Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA new data assimilation method of recovering turbulent flow field at high-Reynolds numbers for turbulence machine learning
208
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
This paper proposes a new data assimilation method for recovering high fidelity turbulent flow field around airfoil at high Reynolds numbers based on experimental data, which is called Proper Orthogonal Decomposition Inversion (POD-Inversion) data assimilation method. Aiming at the flows including shock wave discontinuities or separated flows at high angle of attack, the proposed method can reconstruct high-fidelity turbulent flow field combining with experimental distributed force coefficients. We firstly perform the POD analysis to the turbulent eddy viscosity fields computed by SA model and obtain the base POD modes. Then optimized the POD coefficients by global optimization algorithm coupling with the Navier-Stokes equations solver. The high-fidelity turbulent flied are recovered by several main modes, which can dramatically reduce the dimensions of the system. The effectiveness of the method is verified by the cases of transonic flow around the RAE2822 airfoil at high Reynolds numbers and the separated flow at high angles of attack. The results demonstrate that the proposed assimilation method can recover the turbulent flow field which optimally match the experimental data, and significantly reduce the error of pressure coefficients. The proposed data assimilation method can offer high-fidelity field data for turbulent model based on machine learning.
rate research
Read More
The elementary structures of turbulence, i.e., vortex tubes, are studied using velocity data obtained in laboratory experiments for boundary layers and duct flows at microscale Reynolds numbers 332-1934. While past experimental studies focused on intense vortex tubes, the present study focuses on all vortex tubes with various intensities. We obtain the mean velocity profile. The radius scales with the Kolmogorov length. The circulation velocity scales with the Kolmogorov velocity, in contrast to the case of intense vortex tubes alone where the circulation velocity scales with the rms velocity fluctuation. Since these scaling laws are independent of the configuration for turbulence production, they appear to be universal at high Reynolds numbers.
A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence to large-scale forcing, is proposed and evaluated. The technique overcomes some of the cost limitations of turbulence simulation, since the main flow is treated with a coarse-grid LES, with the equivalent of wall functions supplied by the near-wall sampled QDNS. Two cases are tested, at friction Reynolds number Re$_tau$=4200 and 20,000. The total grid node count for the first case is less than half a million and less than two million for the second case, with the calculations only requiring a desktop computer. A good agreement with published DNS is found at Re$_tau$=4200, both in terms of the mean velocity profile and the streamwise velocity fluctuation statistics, which correctly show a substantial increase in near-wall turbulence levels due to a modulation of near-wall streaks by large-scale structures. The trend continues at Re$_tau$=20,000, in agreement with experiment, which represents one of the major achievements of the new approach. A number of detailed aspects of the model, including numerical resolution, LES-QDNS coupling strategy and sub-grid model are explored. A low level of grid sensitivity is demonstrated for both the QDNS and LES aspects. Since the method does not assume a law of the wall, it can in principle be applied to flows that are out of equilibrium.
Bio-inspired oscillatory foil propulsion has the ability to traverse various propulsive modes by dynamically changing the foils heave and pitch kinematics. This research characterizes the propulsion properties and wake dynamics of a symmetric oscillating foil, specifically targeting the high Reynolds number operation of small to medium surface vessels whose propulsive specifications have a broad range of loads and speeds. An unsteady Reynolds-averaged Navier-Stokes (URANS) solver with a k-$omega$ SST turbulence model is used to sweep through pitch amplitude and frequency at two heave amplitudes of $h_0/c=1$ and $h_0/c=2$ at $Re=10^6$. At $h_0/c=2$, the maximum thrust coefficient is $C_T=8.2$ due to the large intercepted flow area of the foil, whereas at a decreased Strouhal number the thrust coefficient decreases and the maximum propulsive efficiency reaches 75%. Results illustrate the kinematics required to transition between the high-efficiency and high-thrust regimes at high Reynolds number and the resulting changes to the vortex wake structure. The unsteady vortex dynamics throughout the heave-pitch cycle strongly influence the characterization of thrust and propulsive efficiency, and are classified into flow regimes based on performance and vortex structure.
In this video, we present the dynamics of an array of falling particles at intermediate Reynolds numbers. The film shows the vorticity plots of 3, 4, 7, 16 falling particles at $Re = 200$. We highlight the effect of parity on the falling configuration of the array. In steady state, an initially uniformly spaced array forms a convex shape when $n=3$, i.e the middle particle leads, but forms a concave shape when $n = 4$. For larger odd numbers of particles, the final state consists of a mixture of concave and convex shapes. For larger even numbers of particles, the steady state remains a concave shape. Below a threshold of initial particle spacing, particles cluster in groups of 2 to 3.
Turbulence modeling is a classical approach to address the multiscale nature of fluid turbulence. Instead of resolving all scales of motion, which is currently mathematically and numerically intractable, reduced models that capture the large-scale behavior are derived. One of the most popular reduced models is the Reynolds averaged Navier-Stokes (RANS) equations. The goal is to solve the RANS equations for the mean velocity and pressure field. However, the RANS equations contain a term called the Reynolds stress tensor, which is not known in terms of the mean velocity field. Many RANS turbulence models have been proposed to model the Reynolds stress tensor in terms of the mean velocity field, but are usually not suitably general for all flow fields of interest. Data-driven turbulence models have recently garnered considerable attention and have been rapidly developed. In a seminal work, Ling et al (2016) developed the tensor basis neural network (TBNN), which was used to learn a general Galilean invariant model for the Reynolds stress tensor. The TBNN was applied to a variety of flow fields with encouraging results. In the present study, the TBNN is applied to the turbulent channel flow. Its performance is compared with classical turbulence models as well as a neural network model that does not preserve Galilean invariance. A sensitivity study on the TBNN reveals that the network attempts to adjust to the dataset, but is limited by the mathematical form that guarantees Galilean invariance.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
