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 () during M-eC cooling cycles. The influences of magnetic field, geometrical dimension, and ambient temperature on 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 . It is found that a large 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).

中文翻译：

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多铁复合材料中低场磁弹热效应的相场模拟和机器学习

在低外场下实现明显的弹热效应对于固态冷却技术至关重要。在这里，提出了一种将马氏体相变与力学、传热和磁致伸缩行为耦合的非等温相场模型（PFM）来模拟多铁复合材料（例如，磁致伸缩形状记忆合金（MEA-SMA）复合材料）。在 PFM 中，采用非线性本构双曲正切模型来模拟 MEA 的宏观磁致伸缩行为，并采用与相变耦合的传热来计算 M-eC 冷却循环期间的绝热温度变化 ()。综合研究了磁场、几何尺寸和环境温度对其的影响。在 PFM 模拟的数据库上进一步进行机器学习 (ML)，以加速 MEA-SMA 复合材料的预测和设计，并改进了 .研究发现，通过优化复合材料的几何尺寸，可以在0.15~0.38 T的超低磁场下实现10~14 K的大工作温度窗口和30 K的宽工作温度窗口。目前结合 PFM 和 ML 来评估 M-eCE 的工作为 M-eC 冷却装置的优化提供了理论框架，并且还可能扩展到其他多热效应（例如，电弹热效应）。