Surrogate modeling of heat transfers of nanofluids in absorbent tubes with fins based on deep convolutional neural network

Abstract

In this paper, we propose and investigate a deep convolutional neural network-based surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins. Inspired by Unet, the proposed model consists of a contracting path and an expanding path, and skip connections are replaced by residual blocks to enhance prediction performance. The model can predict temperature field of tubes’ specified cross-sections in any quantity. The nanofluid-filled absorbent tubes with two types of fins (rectangular and semiepllise) are investigated, where the shapes of the fins are variable. The results show that the proposed model can predict the temperature field with very high accuracy and extremely fast speed: the average prediction error is less than 0.15% for all studied cases, and the prediction speed is more than 4 orders faster than numerical simulation with OpenFOAM. In addition, we analyze the main factors affecting the network model, including the learning rate, data size, network's layer, activation function and input presentation. It is found that the influence of activation function is significant, where ReLU6 is able to reduce the max prediction error to 2.7%, while the Tanh and Sigmoid functions have two and four times larger errors, respectively. The results of our current work show great application potential of the deep neural network-based surrogate model for rapid 3D geometry optimization of the nanofluid-filled fined absorbent tube of heat exchangers and solar collectors.