Synthetic Turbulent Field Research Articles

Evaluation of Scale-dependent Kurtosis with HelioSwarm

Plasma turbulence involves complex, nonlinear interactions of electromagnetic fields and charged particles across multiple scales. Studying these phenomena in space plasmas, like the solar wind, is facilitated by the intrinsic scale separations and the availability of in situ spacecraft observations. However, the single-point or single-scale configurations of current spacecraft limit our understanding of many properties of the turbulent solar wind. To overcome these limitations, multipoint measurements spanning a range of characteristic scales are essential. This Letter prepares for the enhanced measurement capabilities of upcoming multispacecraft missions by demonstrating that higher-order statistics, specifically kurtosis, as a baseline for intermittency can be accurately measured. Using synthetic turbulent fields with adjustable intermittency levels, we achieve scale separations analogous to those in the solar wind and apply these techniques to the planned trajectories of the HelioSwarm mission. This approach promises significant advancements in our understanding of plasma turbulence.

Tailoring anisotropic synthetic inflow turbulence generator for wind turbine wake simulations

In computational fluid dynamics, defining precise boundary conditions, especially at inlets, is of great importance. Inlet flows typically exhibit natural turbulence, which is managed in various ways in scale-resolving simulations. Methods to establish turbulent inlet conditions are commonly created using natural transition, uncorrelated oscillations, periodic boundary conditions from auxiliary simulations, or synthetic turbulent fields. In this study, we explore a technique aimed at generating a divergence-free synthetic inflow turbulence with arbitrary anisotropy. The methodology is based on the conventional portrayal of turbulence as consisting of several coherent structures. While our approach adeptly emulates predefined statistical characteristics across different scales, its primary focus is on generating input parameters that impact the airflow within the wake of individual wind turbines and the atmospheric boundary layer within a wind farm. The results are compared with high-resolution velocity experimental measurements, large eddy simulations, and the digital filter-based inlet boundary condition already available in OpenFOAM. The findings demonstrate that the applied inflow generator outperforms the default OpenFOAM filter, particularly in the context of a single wind turbine.

Прямой тензорный метод фильтрации для генерации синтетических турбулентных полей скорости

A Direct Tensor Filter Method for synthetic turbulent field generation is proposed in this paper. The method is a generalization of the Direct Anisotropic Filter Method. The turbulent velocity fields built on the base of this method provides more properties corresponding to real physical turbulent fields in comparison to ones obtained with help of DAF method.

Three-Dimensional Direct Numerical Simulation of Turbulent Combustion of Hydrogen-Air Mixtures in a Synthetic Turbulent Field

In this paper, we propose a method of three-dimensional direct numerical simulation of turbulent flame propagation in gas-reacting mixtures under stationary, homogeneous, and isotropic turbulence conditions. This method is based on the detailed kinetic mechanism of combustion of a multicomponent mixture, does not contain any adjustable parameters, and is applied to the calculation of turbulent combustion of fuel-lean hydrogen–air mixtures. The calculated propagation speeds of the turbulent flame are in satisfactory agreement with the measured values. The calculated concentrations of the reaction active centers, such as OH, H, and O, in a turbulent flame are lower than those in a laminar flame, which also agrees with the experimental results.

A hyperbolic partial differential equation model for filtering turbulent flows

A two dimensional partial differential equation scheme that uses a second-order hyperbolic diffusion equation for image denoising is developed to a three dimensional model for filtering isotropic turbulence. The mathematical derivation of the model is introduced and a consistent finite difference numerical approximation scheme is proposed. The model is tested against the velocity fields of isotropic turbulence that are simulated by solving the lattice Boltzmann(LB) and the Navier–Stokes(NS) equations, respectively. The D3Q15 LB model and the Fourier spectral method are used to solve the LB equation and the Navier–Stokes vorticity equation at grid points of 2563, respectively. Also the filtering method with the choice of the same filtering parameters is applied against a synthetic turbulent field with the same number of points. The high rotation vortex identification method Q is used to visualize the total, coherent and incoherent fields in all cases of the study. Despite the different nature of the LB and NS direct numerical simulations and the synthetic turbulence, all input parameters for the hyperbolic filtering model are chosen the same in all cases. Comparisons between the LB and NS filtered energy spectra, coherent vortices, incoherent regions, skewness, flatness and the fourth order moments are also considered. The same features are calculated for the non-filtered and filtered synthetic turbulent fields. It is shown that the coherent part preserves all statistical features of turbulence. However, the isotropy is the only feature that has been preserved for the incoherent part and other statistical features are divergent in all cases of the study.

A systematic approach to the generation of synthetic turbulence using spectral methods

In this paper, a systematic discussion on the generation of synthetic turbulence using spectral methods is presented. After a brief introduction which reviews existing methodologies, the role played by the fulfilment of the divergence-free condition and Taylor assumption is investigated. Special attention is given to the case in which such random fields are applied as inflow condition for Computational Fluid Dynamics simulations. Subsequently, a new methodology of general applicability for the generation of synthetic turbulence is proposed. The strength of the new approach lies in its generality and conceptual simplicity. The obtained random field fulfils the divergence-free condition as well as Taylor assumption and it is approximately characterised by preselected spectral content in each spatial direction, so also providing direct control over all turbulence integral scales. Synthetic turbulent fields characterised by different spectral content are generated confirming the soundness of the proposed approach and showing its ability to target strongly anisotropic fields. Finally, some remarks on the generation of inhomogeneous fields, obtained by combination of homogeneous ones, are provided so generalising the proposed procedure.

Using Machine Learning to Analyze Factors Determining Cycle-to-Cycle Variation in a Spark-Ignited Gasoline Engine

In this work, we have applied a machine learning (ML) technique to provide insights into the causes of cycle-to-cycle variation (CCV) in a gasoline spark-ignited (SI) engine. The analysis was performed on a set of large eddy simulation (LES) calculations of a single cylinder of a four-cylinder port-fueled SI engine. The operating condition was stoichiometric, without significant knock, at a load of 16 bar brake mean effective pressure (BMEP), at an engine speed of 2500 rpm. A total of 123 cycles was simulated. Of these, 49 were run in sequence, while 74 were run in parallel. For the parallel approach, each cycle is initialized with its own synthetic turbulent field to generate CCV, as a part of another work performed by us. In this work, we used 3D information from all 123 cycles to compute flame topology and pre-ignition flow-field metrics. We then evaluated correlations between these metrics and peak cylinder pressure (PCP) employing an ML technique called random forest. The computed metrics form the inputs to the random forest model, and PCP is the output. This model captures the effect of all inputs, as well as interactions between them owing to its decision-tree structure. The goal of this work is to demonstrate (as a first step) that ML models can implicitly learn complex relationships between the pre-ignition flow-fields, the flame shapes, and the eventual outcome of the cycle (whether a cycle will be a high or a low cycle).

Optical determination of temperature and species concentration for homogeneous turbulent gas medium

In this study, we present an inverse calculation model to reconstruct time-averaged temperature, species concentration and their root mean square (rms) values from optical synthetic measurements for a homogeneous turbulent gaseous medium. The model is based on line-of-sight spectral transmissivity synthetic measurements, and time-averaged transmissivities and their rms values are successfully related to time-averaged temperatures, species concentrations and their rms values by considering interaction between turbulence and radiation (TRI). The turbulence length scale is also retrieved simultaneously with the turbulent scalars. In order to validate the model, a stochastic approach is used to generate synthetic turbulent fields (fluctuations of temperature and species concentration), and measured spectra are synthesized through calculations from HITEMP 2010 for different spectral bands of CO2, H2O and CO.

Confining turbulence in plasmas

The transport properties of electrostatic turbulence in plasmas are investigated by using test-particle simulations. In particular, the possibility of control of the transport in a given synthetic turbulent field, which evolves both in space and time, is explored. The fluctuations are built up taking into account observations of real turbulence in laboratory plasmas, that is, by allowing the field to contain structures lying on all dynamically interesting scales. It is shown that, inside a given region of space, the transport can be reduced when phases of the field are randomized, that is, when correlations of the field, which are responsible for the generation of structures, are annihilated. This means that a barrier for the transport can be achieved in a plasma even without actually suppressing turbulence. When the barrier is active, a flux of particles toward the center of the simulation box is present inside the region where the barrier has been located.

Stochastic approach to noise modeling for free turbulent flows

A new approach to noise modeling for free turbulent flows is presented. The equations governing the sound field are obtained in two steps. The first step consists of treating the mean and turbulent components of the flow while the acoustic perturbations are neglected. In the second step, a set of equations is derived for the acoustic variables. On the left-hand side of this system, one finds the linearized Euler equations, whereas the right-hand side exhibits source terms related to the turbulent fluctuations and their interactions with the mean flow. These terms are modeled using a stochastic description of the three-dimensional turbulent motion. This is achieved by synthesizing the velocity field at each point in space and for all times with a collection of discrete Fourier modes. The synthesized field posesses the suitable one- and two-point statistical moments and a reasonable temporal power spectral density. The linearized Euler equations including a stochastic description of noise sources are solved numerically with a scheme based on a fractional step treatment. Each one-dimensional problem is solved with a weak formulation. A set of calculations are carried out for a simple freejet. Comparisons between calculations and experiments indicate that a spatial filtering of the source terms is required to obtain the expected level in the far field. Realistic pressure signals, power spectral densities, and sound field patterns are obtained. It is indicated that the stochastic noise generation and radiation (SNGR) approach may be applied to more complex flows because the numerical codes used to calculate the mean flowfield and the wave propagation are not specific of jet configurations. The limitations of the present model lie in the statistical properties of the synthetic turbulent field and in the use of an axisymmetric modeling of the acoustic propagation.

Une approche stochastique pour la modélisation du bruit des écoulements turbulents

A new approach to noise modeling for free turbulent flows is presented. A set of equations is derived from the acoustic variables. In the left-hand side of this system, one finds the linearized Euler equations, whereas the right-hand side exibits source terms. These source terms are modeled in using a stochastic space-time description of the three-dimensionnal turbulent motion. The linearized Euler equations including a stochastic description of noise sources are solved numerically. The Stochastic and Noise Generation (SNGR) model is applied to a subsonic free jet. The weak interaction between the stationnary mean flow and the acoustic field is taken account. The limitations of the SNGR model lie in the statistical properties of the synthetic turbulent field and in the use of an axisymmetric modeling of the acoustic propagation