Xiaodong Xia

In contrast to the conventional piezoelectric sensor, the CNT-based nanocomposite strain sensor (CNCSS) serves as a new category of high-performance strain sensor. The bottleneck for evaluating sensing capacities of CNCSS lies in the nonlinear electromechanical coupling mechanism and extra high computational costs of multi-scale simulation. In this paper, a novel multi-scale FE2 model and a machine learning accelerated FE-RNN computational model have both been developed on the strain sensing capacities of high-performance CNCSS. First, a multi-scale electromechanically coupled FE2 model is established for the CNCSS with a realistic configuration. The electromechanically coupled mechanism is illustrated by a loading-dependent tunneling model, which is highly dependent on the tunneling distance between the adjacent CNTs. The developed coupled FE2 model is able to predict the strain sensing performance of CNCSS with a macroscopic configuration while considering specific microstructural characteristics. Then, an electromechanically coupled recurrent neural network (RNN) surrogate model is utilized to accelerate the FE2 model in the microscopic scale. The developed FE-RNN model can accelerate the microscopic simulation of RVE for a continuous range of microstructural parameters. The predicted sensing characteristics via the developed FE2 model and accelerated FE-RNN model are both highly consistent with the experiment of CNT/epoxy nanocomposite strain sensor under a realistic configuration. Especially at the high strain loading, the predicted results reflect the sharp increase of sensing capacities for CNCSS. The accelerated FE-RNN approach is concluded to possess the advantage over the FE2 model on the structural analysis by significantly reducing the computational costs by 97%. The developed FE-RNN scheme is capable of providing rapid design instructions for the microstructure of high-performance strain sensors.

Piezoelectric/piezomagnetic nanoplates exhibit miniaturization, intelligence, and excellent mechanical-electromagnetic energy conversion capabilities, making them critical for the functionality of micro/nano-electromechanical systems (NEMS) and serving as the foundation for designing highly sensitive, compact NEMSs. The strain gradient is a key parameter influencing the flexural effect. As the size decreases and deformation increases, the strain gradient in piezoelectric/piezomagnetic nanoplates rises rapidly, enhancing both the flexoelectric and flexomagnetic effects. This paper proposes for the first time a double-layer rectangular nano-laminates model consisting of a piezoelectric layer and a piezomagnetic layer, which simultaneously takes into account flexoelectric effect, flexomagnetic effect and surface effect. Through force analysis, the equilibrium equation of the nano-layered structure model is obtained, and then the control equation of the model is further derived. By substituting different boundary conditions, analytical solutions for the deflection and natural frequencies of the piezoelectric/piezomagnetic nano-laminates under bending are derived. The influences of surface effects, flexoelectric effects, flexomagnetic effects, boundary conditions, aspect ratios, and thickness ratios on bending deflection and natural frequencies are discussed. The surface effects, flexoelectric effects, and flexomagnetic effects impact the mechanical properties of the nano-laminates to varying degrees, and their influence is constrained by displacement boundary conditions.