Study On The Mechanism Of Graphene Nanofluid Thermal Enhancement

With the rapid development of the manufacturing and energy industries,the requirements for thermal management technology have become more demanding.Graphene nanofluids with high thermal conductivity are expected to become a new generation of heat transfer fluid,which will fundamentally improve the heat transfer efficiency of mechanical equipment and products.The thermal conductivity test and mechanism study of graphene nanofluids have received extensive attention from scholars.The main work of this article includes:(1)A nanofluid thermal conductivity measurement system was set up and calibrated.Based on the measurement principle of 3?method,combined with the powerful LabVIEW platform for measurement and control,and specific experimental steps,a nanofluid thermal conductivity measurement system was designed.The experiment of measuring the thermal conductivity of deionized water shows that the system error is less than 5%,and the measurement system has high reliability and stability.(2)The stability of graphene nanofluid was prepared and tested.The two-step method is used to prepare experimental graphene nanofluids.The stability of the nanofluids is improved by selecting a base fluid with a high viscosity and ultrasonic oscillation.The stability of the graphene nanofluids was determined by the method of visual sedimentation and thermal conductivity measurement.(3)The thermal conductivity mechanism of graphene nanofluids was studied and the effective thermal conductivity of graphene particles under the influence of the adsorption layer was derived.Microscopic observation revealed no obvious agglomeration in the graphene nanofluid in the experiment,indicating that the high thermal conductivity of the graphene nanofluid was not caused by graphene particle agglomeration.Subsequently,through the experimental study of the thermal conductivity of graphene/hot fag and graphene/PEG with solid-liquid phase changes,the Brownian motion mechanism was excluded as the main cause of the high thermal conductivity of graphene nanofluids.The numerical solution of the average thermal conductivity k_l in the adsorption layer is derived by summarizing the research on the thickness of the existing adsorption layer and the thermal conductivity in the adsorption layer.The equivalent thermal conductivity formula of composite particles composed of flake graphene particles and adsorption layer is further proposed.Finally,experiments have shown that the thermal conductivity of graphene nanofluids increases with the size of the nanoparticles.(4)The thermal conductivity prediction model of graphene nanofluid was established and compared with the thermal conductivity modeling based on neural network method.Based on the Nan model,the effects of graphene thickness(number of layers)and surface flatness on the effective thermal conductivity of graphene are quantitatively analyzed.A new model for predicting the thermal conductivity of graphene nanofluids is established.This model takes into account the Effects of shape,interfacial thermal resistance R_k,adsorption layer,and effective thermal conductivity of graphene.The influence of the interface thermal resistance and surface flatness on the predicted thermal conductivity in the model was analyzed,and the graphene/lubricant measured values were compared with the predicted values.The results show that the model can well predict the thermal conductivity of graphene nanofluids.Then,the optimal topology structure of graphene/lubricating oil is simply predicted by BP neural network through temperature and volume fraction,and the advantages and disadvantages of thermal conductivity predicted by theoretical model and neural network method are compared.The prediction method of the thermal conductivity of the neural network can be used as a supplementary method for the study of nanofluids.The research shows that a well-established nanofluid measurement system has high reliability and stability,and the method of selecting a base fluid with a relatively high viscosity and ultrasonic oscillation can effectively improve the stability of the graphene nanofluid prepared by the two-step method.Experiments show that the agglomeration and Brownian motion of graphene particles are not the main reason for the increase in thermal conductivity of graphene nanofluids.The newly established prediction model for the thermal conductivity of graphene nanofluids can better predict the thermal conductivity of graphene nanofluids.Compared with the neural network method,the thermal conductivity prediction of the graphene nanofluid can better reveal the mechanism behind the enhanced thermal conductivity of graphene nanofluids.The network thermal conductivity prediction method can be used as a supplementary method for nanofluid research,reducing the impact of cost,time and experimental errors.