Assessment of force models on finite-sized particles at finite Reynolds numbers

Finite-sized inertial spherical particles are fully-resolved with the immersed boundary projection method(IBPM)in the turbulent open-channel flow by direct numerical simulation(DNS).The accuracy of the particle surface force models is investigated in comparison with the total force obtained via the fully-resolved method.The results show that the steady-state resistance only performs well in the streamwise direction,while the fluid acceleration force,the added-mass force,and the shear-induced Saffman lift can effectively compensate for the large-amplitude and high-frequency characteristics of the particle surface forces,especially for the wall-normal and spanwise components.The modified steady-state resistance with the correction effects of the acceleration and the fluid shear can better represent the overall forces imposed on the particles,and it is a preferable choice of the surface force model in the Lagrangian point-particle method.

Multi-objective optimization design of radar absorbing sandwich structure

By introducing a dimensionless parameter to couple the two objectives, weight and radar absorbing performance, into a single objective function, a multi-objective optimization procedure for the radar absorbing sandwich structure （RASS） with a cellular core is proposed. The optimization models considered are one-side clamped sandwich panels with four kinds of cores subject to uniformly distributed loads. The average specular reflectivity calculated with the transfer matrix method and the periodic moment method is utilized to characterize the radar absorbing performance, while the mechanical constraints include the facesheet yielding, core shearing, and facesheet wrinkling. The optimization analysis indicates that the sandwich structure with a two-dimensional （2D） composite lattice core filled with ultra-lightweight sponge may be a better candidate of lightweight RASS than those with cellular foam or hexagonal honeycomb cores. The 2D Kagome lattice is found to outperform the square lattice with respect to radar absorbing.

A novel random phase-shifting digital holographic microscopy method

This paper proposes a new method that reconstructs the information of specimen by using random phase shift step in digital holographic microscopy (DHM). The principles of the method are described and discussed in detail. In practical experiment, because the phase shifter is neither perfectly linear nor calibrated, digital holograms with inaccurate phase shift step are recorded by the charge-coupled device (CCD). The phase could be accurately reconstructed from the recorded digital holograms by using the random phase-shifting algorithm, which makes up for reconstructed phase error caused by ordinary phase-shifting algorithm. The phase aberration compensation is also discussed. In order to verify the flexibility of the proposed method, numerical simulation of random phase-shifting DHM was carried out. The simulation results illustrated that the presented method is effective when the phase shift step is unknown or random in DHM.

Swimming strategy of settling elongated micro-swimmers by reinforcement learning

Particular types of plankton in aquatic ecosystems can coordinate their motion depending on the local flow environment to reach regions conducive to their growth or reproduction.Investigating their swimming strategies with regard to the local environment is important to obtain in-depth understanding of their behavior in the aquatic environment.In the present research,to examine an impact of the shape and gravity on a swimming strategy,plankton is considered as settling swimming particles of ellipsoidal shape.The Q-learning approach is adopted to obtain swimming strategies for smart particles with a goal of efficiently moving upwards in a two-dimensional steady flow.Strategies obtained from reinforcement learning are compared to those of naive gyrotactic particles that are modeled considering the behavior of realistic plankton.It is found that the elongation of particles improves the performance of upward swimming by facilitating particles’resistance to the perturbation of vortex.In the case when the settling velocity is included,the strategy obtained by reinforcement learning has similar performance to that of the naive gyrotactic one,and they both align swimmers in upward direction.The similarity between the strategy obtained from machine learning and the biological gyrotactic strategy indicates the relationship between the aspherical shape and settling effect of realistic plankton and their gyrotactic feature.