Adiabatic Temperature Change of Gadolinium Modeling using Artificial Neural Network
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Abstract
An adiabatic temperature change of magnetocaloric material is an important parameter required in an active magnetic regenerator modeling. In this study, the adiabatic temperature change of Gadolinium was modeled by using artificial neural network. The adiabatic temperature changes were found at different magnetic inductions and magnetic material temperatures by means of WDS (Weiss-Debye-Sommerfeld) method. These data were applied to train a multilayer neural network with backpropagation algorithm. Artificial neural network with one hidden layer was chosen and the number of neurons was varied in training process until its mean square error (MSE) is lower than 10-6. From the training result, the optimum number of neurons in the hidden layer is 16. Untrained data were used to test the optimum structure. It is found that MSE of testing is 4.4610-5. The weights and biases obtained from the optimum structure were used to model the adiabatic temperature change. Finally, an example code for the adiabatic temperature change calculation based on the weights and biases was presented as a guide for application.
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