Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

Abstract

By the merits of self-stability and low energy consumption, high temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes 5 different neural network models (RBF, DNN, CNN, RNN, LSTM) to predict HTS maglev guiding force, and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptively iterative algorithms for parameters matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. And according to the results, it is revealed that, the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90000, respectively. Moreover, the K-fold cross validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

“…Five types of NNs were implemented in [206] to anticipate the magnetic levitation and guide forces based on 3720 data. These five types of NNs are RBF NN, DNN, CNN, RNN, and 5)-( 8):…”

Artificial intelligence, machine learning, deep learning, and big data techniques for the advancements of superconducting technology: a road to smarter and intelligent superconductivity

“…Guiding force which has strong non-linearity is a key index to guarantee the lateral self-stability of HTS maglev, and is usually determined by numerous interdependent parameters. AI techniques could be used to predict guiding force more accurately and with less computational burden in real-time manner compared with traditional FE and polynomial fitting methods [17,18].…”

“…In [17], five different AI-based models utilizing ANN were developed to predict HTS maglev guiding force. The prediction efficiency of these models was compared based on thousands of collected data.…”

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

“…For example, in 2022, Liu et al applied a typical BPNN to predict the levitation force of the HTS maglev system, and the accuracy is high [34]. At the same time, Ke et al selected five different neural networks to predict the guiding force of HTS maglev, and some considerations for structure selection of neural network in HTS maglev system were also presented [35]. Mohammad Yazdani-Asram used data-driven methods to predict the normalized critical current values and stresses of twisted tapes at different temperatures and magnetic flux densities.…”