Data-driven methods for estimating the effective thermal conductivity of nanofluids: A comprehensive review

There is a growing body of work in the field of nanofluids and several investigations have been conducted on their thermal conductivities. While the experimental works require considerable investments to provide a highly equipped laboratory and proper instruments, the predictive models are useful to promote understanding nanofluids under different operational conditions. However, the restrictions on the traditional predictive models require scholars to develop reliable models that are capable of simulating the thermophysical properties of nanofluids. Data-driven models have attracted huge attention and been widely applied in various fields. There is no specific review on application of data-driven models in thermal conductivity of nanofluids till now and thus this is the first review paper which gives a state of the art review of research progress in this field of study. It was identified that the effective thermal conductivity of nanofluids is reflected well by the new predictive models. The authors believe that the data-driven models are fast, reliable, simple-to-use, and practical. This may open a new door for the scientists, engineers, and researchers in the field of nanofluid for calculating the effective thermal conductivity with excellent precision. This investigation leads to the recognition of the main problems and the opportunities in the research area for the future works.