Nanofluid research and applications: A review

Nanofluids have been widely used in various fields because of their unique thermal conductivity. This paper reviews and evaluates the thermal conductivity of nanofluids according to the recent literatures on the thermal conductivity of nanofluids. The results showed that in recent years, with the support of multiple disciplines, many new nanofluids have been prepared, such as cellulose nanofluids, graphene nanofluids, etc. These nanofluids have even better thermal conductivity. In terms of the prediction of thermal conductivity of nanofluids, neural network prediction has high accuracy and low time cost, so it is believed that it will become the main prediction method in the future. Although the experiments and studies of nanofluids have been carried out, there is no comprehensive review on the thermal conductivity of nanofluids from various aspects. In order to solve this problem, the research on nanofluids in recent years is reviewed from three aspects: influencing factors, prediction models and applications. The prediction models of thermal conductivity of nanofluids are introduced emphatically and each prediction model is evaluated. The neural network prediction models in recent years are discussed in detail. The purpose of this paper is to give a complete and detailed description of the thermal conductivity of nanofluids, as a reference for researchers to understand and study the thermal conductivity of nanofluids, and to update the latest research results in a timely manner. It is hoped to point out the direction for the research of nanofluids in the future.

Automated summary

Nanofluids, known for their unique thermal conductivity, have been widely utilized across various fields. Recent studies have shown the emergence of new nanofluids with even higher thermal conductivity. Neural network prediction models are expected to become the primary method for thermal conductivity prediction in the future. Current research is focused on reviewing and evaluating influencing factors, prediction models, and applications of nanofluids.