A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids

High thermal conductivity of carbon nanotube nanofluids(k_(nf))has received great attention.However,the current researches are limited by experimental conditions and lack a comprehensive understanding of k_(nf) variation law.In view of proposition of data-driven methods in recent years,using experimental data to drive prediction is an effective way to obtain k_(nf),which could clarify variation law of k_(nf) and thus greatly save experimental and time costs.This work proposed a neural regression model for predicting k_(nf).It took into account four influencing factors,including carbon nanotube diameter,volume fraction,temperature and base fluid thermal conductivity(k_(f)).Where,four conventional fluids with k_(f),including R113,water,ethylene glycol and ethylene glycol-water mixed liquid were considered as base fluid considers.By training this model,it can predict k_(nf) with different factors.Also,change law of four influencing factors considered on the k_(nf) enhancement has discussed and the correlation between different influencing factors and k_(nf) enhancement is presented.Finally,compared with nine common machine learning methods,the proposed neural regression model shown the highest accuracy among these.