A macroscopic viscoelastic model of magnetorheological elastomer with different initial particle chain orientation angles based on fractional viscoelasticity

In this paper, a macroscopic viscoelastic modeling method for magnetorheological elastomer (MRE) based on fractional derivative model is presented to describe the dynamic viscoelastic properties of MRE with different initial particle chain orientation angles. The angle between the particle chain and the applied magnetic field is used as an indicator to describe the directionality of the particle chain. MRE samples with different initial inclination angles have been designed and fabricated. The dynamic viscoelastic properties of different MRE samples under shear working mode were measured using a parallel plate rheometer. The dynamic viscoelastic properties of MRE with different initial inclination angles are analyzed under the test conditions of different strain amplitude, frequency and magnetic flux density. The test results show that the initial inclination angle of the particle chain in the MRE has significant effect on the dynamic viscoelastic properties of the MRE. A polynomial function is used to describe the relationship between the initial particle chain orientation angle and the magneto-induced modulus of MRE. A phenomenological model of magneto-induced modulus is established based on the fractional derivative model. The model parameters are identified using the nonlinear least square method. The predicted values of the model are in good agreement with the experimental results, indicating that the model can well describe the dynamic viscoelastic properties of MRE.

Dynamic Pathfinding for Non-Player Character Follower on Game

Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.

Transient behavior of compressed magnetorheological brake excited by step currents

Transient behavior of a magnetorheological brake excited by step currents under compression-shear mode has been experimentally studied. The results show that the amplitude of the applied current had little effect on the rising time of transient torque, while the rising time was significantly affected by the rotational speed, the compressive speed and the compressive strain position. The falling time of transient torque was independent of the amplitude of the applied current, the compressive speed and the compressive strain position, and it was affected by the rotational speed. The falling time of the transient torque was much shorter than the rising time by a step current. The transient process of MR brake applied as a step current was different from a stable process pre-applied at constant current in different particle chain structure forming processes. In addition, the compressive processes applied in one step current and randomly on/off current were compared and experimentally verified: the particle chains in two processes both experienced the same evolutionary of transient torque. The results achieved in this study should be properly considered in the design and control of magnetorheological brake under compression-shear mode.

Piezoelectric Impact Energy Harvester Based on the Composite Spherical Particle Chain for Self-Powered Sensors

In this study, a novel piezoelectric energy harvester (PEH) based on the array composite spherical particle chain was constructed and explored in detail through simulation and experimental verification. The power test of the PEH based on array composite particle chains in the self-powered system was realized. Firstly, the model of PEH based on the composite spherical particle chain was constructed to theoretically realize the collection, transformation, and storage of impact energy, and the advantages of a composite particle chain in the field of piezoelectric energy harvesting were verified. Secondly, an experimental system was established to test the performance of the PEH, including the stability of the system under a continuous impact load, the power adjustment under different resistances, and the influence of the number of particle chains on the energy harvesting efficiency. Finally, a self-powered supply system was established with the PEH composed of three composite particle chains to realize the power supply of the microelectronic components. This paper presents a method of collecting impact energy based on particle chain structure, and lays an experimental foundation for the application of a composite particle chain in the field of piezoelectric energy harvesting.

Shape anisotropy-governed locomotion of surface microrollers on vessel-like microtopographies against physiological flows

Surface microrollers are promising microrobotic systems for controlled navigation in the circulatory system thanks to their fast speeds and decreased flow velocities at the vessel walls. While surface propulsion on the vessel walls helps minimize the effect of strong fluidic forces, three-dimensional (3D) surface microtopography, comparable to the size scale of a microrobot, due to cellular morphology and organization emerges as a major challenge. Here, we show that microroller shape anisotropy determines the surface locomotion capability of microrollers on vessel-like 3D surface microtopographies against physiological flow conditions. The isotropic (single, 8.5 µm diameter spherical particle) and anisotropic (doublet, two 4 µm diameter spherical particle chain) magnetic microrollers generated similar translational velocities on flat surfaces, whereas the isotropic microrollers failed to translate on most of the 3D-printed vessel-like microtopographies. The computational fluid dynamics analyses revealed larger flow fields generated around isotropic microrollers causing larger resistive forces near the microtopographies, in comparison to anisotropic microrollers, and impairing their translation. The superior surface-rolling capability of the anisotropic doublet microrollers on microtopographical surfaces against the fluid flow was further validated in a vessel-on-a-chip system mimicking microvasculature. The findings reported here establish the design principles of surface microrollers for robust locomotion on vessel walls against physiological flows.

Efficient Numerical Evaluation of Thermodynamic Quantities on Infinite (Semi-)classical Chains

This work presents an efficient numerical method to evaluate the free energy density and associated thermodynamic quantities of (quasi) one-dimensional classical systems, by combining the transfer operator approach with a numerical discretization of integral kernels using quadrature rules. For analytic kernels, the technique exhibits exponential convergence in the number of quadrature points. As demonstration, we apply the method to a classical particle chain, to the semiclassical nonlinear Schrödinger (NLS) equation and to a classical system on a cylindrical lattice. A comparison with molecular dynamics simulations performed for the NLS model shows very good agreement.

Discrete closed one-particle chain of contours

A discrete dynamical system called a closed chain of contours is considered. This system belongs to the class of the contour networks introduced by A. P. Buslaev. The closed chain contains N contours. There are 2m cells and a particle at each contour. There are two points on any contour called a node such that each of these points is common for this contour and one of two adjacent contours located on the left and right. The nodes divide each contour into equal parts. At any time t = 0,1, 2,... any particle moves onto a cell forward in the prescribed direction. If two particles simultaneously try to cross the same node, then only the particle of the left contour moves. The time function is introduced, that is equal to 0 or 1. This function is called the potential delay of the particle. For t ≥ m, the equality of this function to 1 implies that the time before the delay of the particle is not greater than m. The sum of all particles potential delays is called the potential of delays. From a certain moment, the states of the system are periodically repeated (limit cycles). Suppose the number of transitions of a particle on the limit cycle is equal to S(T) and the period is equal to T. The ratio S(T) to T is called the average velocity of the particle. The following theorem have been proved. 1) The delay potential is a non-increasing function of time, and the delay potential does not change in any limit cycle, and the value of the delay potential is equal to a non-negative integer and does not exceed 2N/3. 2) If the average velocity of particles is less than 1 for a limit cycle, then the period of the cycle (this period may not be minimal) is equal to (m + 1)N. 3) The average velocity of particles is equal to v = 1 - H/((m + 1)N), where H is the potential of delays on the limit cycle. 4) For any m, there exists a value N such that there exists a limit cycle with H > 0 and, therefore, v < 1.