Self-assembled vesicle–colloid hybrid swimmers:Non-reciprocal strokes with reciprocal actuation

We consider a self-assembled hybrid system,composed of a bilayer vesicle to which a number of colloids are adhered.Based on known results of membrane curvature elasticity,we predict that,for sufficiently deflated prolate vesicles,the colloids can self-assemble into a ring at a finite distance away from the vesicle equator,thus breaking the up–down symmetry in the system.Because the relative variation of the position of the colloidal ring along the vesicle endows the system with an effective elasticity,periodic cycles of inflation and deflation can lead to non-reciprocal shape changes of the vesicle–colloid hybrid,allowing it to swim in a low Reynolds number environment under reciprocal actuation.We design several actuation protocols that allow control over the swimming direction.

Numerical simulation of condensation shock in partial cavitating flow on a hydrofoil

We present a numerical simulation of the so-called condensation shock on a NACA 0015 hydrofoil with a finite mass transfer model for the first time.Most recently,condensation shock was indentified in the experimental measurement as a mechanism for partial cavity shedding in the flow past a hydrofoil.Compressible solvers,which were adopted in the previous simulations to study such phenomenon numerically,are extremely time consuming due to the limitation of acoustic Courant number.In this work,we consider a finite mass transfer model for cavitation flow with slightly modification.Our numerical results show that the finite mass transfer model can be successfully applied for calculating the condensation shock in the flow past a hydrofoil.The dynamics of the condensation shock on the hydrofoil is also discussed.The model is proved to be useful for further understanding of the underlying phyiscs of such flow.

Applying physics informed neural network for flow data assimilation

Data assimilation(DA)refers to methodologies which combine data and underlying governing equations to provide an estimation of a complex system.Physics informed neural network(PINN)provides an innovative machine learning technique for solving and discovering the physics in nature.By encoding general nonlinear partial differential equations,which govern different physical systems such as fluid flows,to the deep neural network,PINN can be used as a tool for DA.Due to its nature that neither numerical differential operation nor temporal and spatial discretization is needed,PINN is straightforward for implementation and getting more and more attention in the academia.In this paper,we apply the PINN to several flow problems and explore its potential in fluid physics.Both the mesoscopic Boltzmann equation and the macroscopic Navier-Stokes are considered as physics constraints.We first introduce a discrete Boltzmann equation informed neural network and evaluate it with a one-dimensional propagating wave and two-dimensional lid-driven cavity flow.Such laminar cavity flow is also considered as an example in an incompressible Navier-Stokes equation informed neural network.With parameterized Navier-Stokes,two turbulent flows,one within a C-shape duct and one passing a bump,are studied and accompanying pressure field is obtained.Those examples end with a flow passing through a porous media.Applications in this paper show that PINN provides a new way for intelligent flow inference and identification,ranging from mesoscopic scale to macroscopic scale,and from laminar flow to turbulent flow.

Fabrication of freestanding Pt nanowires for use as thermal anemometry probes in turbulence measurements

We report a robust fabrication method for patterning freestanding Pt nanowires for use as thermal anemometry probes for small-scale turbulence measurements.Using e-beam lithography,high aspect ratio Pt nanowires(~300 nm width,~70µm length,~100 nm thickness)were patterned on the surface of oxidized silicon(Si)wafers.Combining wet etching processes with dry etching processes,these Pt nanowires were successfully released,rendering them freestanding between two silicon dioxide(SiO2)beams supported on Si cantilevers.Moreover,the unique design of the bridge holding the device allowed gentle release of the device without damaging the Pt nanowires.The total fabrication time was minimized by restricting the use of e-beam lithography to the patterning of the Pt nanowires,while standard photolithography was employed for other parts of the devices.We demonstrate that the fabricated sensors are suitable for turbulence measurements when operated in constant-current mode.A robust calibration between the output voltage and the fluid velocity was established over the velocity range from 0.5 to 5 m s−1 in a SF6 atmosphere at a pressure of 2 bar and a temperature of 21°C.The sensing signal from the nanowires showed negligible drift over a period of several hours.Moreover,we confirmed that the nanowires can withstand high dynamic pressures by testing them in air at room temperature for velocities up to 55 m s−1.

Compressible effect on the cavitating flow:A numeric study

To understand the effect of the compressibility on the cavitating flow, a compressible, multiphase, single component Reynolds averaged Navier-Stokes（RANS） solver is used to study the cavitating flow on a wedge in the present work. A barotropic equation of status is used. A non-linear model for compressibility in the mixture is adopted to capture the effect of the compressibility within the complex cavitation bubbly mixtures. An unsteady cavitation phenomenon is found in the numerical simulation. The numerical results of local compressibility and Mach number in the bubbly mixture are given. The mechanism responsible for the unsteady shedding of the bubbly mixture is discussed based on the numerical results.

Applying extended intrinsic mean spin tensor in evolution algorithm for RANS modelling of turbulent rotating channel flow

We present a machine learning based method for RANS modeling in the rotating frame of reference(RFR).The extended intrinsic mean spin tensor(EIMST)is adopted in a novel expansion of the evolution algorithm,named multi-dimensional gene expression programming(MGEP).Based on DNS data,a constrain free model for Reynolds stress is created by considering system rotating.The anisotropy behavior of Reynolds stress is considered in the model,which is then for the first time applied for modeling turbulent flow inside a rotating channel.Compared with the traditional RANS model,the new model can predict the non-symmetric profile of Reynolds stress.Meanwhile,the Taylor-Gortler vortex is captured in our simulations with the new model.It is demonstrated that the application of EIMST in MGEP can be successfully adopted for RANS modeling in the RFR.