Authors: Xingyu Zhou | Ziang Liu | Chao Yu | Guozheng Kang
Source: Mechanics of Materials (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

An artificial neural network-based thermo-mechanically coupled model for elastocaloric cooling of shape memory alloys Environment-friendly and high-efficiency elastocaloric solid-state cooling has emerged as a promising alternative to traditional vapor-compression refrigeration. Owing to the high entropy change during martensite transformation, NiTi shape memory alloy (SMA) is a competitive candidate for the core components of solid-state cooling systems. In the design of elastocaloric cooling devices manufactured by SMAs, a thermo-mechanically coupled constitutive model accounting for the internal heat production, heat exchange and their influences on the subsequent deformation is needed. However, the concepts in classical plasticity are often employed in the existing physics-based constitutive models, and a series of complex mathematical equations are involved. Such complexity brings inconvenience for the construction, implementation, and application of the constitutive models. To overcome these shortcomings, an artificial neural network (ANN)-based model is developed in this work to reproduce the thermo-mechanically coupled deformation of SMAs. In the proposed model, the components of strain tensor in principal space, ambient temperature and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features, and the components of the stress tensor in principal space and the internal heat source are set as the outputs. The proposed ANN-based model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine (UMAT). The thermal response and deformation of SMA under various loading paths and at different ambient temperatures are used to train the ANN model, which are generated from the existing physics-based constitutive model (numerical experiments). To validate the capability of the proposed model, the predicted thermal and mechanical responses of a SMA tube subjected to pure tension, pure torsion and tension-torsion combined multi-axial loading conditions at various loading rates are compared with the corresponding numerical experiments. The proposed model is further verified by predicting the elastocaloric cooling performances of typical SMA structures, including the helical springs and thin plates with holes. • An ANN-based model is developed to reproduce the thermo-mechanically coupled deformation of SMAs. • The proposed model is implemented into the finite element program by writing a user-defined material subroutine (UMAT). • The model is trained only by the isothermal deformation of a material point. • The thermo-mechanically coupled deformation and elastocaloric cooling performances of SMA structures are well predicted. Shape-memory alloy ; Constitutive equation ; Finite element method ; Cauchy elastic material ; Deformation (meteorology) ; Tangent ; Infinitesimal strain theory ; Viscoplasticity ; Tangent modulus ; Plasticity Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience ; Topology Optimization in Engineering