SUMMARYDirect or large eddy simulation of a turbulent flow field is strongly influenced by its initial or inflow boundary condition. This paper presents a new stochastic approach to generate an artificial turbulent velocity field for initial or inflow boundary condition based on digital filtering. Each velocity component of the artificial turbulent velocity field is generated by linear combination of individual uncorrelated random fields. These uncorrelated random fields are obtained by filtering random white-noise fields. Using common elements in these linear combinations results in multi-correlation among different velocity components. The generated velocity field reproduces locally desired Reynolds stress components and integral length scales including cross-integral length scales. The method appears to be simple, flexible and more accurate in comparison with previously developed methods. The accuracy and performance of the method are demonstrated by numerical simulation of a homogeneous turbulent shear flow with high and low shear rates. To assess the accuracy and performance of the method, simulation results are compared with a reference simulation.

In this paper, the unsteady aerodynamic of an insect’s forward flight has been carried out with a novel approach. In order to fully utilize the available powerful solvers, an innovative intermediary MATLAB code has been written for the high-fidelity time-resolved multi-physics problems involving fluid flow and multi-body simulations. For simulating the insect’s flight, the FLUENT solver has been utilized to determine aerodynamic forces and moments of the wings and main body while ADAMS software has been employed to calculate translational and angular velocities. Overset grid technology accompanied with dynamic mesh method have been implemented for the movement of the insect. The code is responsible for the synchronization of the solvers at the end of each time step as well as the integration of the solutions. Three different simulations are done for two different insects’ geometries. For the first and second simulations, a simplified geometry of an insect is selected, due to the ease of manufacturing and testing. At first, all rotational and translational degrees of freedom are considered to be free. The motion path history shows the instability due to an inappropriate location of the center of gravity. Hence, in the second case, it is assumed that the insect’s main body is limited to the vertical motion. In the final simulation, a complicated model of a bee with exact geometry and wings kinematics extracts from the experimental data with the free translational degrees of freedom. According to the results, combining multiple software in which they can interact with each other at each time step, is the most accurate way for doing precise multi-physics simulations.

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Energy dynamics in elastoinertial turbulence is investigated by performing different direct numerical simulations of stationary, homogeneous isotropic turbulence for the range of Weissenberg numbers 0 ≤ Wi ≤ 9. Viscoelastic effects are described by the finite extensibility nonlinear elastic-Peterlin model. It is found that the presence of the polymer additives can nontrivially modify the kinetic energy dynamics by suppressing the rate of the kinetic energy transfer and altering the locality nature of this energy transfer. Spectral representation of the elastic field revealed that the elastic energy is also transferred locally through different elastic degrees of freedom via a dominantly forward energy cascade. Moreover, the elastic energy spectrum can display a power-law behavior, k−m, with the possibility of different scaling exponents depending on the Wi number. It is observed that the energy exchange between macro- and microstructures is a two-directional process: there is a dominant energy transfer from the solvent large-scale structures to the polymers alongside a weak energy transfer from polymers to the solvent small-scale structures. This energy exchange consists of three different fluxes. Two of these fluxes equally transfer a small fraction of the kinetic energy into the mean and fluctuating elastic fields. However, the main energy conversion takes place between fluctuating kinetic and elastic fields through a completely nonlocal energy transfer process.

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