict the thermal fields of nanofluid flows at unseen Reynolds numbers (Re) and particle volume concentrations ( \(\epsilon\) ). The performance of two models, namely the DMD-based and the CFD-based models, operating in one- and two-dimensional parametric spaces are investigated. Initially, a DMD with linear interpolation (DMD-LI) based solver is used for prediction of temperature of the nanofluid at any Re > 100. The DMD-LI based model, predicts temperature fields with a maximum percentage difference of just 0.0273%, in comparison with the CFD-based solver at Re =960, and \(\epsilon\) = 1.0%. The corresponding difference in the average Nusselt numbers is only 0.39%. Following that a DMD with bi-linear interpolation (DMD-BLI) based solver is used for prediction of temperature of the nanofluid at any Re > 100 and \(\epsilon\) > 0.5%. The performance of two different ways of stacking the data are also examined. When compared to the CFD-based model, the DMD-BLI-based model predicts the temperature fields with a maximum percentage difference of 0.21 %, at Re = 800 and \(\epsilon\) = 1.35%. And the corresponding percentage difference in the average Nusselt number prediction is only 6.08%.
