Dynamic interactions of an integrated vehicle–electromagnetic energy harvester–tire system subject to uneven road excitations

An investigation is undertaken of an integrated mechanical-electromagnetic coupling system consisting of a rigid vehicle with heave, roll, and pitch motions, four electromagnetic energy harvesters and four tires subject to uneven road excitations in order to improve the passengers' riding comfort and harvest the lost engine energy due to uneven roads. Following the derived mathematical formulations and the proposed solution approaches, the numerical simulations of this interaction system subject to a continuous sinusoidal road excitation and a single ramp impact are completed. The simulation results are presented as the dynamic response curves in the forms of the frequency spectrum and the time history, which reveals the complex interaction characteristics of the system for vibration reductions and energy harvesting performance. It has addressed the coupling effects on the dynamic characteristics of the integrated system caused by: (1) the natural modes and frequencies of the vehicle; (2) the vehicle rolling and pitching motions; (3) different road excitations on four wheels; (4) the time delay of a road ramp to impact both the front and rear wheels, etc., which cannot be tackled by an often used quarter vehicle model. The guidelines for engineering applications are given. The developed coupling model and the revealed concept provide a means with analysis idea to investigate the details of four energy harvester motions for electromagnetic suspension designs in order to replace the current passive vehicle isolators and to harvest the lost engine energy. Potential further research directions are suggested for readers to consider in the future.

Hydrodynamic Coefficients for a 3-D Uniform Flexible Barge UsingWeakly Compressible Smoothed Particle Hydrodynamics

The numerical modelling of the interactions between water waves and floating structures is significant for different areas of the marine sector, especially seakeeping and prediction of wave-induced loads. Seakeeping analysis involving severe flow fluctuations is still quite challenging even for the conventional RANS method. Particle method has been viewed as alternative for such analysis especially those involving deformable boundary, wave breaking and fluid fragmentation around hull shapes. In this paper, the weakly compressible smoothed particle hydrodynamics(WCSPH), a fully Lagrangian particle method, is applied to simulate the symmetric radiation problem for a stationary barge treated as a flexible body. This is carried out by imposing prescribed forced simple harmonic oscillations in heave, pitch and the two-and three-node distortion modes. The resultant,radiation force predictions, namely added mass and fluid damping coefficients, are compared with results from 3-D potential flow boundary element method and 3-D RANS CFD predictions, in order to verify the adopted modelling techniques for WCSPH.WCSPH were found to be in agreement with most results and could predict the fluid actions equally well in most cases.

Characterization of Reinforced Carbon Composites with Full Field Measurements: Long Gauge Length Compressive Apparatus

A new compressive testing apparatus is developed and used in this research. It has long gauge length to allow digital image correlation monitoring and anti buckling guides to prevent buckling. It allows the optical recording of strains and displacements. The novel setup is used to study the compressive response of tufted and untufted Carbon non crimp fabric composites with full field measurements. Experimental results show that the specimens are not bending in the apparatus under compression. Results also show reduced strain concentrations and a large strain field that provides a good environment for material compressive stiffness characterization. The test proves particularly successful for bias direction layup of [+45/-45] for which large damage mechanism occurs. However for [0/90] specimens a scatter in compressive ultimate strength was noticed which is due to the difficulty to prepare specimens with best minute accurate geometry. The compressive apparatus has shown to be a good alternative to existing setups and to provide significantly more information as well as having the possibility to be used in dynamics with a drop tower.

Deep reinforcement learning in fluid mechanics:A promising method for both active flow control and shape optimization

In recent years,artificial neural networks(ANNs)and deep learning have become increasingly popular across a wide range of scientific and technical fields,including fluid mechanics.While it will take time to fully grasp the potentialities as well as the limitations of these methods,evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known.This is particularly true in fluid mechanics,where problems involving optimal control and optimal design are involved.Indeed,such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity,non convexity,and high dimensionality they involve.By contrast,deep reinforcement learning(DRL),a method of optimization based on teaching empirical strategies to an ANN through trial and error,is well adapted to solving such problems.In this short review,we offer an insight into the current state of the art of the use of DRL within fluid mechanics,focusing on control and optimal design problems.