This study presents a high-performance valve-less piezoelectric micropump with crescent-shaped structure. This micropump was designed by finite element analysis (FEA) and fabricated through 3D printing technology. Different configurations of the micropump with different distances from inlet to the crescent-shaped structure ( δ = 0.3, 0.6, 0.9, 1.2, and 1.5 mm) and numbers of crescent-shaped structures (from one up to four stages) were tested. The sound pressure level (SPL) characteristics were experimentally investigated and evaluated at 0.6, 1.5, 2.0, 2.5, and 3.0 mm. The results gave a maximum flow rate of 219.4 ml min ⁻¹ at 0.6 mm under 220 V and 45 Hz. Moreover, the flow rate for the quadruple crescent-shaped structure was 286 ml min ⁻¹ at 220 V and 82 Hz. Besides, the difference of the SPL was 3.2 dB at 0.6 m, making this pump better than others; the difference was kept at about 18 dB when the gap exceeded 2.0 mm. The results suggest that the structure with the best configuration is that with δ = 0.6 mm and four stages, and the SPL fluctuation is smaller as the output performance becomes better. The study offers some valuable insights into improving its performance and practical application.

In this paper, the mean first-passage time of a delayed tumor cell growth system driven by colored cross-correlated noises is investigated. Based on the Novikov theorem and the method of probability density approximation, the stationary probability density function is obtained. Then applying the fastest descent method, the analytical expression of the mean first-passage time is derived. Finally, effects of different kinds of delays and noise parameters on the mean first-passage time are discussed thoroughly. The results show that the time delay included in the random force, additive noise intensity and multiplicative noise intensity play a positive role in the disappearance of tumor cells. However, the time delay included in the determined force and the correlation time lead to the increase of tumor cells.

Study of the engraving process is significant for interior ballistics. Due to the high transient and strong nonlinear characteristics of the engraving, research on the engraving mechanism is challenging. In this paper, based on the one-dimensional two-phase flow model of interior ballistics coupled with explicit dynamics software, finite element simulation of the engraving is carried out to obtain the changes of parameters during the engraving process, and the dynamic characteristics of the projectile engraving are discussed. By comparing the simulation results with the experimental data, the accuracy of the coupling method in this paper is verified. The relationship between the structural parameters of the sealing band and the extrusion resistance is revealed, and the influence of the extrusion resistance on the dynamic characteristics of the projectile is demonstrated. The simulation results can provide an essential reference for selecting a reasonable sealing band structure.

Environmental noise can lead to complex stochastic dynamical behaviors in nonlinear systems. In this paper, a Lorenz system with the parameter region with two stable fixed points and a chaotic saddle subject to white Gaussian noise is investigated as an example. Noise‐induced phenomena, such as noise‐induced quasi‐cycle, three‐state intermittency, and chaos, are observed. In the intermittency process, the optimal path used to describe the transition mechanism is calculated and confirmed to pass through an unstable periodic orbit, a chaotic saddle, a saddle point, and a heteroclinic trajectory in an orderly sequence using generalized cell mapping with a digraph method constructively. The corresponding optimal fluctuation forces are delineated to uncover the effects of noise during the transition process. Then the process will switch frequently between the attractors and the chaotic saddle as noise intensity increased further, that is, noise induced chaos emerging. A threshold noise intensity is defined by stochastic sensitivity analysis when a confidence ellipsoid is tangent to the stable manifold of the periodic orbit, which agrees with the simulation results. It is finally reported that these results and methods can be generalized to analyze the stochastic dynamics of other nonlinear mechanical systems with similar structures.

The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, which depend on the prior knowledge of the dynamical models. In this work, we propose a novel data-driven framework to discover the hybrid dynamical systems from time series data, without any prior knowledge required of the systems. More specifically, we devise a dual-loop algorithm to peel off the data subject to each subsystem of the hybrid dynamical system. Then, we approximate the subsystems by iteratively training several residual networks and estimate the transition rules by training a fully connected neural network. Several prototypical examples are presented to demonstrate the effectiveness and accuracy of our method for hybrid models with various dimensions and structures. This method appears to be an effective tool for learning the evolutionary governing laws of hybrid dynamical systems from available data sets with wide applications.