Deep Learning for Nanofluid Field Reconstruction in Experimental Analysis

Experiment is an important method to study the thermal and flow performance. Nevertheless, only limited information, such as local temperature and pressure, can be obtained through detection machines. Based on deep learning, a general, useful and flexible reconstruction model is proposed to reconstruct global flow field in two-dimension domain with the limited information exploiting from experiments as input information. Besides, the corresponding performance parameters Nu and $f $ are extracted from generated fields. To validate the feasible, stability and accuracy of the framework, the micro channels with nano fluids are taken as validation case. First of all, the comparison between reconstructed fields and original fields are presented. It shows that reconstructed fields are almost the same as original ones and extracted performance parameters also have high precision. Next, the effects of train size, measuring uncertainty and measuring layouts are considered in sensitivity analysis. Higher train size and smaller measuring uncertainty are advantageous to the reconstruction results. Measuring layout has little influence on reconstruction performance and at least 7 local measuring points are enough.