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A scalar mixing layer in fractal grid turbulence is simulated by the implicit large eddy simulation (ILES) using low-pass filtering as an implicit subgrid-scale model. The square-type fractal grid with three fractal iterations is used for generating turbulence. The streamwise evolutions of the streamwise velocity statistics obtained in the ILES are in good agreement with the experimental results. The ILES results are used for investigating the development of the scalar mixing layer behind the fractal grid. The results show that the vertical development of the scalar mixing layer strongly depends on the spanwise location. Near the fractal grid, the scalar mixing layer rapidly develops just behind the largest grid bars owing to the vertical turbulent transport. The scalar mixing layer near the fractal grid also develops outside the largest grid bars because the scalar is transported between the outside and back of the largest grid bars by the spanwise turbulent transport. In the downstream region, the scalar mixing layer develops more rapidly near the grid centerline by the vertical turbulent transport and by the spanwise one which transports the scalar between the back of the largest grid bars and both the centerline and outer edge of the fractal grid. Then, the mean scalar profile becomes close to be homogeneous in the spanwise direction.

Direct numerical simulations are performed to investigate the helical properties of single-square-grid-generated turbulence. The streamwise evolution of the probability density functions of the relative helicity density h reveals the existence of a transition from a quasi-two-dimensional state to a three-dimensional state. The correlations between the helicity and the enstrophy level as well as the dissipation level are examined. When conditioned on a high level of dissipation or enstrophy, in the energy decay region the velocity and vorticity vectors in both instantaneous and fluctuating fields become more aligned. However, this correlation does not hold in the production region. We also study the second-order structure function and reveal that a well-defined power-law can be found at a location quite close to the grid, where the turbulent flow is still in the transition state.

Obtaining real flow information is important in various fields, but is a difficult issue because measurement data are usually limited in time and space, and computational results usually do not represent the exact state of real flows. Problems inherent in the realization of numerical simulation of real-world flows include the difficulty in representing exact initial and boundary conditions and the difficulty in representing unstable flow characteristics. This article reviews studies dealing with these problems. First, an overview of basic flow measurement methodologies and measurement data interpolation/approximation techniques is presented. Then, studies on methods of integrating numerical simulation and measurement, namely, four-dimensional variational data assimilation (4D-Var), Kalman filters (KFs), state observers, etc are discussed. The first problem is properly solved by these integration methodologies. The second problem can be partially solved with 4D-Var in which only initial and boundary conditions are control parameters. If an appropriate control parameter capable of modifying the dynamical structure of the model is included in the formulation of 4D-Var, unstable modes are properly suppressed and the second problem is solved. The state observer and KFs also solve the second problem by modifying mathematical models to stabilize the unstable modes of the original dynamical system by applying feedback signals. These integration methodologies are now applied in simulation of real-world flows in a wide variety of research fields. Examples are presented for basic fluid dynamics and applications in meteorology, aerospace, medicine, etc.

We simulate fractal-generated turbulence (Hurst and Vassilicos 2007 Phys. Fluids 19 035103)) by means of a direct numerical simulation and address its fundamental characteristics. We examine whether the fractal-generated turbulence in the upstream region has a nature similar to that of a wake. We propose an equation for predicting peak values of the velocity fluctuation intensity and devise a method for formulating the functional form of the quantity of interest by focusing on the time scale of decaying turbulence, and we examine those forms for the turbulent kinetic energy and rms of pressure fluctuation through this method. By using the method, both of these functional forms are found to be power-law functions in the downstream region, even though these profiles follow exponential functions around these peaks. In addition, decay exponents of these quantities are estimated. The integral length scales of velocity fluctuations for transverse as well as streamwise directions are essentially constant in the downstream direction. Decaying turbulence having both these characteristics conflicts with decaying turbulence described by the theory predicting exponential decay. We discuss a factor causing the difference by focusing on the functional form of the transfer function of homogeneous, isotropic turbulence.

The purpose of this study is to investigate fundamental statistics of a spatially developing grid turbulence by means of direct numerical simulation based on the finite difference method. The Reynolds number based on the mesh size, M, is 2500, and streamwise computational domain size normalized by M is 112. The streamwise profiles of turbulent intensities show that the large-scale fluctuating velocity field is quasi-isotropic in the region x/M > 4, where x is the streamwise distance from the grid. The derivative skewness of streamwise fluctuating velocity u is smaller than those of vertical and spanwise fluctuating velocities in the far downstream region. On the other hand, the derivative flatness of u, v, and w are almost identical in the far downstream region.

Turbulent mixing in regular and fractal grid turbulence is investigated in this work by using direct numerical simulation (DNS). Two types of turbulence-generating grids are used: a biplane square grid (regular grid) and a square fractal grid. The thickness ratios tr of the fractal grids are set at 5.0 and 8.5. The grid solidity is maintained at ?=0.36 for all the grids. The mesh Reynolds number, ReM=U0Meff/?, is set at 2500 for all cases, where U0 is the cross-sectionally averaged mean velocity; Meff, the effective mesh size; and ?, the kinematic viscosity. The grids are numerically generated using the immersed boundary method at 4Meff downstream of the entrance to the computational domain. The computational domain size normalized by Meff is 64?8?8 in the streamwise, vertical and spanwise directions for the regular grid and 64?16?16 for the fractal grids. Scalar mixing layers that initially have a step profile develop downstream of the grids. The Prandtl number is set at Pr=0.71 considering the heat transfer in air flow. Instantaneous temperature fields, instantaneous fluctuating temperature fields and fundamental turbulent statistics are presented. The results show that turbulent mixing is more strongly enhanced in fractal grid turbulence than in regular grid turbulence for the same ReM. In fractal grid turbulence, turbulent mixing is more strongly enhanced at tr=8.5 than at tr=5.0.

Turbulent mixing of passive scalar (heat) in grid-generated turbulence (GGT) is simulated by means of direct numerical simulation (DNS). A turbulence-generating grid, on which the velocity components are set to zero, is located downstream of the channel entrance, and it is numerically constructed on the staggered mesh arrangement using the immersed boundary method. The grid types constructed are: (a) square-mesh biplane grid, (b) square-mesh single-plane grid, (c) composite grid consisting of parallel square-bars and (d) fractal grid. Two fluids with different temperatures are provided separately in the upper and lower streams upstream of the turbulence-generating grids, generating the thermal mixing layer behind the grids. For the grid (a), simulations for two different Prandtl numbers of 0.71 and 7.1, corresponding to air and water flows, are conducted to investigate the effect of the Prandtl number. The results show that the typical grid turbulence and shearless mixing layer are generated downstream of the grids. The results of the scalar field show that a typical thermal mixing layer is generated as well, and the effects of the Prandtl numbers on turbulent heat transfer are observed.