Phase-field simulation and machine learning of low-field magneto-elastocaloric effect in a multiferroic composite.

Achieving appreciable elastocaloric effect under low external field is critical for solid-state cooling technology. Here, a non-isothermal Phase-Field Model (PFM) coupling martensitic transformation with mechanics, heat transfer and magnetostrictive behavior is proposed to simulate Magneto-elastoCaloric Effect (M-eCE) that is induced by magnetic field in a multiferroic composite (e.g., Magnetostrictive-Shape Memory Alloys (MEA-SMA) composite). In the PFM, a nonlinear constitutive hyperbolic tangent model is utilized to model the macroscopic magnetostrictive behavior of MEA, and the heat transfer coupled with phase transformation is employed to calculate the adiabatic temperature change (Δ T ad ) during M-eC cooling cycles. The influences of magnetic field, geometrical dimension, and ambient temperature on Δ T ad are comprehensively investigated. Machine Learning (ML) is further conducted on the database from PFM simulations to accelerate the prediction and design of MEA-SMA composite with an improved Δ T ad . It is found that a large Δ T ad of 10–14 K and a wide working temperature window of 30 K can be achieved under ultra-low magnetic field of 0.15–0.38 T by optimizing the composite's geometrical dimension. The present work combining PFM and ML for evaluating M-eCE provides a theoretical framework for the optimization of M-eC cooling devices, and is also potentially extended to other multicaloric effects (e.g., electro-elastocaloric effect). [Display omitted] • A non-isothermal phase-field model is proposed for magneto-elastocaloric effect (M-eCE). • Effects of magnetic field, geometric dimension, and ambient temperature on M-eCE are revealed. • A large temperature change of 10–14 K is achieved by a low magnetic field (0.15–0.38 T). • Machine learning is used to accelerate the prediction and optimization of M-eCE. [ABSTRACT FROM AUTHOR]