A novel neural network and grey correlation analysis method for computation of the heat transfer limit of a loop heat pipe (LHP). (15th November 2022)

• Record Type: Journal Article
• Authors: Liu, Xuexiang; Liu, Haowen; Zhao, Xudong; Han, Zhonghe; Cui, Yu; Yu, Min
• Is Part Of: Energy. Volume 259(2022)
• Publication Date: 2022-11-15
• Subjects: Loop heat pipe -- Artificial neural network -- Grey correlation coefficient -- Prediction model; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124830
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: A loop heat pipe (LHP) has the advantages of larger heat transfer capacity and anti-gravity operational performance. The current prediction models for LHP heat transfer capacity have the difficulties in popularization of data volume and determination of accurate parametrical data, leading to the uncertain and varying outcomes that are inconsistent and away from reality. To address these challenges, this paper developed a first-of-its-kind big-data-driven LHP heat transfer limit prediction model by employing the neural network and grey correlation analysis method, which have advantages of high precision and large data volume. A double-layer feedforward neural network with sigmoid hidden neuron and linear output neuron was constructed to predict the heat transfer limit of the LHP. The grey scale analysis is applied to select the variables with correlation coefficient greater than 0.5, thus giving the clear identification of the both input parameters (e.g. refrigerant temperature, filling liquid quantity, height difference between evaporator and condenser, and number of heat pipe array) and output ones (heat transfer limit). The previously validated LHP heat transfer limit calculation model is used to calculate the heat transfer limit corresponding to the selected parameters, thus formulating 1, 010, 038 sets of data points. Of those calculated datasets, 707, 026 (70% of data) are treated as a training set, 151, 506 (15% of data) as a verification set, and 151, 506 groups of data (15% of data) as the test sets for training. After several optimization and debugging, the number of hidden layer neurons is determined to be 100. The correlation coefficient (R), mean square error (MSE) and mean relative error (MRE) are 0.9997, 52.7 and 0.32% respectively, all of which are within reasonable accuracy range. The results show that the model has good prediction accuracy and consistence and is an effective tool to characterize and optimize the LHP in various application synergies. Highlights: A novel neural network and grey correlation analysis method for heat transfer limit of loop heat pipe. Performance prediction for loop heat pipe. Performance evaluation and optimization for loop heat pipe via grey correlation method and artificial neural network.