Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Abstract

Natural convection in an enclosure is a classical problem in heat transfer field. In this study, natural convection induced by the heat source in the enclosure is studied with two analysis methods, i. e. CFD and artificial neural networks (ANN). The heat transfer in the enclosure is an unsteady process. During this process, the temperature fields in the enclosure are changing with time. The vertical temperature field of y = 0 at one moment is picked up for investigation. Firstly, FLUENT software which is a simulation program of CFD is adopted to simulate the temperature fields under different computation conditions. Then part of the simulation condition's temperature data is picked for training an ANN model and the rest of data is used for validating the ANN model. It has been found from the comparison between the CFD simulation and ANN prediction that the two results have a good agreement with each other. In the comparison, the max relative errors are around 12%, mean relative errors are around 0.3%, mean square errors are around 0.6%, values of absolute fraction of variance are all not less than 0.99. The results demonstrated that the ANN prediction have enough accuracy.

Performance improvement of an industrial control enclosure cooling system

“…Moreover, the problem of natural convection in an enclosure in heat transfer field was investigated with CFD and ANN. It has been found from the comparison between the CFD and ANN prediction that the two results have a good agreement with each other [18]. And, the ANN model was constructed and trained based on the part of database from CFD simulation.…”

Machine Learning and IoT Trends for Intelligent Prediction of Aircraft Wing Anti-Icing System Temperature

“…ANNs are considered a subset of ML [25][26][27][28][29][30][31][32][33][34][35][36][37]. ANNs are considered one of the most important elements of deep learning, as they lie at the heart of the algorithms underlying this learning.…”

“…Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is higher than the specified value of the threshold, that node is exited and activated, and data are sent to the next network layer [25][26][27][28][29][30][31][32][33][34][35][36][37]. Otherwise, no data will be sent to the next network layer.…”

“…The technology of deep learning (DL) has been used in many different applications, including smart homes, banking, marketing, airlines, and the aviation field [25][26][27][28][29][30][31][32][33][34][35]. In addition, DL has been used to improve the results of simulation software, controllers, and forecasting models.…”

“…It is worth noting that one of the classic problems in the field of heat transfer is the analysis of natural convection in different spaces, such as containers. In [25], two different methods were used to analyze the convection resulting from a heat source in a can, namely, ANN and CFD. An accurate design of an expert system for predicting the time required for convergence and the number of iterations based on CFD and ANNs is presented in [26].…”