Dielectric elastomer (DE) is an electroactive polymer with the characteristics of high energy output, great flexibility, lightweight, mechanical compliance, and low cost, which are particularly suitable for DE energy generators. Energy harvesting efficiency is a key index to evaluate the performance of the energy generator, which depends on the structural configuration and the mechanical and dielectric properties of the DE material. This paper proposes a fractional viscoelastic polarization(FVP) model by combining the fractional viscoelasticity model and the polarization-based lumped parameter model. A dynamical model of a cone dielectric energy generator (CDEG) considering stretch-dependent electrostriction and nonlinear viscoelasticity is established. Additionally, a deep neural network (DNN) model is developed to explore the relationships between various parameters and the output energy of CDEGs to efficiently and accurately predict the energy output of CDEGs. Based on the DNN model, optimal parameter designs for CDEGs are obtained by using non-dominated sorting genetic algorithm II (NSGA-II). The experiments verified that the FVP model predicts accurately the output energy of CDEG and the established optimal design framework can accurately provide the optimal design parameters of CDEG, which offers deep insights for the design and fabrication of a high-efficiency dielectric energy generator.

The present study proposes a modified random sequential absorption (RSA) algorithm to generate a representative volume element (RVE) model for predicting the elastic properties of discontinuous curved fiber reinforced composites (DCFRCs) with varying fiber waviness functions and orientations. A small-move method was proposed to modify the traditional RSA algorithm. In comparison with the original RSA algorithm, the generation efficiency of the proposed modified RSA algorithm increased by over 40%, and the achievable maximum fiber volume fraction could reach up to 15% with a fiber aspect ratio of 15. The generated RVE model was utilized in conducting finite element analysis to investigate the effect of fiber waviness and wavy functions on the elastic properties of DCFRCs. Finally, a modified rule-of-mixture was proposed to predict the elastic properties of DCFRCs with various fiber orientations. The results indicated that the elastic properties predicted by the modified rule-of-mixture were in good agreement with those obtained from the RVE model, thereby demonstrating its effectiveness.

We examine the electromechanical field and charge redistribution within a flexoelectric semiconductor (FS) nanobeam, accounting for bending, fundamental thickness-shear, and antisymmetric thickness-stretch deformations. The coupled governing equations include microstructure, flexoelectric, and semiconductor effects, highlighting the interplay between mechanical displacement, electric potential, and charge carriers. For applications in flexoelectronic devices, the static bending of a simply supported FS beam induced by uniform pressure and wave propagation in an unbounded FS beam are analytically addressed using the derived framework. The effects of antisymmetric thickness-stretch on mechanical displacements and electron concentration perturbation, as well as size dependence of microstructure and flexoelectric effects, are identified. An interesting finding reveals that wave frequencies of the antisymmetric thickness-stretch mode, as anticipated by the proposed model, are larger compared to those of the model neglecting flexoelectric and semiconductor effects. For the first time, the cutoff frequency of antisymmetric thickness-stretch impacted by the two features is explained mathematically. These findings are beneficial for enhancing the performance of flexoelectronic sensors and electroacoustic devices.

To avoid collisions between a suspended object, cables, towing robots, and obstacles in the environment in a multi-robot suspension system, obstacle avoidance planning was studied based on a collaborative optimization method for force and position. Based on the analysis of the kinematics and dynamics of the system, the inverse kinematics and inverse dynamics of the system are solved using the least variance method. The obstacle avoidance planning is performed in the solved collisionfree feasible space using the stable dung beetle optimization (SDBO) algorithm, which ensures that the suspended object can move stably to the target point in the workspace. The optimal obstacle avoidance trajectory of the multi-robot suspension system can be accurately determined by using the collaborative optimization method for force and position to plan the towing robot and the cable. Finally, the correctness of the obstacle avoidance planning method is verified by simulations. By taking a special scenario, the remarkable findings reveal that the SDBO algorithm outperforms the dung beetle optimization algorithm by reducing the length of the planned trajectory of the suspended object by 14.51% and the height by 79.88%, and reducing the minimum fitness by 95.84% and the average fitness by 94.77%. The results can help the multi-robot suspension system to perform various towing tasks safely and stably, and extend the related planning and control theory.

Osteoarthritis is one of the most common joint diseases, leading to joint pain, dysfunction, and a reduced quality of life for patients. Therefore, it is particularly important to explore more effective prevention, treatment and management methods to relieve patients’ pain and enhance their quality of life. Among physical therapies, pulsed electrical stimulation (PES) is considered to be a promising treatment method due to its high safety and ease-of-use features. PES provides a non-invasive, safe and effective option for patients. However, there are fewer studies on the biomechanical changes of PES in periarticular tissues, and its effects on the biological behavior of chondrocytes remain unknown. This study investigated the effects of PES on the biomechanical properties of osteoarthritic joints and the biological behavior of chondrocytes. The results showed that PES with an intensity of 10 mA and a frequency of 4 Hz increased the cross-sectional area of muscle fibers, prevented muscle atrophy and loss of function, and restored the mechanical properties of muscle tissue. PES also effectively increases the resistivity of knee osteoarthritis cartilage tissue, as well as the elastic modulus of cartilage, which can enhance the biomechanical characteristics of cartilage tissue. PES also promoted the metabolic activity of chondrocytes and increased cartilage matrix synthesis, thereby improving the overall structure and mechanical properties of cartilage tissue. Additionally, cellular experiments showed that 5 consecutive days of 800 mV PES significantly increased the expression level of Piezo1 gene in chondrocytes. At the same time, the expression of type II collagen and transforming growth factor beta increased, while the expression of matrix metallopeptidase 13 decreased. These changes favored the promotion of cartilage matrix synthesis. This has a positive effect on protecting and improving joint health and reducing the impact of osteoarthritis, and is important for understanding the mechanism of action of PES on chondrocytes and the development of related therapeutic strategies.

Non-Schmid (NS) effects in body-centered cubic (BCC) single-phase metals have received special attention in recent years. However, a deep understanding of these effects in the BCC phase of dual-phase (DP) steels has not yet been reached. This study explores the NS effects in ferrite-martensite DP steels, where the ferrite phase has a BCC crystallographic structure and exhibits NS effects. The influences of NS stress components on the mechanical response of DP steels are studied, including stress/strain partitioning, plastic flow, and yield surface. To this end, the mechanical behavior of the two phases is described by dislocation density-based crystal plasticity constitutive models, with the NS effect only incorporated into the ferrite phase modeling. The NS stress contribution is revealed for two types of microstructures commonly observed in DP steels: equiaxed phases with random grain orientations, and elongated phases with preferred grain orientations. Our results show that, in the case of a microstructure with equiaxed phases, the normal NS stress components play significant roles in tension-compression asymmetry. By contrast, in microstructures with elongated phases, a combined influence of crystallographic texture and NS effect is evident. These findings advance our knowledge of the intricate interplay between microstructural features and NS effects and help to elucidate the mechanisms underlying anisotropic-asymmetric plastic behavior of DP steels.

A multiscale nonlocal continuum model is proposed to describe the superelastic deformation of gradient nano-grained NiTi shape memory alloys (SMAs). At the mesoscopic scale, the polycrystalline aggregate is regarded as a composite, i.e., the grain-interior (GI) phase is assumed to be a cuboidal inclusion embedded in a matrix of grain-boundary (GB) phase. An intrinsic energetic length and a gradient energy are introduced into the Helmholtz free energy of the GI phase. The criterion of martensite transformation (MT) is derived based on the principle of virtual power and second law of thermodynamics. The hindering effect of GB on MT in GI phase is addressed. By deriving the analytical solution of the proposed model and introducing a scale transition rule, the overall and local stress-strain responses of the specimen at the macroscopic scale are obtained. The prediction capability of the proposed model is verified by comparing the analytical solution with the experiment. The influences of the distribution form for the grain size (GS) on the superelastic deformation of gradient nano-grained NiTi SMAs are further predicted and discussed. The analytical form and low computational cost of the proposed model make it an appropriate theoretical tool to design the gradient nano-grained SMAs with desired mechanical property.