scholarly journals Lagrangian network analysis of turbulent mixing

2019 ◽  
Vol 865 ◽  
pp. 546-562 ◽  
Author(s):  
Giovanni Iacobello ◽  
Stefania Scarsoglio ◽  
J. G. M. Kuerten ◽  
Luca Ridolfi
Keyword(s):  
Complex Networks ◽  
Turbulent Flows ◽  
Turbulent Mixing ◽  
Turbulent Channel Flow ◽  
Spatial Proximity ◽  
Time Varying ◽  
Mean Flow ◽  
Lagrangian Analysis ◽  
Swarm Dynamics ◽  
Flow Features

A temporal complex network-based approach is proposed as a novel formulation to investigate turbulent mixing from a Lagrangian viewpoint. By exploiting a spatial proximity criterion, the dynamics of a set of fluid particles is geometrized into a time-varying weighted network. Specifically, a numerically solved turbulent channel flow is employed as an exemplifying case. We show that the time-varying network is able to clearly describe the particle swarm dynamics, in a parametrically robust and computationally inexpensive way. The network formalism enables us to straightforwardly identify transient and long-term flow regimes, the interplay between turbulent mixing and mean flow advection and the occurrence of proximity events among particles. Thanks to their versatility and ability to highlight significant flow features, complex networks represent a suitable tool for Lagrangian investigations of turbulent mixing. The present application of complex networks offers a powerful resource for Lagrangian analysis of turbulent flows, thus providing a further step in building bridges between turbulence research and network science.

Modeling the Transport of Fuel Mixture Perturbations and Entropy Waves in the Linearized Framework

2020 ◽  
Author(s):  
Thomas Ludwig Kaiser ◽  
Kilian Oberleithner
Keyword(s):  
Turbulent Flows ◽  
Channel Flow ◽  
Turbulent Mixing ◽  
Equivalence Ratio ◽  
Stokes Equations ◽  
Turbulent Channel Flow ◽  
Passive Scalar ◽  
Computational Domain ◽  
Reynolds Stresses ◽  
Mean Flow

Abstract In this paper a new method is introduced to model the transport of entropy waves and equivalence ratio fluctuations in turbulent flows. The model is based on the Navier-Stokes equations and includes a transport equation for a passive scalar, which may stand for entropy or equivalence ratio fluctuations. The equations are linearized around the mean turbulent fields, which serve as the input to the model in addition to a turbulent eddy viscosity, which accounts for turbulent diffusion of the perturbations. Based on these inputs, the framework is able to predict the linear response of the flow velocity and passive scalar to harmonic perturbations that are imposed at the boundaries of the computational domain. These in this study are fluctuations in the passive scalar and/or velocities at the inlet of a channel flow. The code is first validated against analytic results, showing very good agreement. Then the method is applied to predict the convection, mean flow dispersion and turbulent mixing of passive scalar fluctuations in a turbulent channel flow, which has been studied in previous work with Direct Numerical Simulations (DNS). Results show that our code reproduces the dynamics of coherent passive scalar transport in the DNS with very high accuracy and low numerical costs, when the DNS mean flow and Reynolds stresses are provided. Furthermore, we demonstrate that turbulent mixing has a significant effect on the transport of the passive scalar fluctuations. Finally, we apply the method to explain experimental observations of transport of equivalence ratio fluctuations in the mixing duct of a model burner.

Modeling the Transport of Fuel Mixture Perturbations and Entropy Waves in the Linearized Framework

2021 ◽  
Author(s):  
Thomas Ludwig Kaiser ◽  
Kilian Oberleithner
Keyword(s):  
Turbulent Flows ◽  
Channel Flow ◽  
Turbulent Mixing ◽  
Equivalence Ratio ◽  
Stokes Equations ◽  
Turbulent Channel Flow ◽  
Fuel Mixture ◽  
Passive Scalar ◽  
Computational Domain ◽  
Mean Flow

Abstract In this paper a new method is introduced to model the transport of entropy waves and equivalence ratio fluctuations in turbulent flows. The model is based on the Navier-Stokes equations and includes a transport equation for a passive scalar, which may stand for entropy or equivalence ratio fluctuations. The equations are linearized around the mean turbulent fields. These serve as the input to the model in addition to a turbulent eddy viscosity, which accounts for turbulent diffusion of the perturbations. Based on these inputs, the framework is able to predict the linear response of the flow velocity and passive scalar to harmonic perturbations that are imposed at the boundaries of the computational domain. These, in this study, are fluctuations in the passive scalar and/or velocities at the inlet of a channel flow. The code is first validated against analytic results, showing very good agreement. Then the method is applied to predict the convection, mean flow dispersion and turbulent mixing of passive scalar fluctuations in a turbulent channel flow, which has been studied in previous work with Direct Numerical Simulations (DNS). Results show that our code reproduces the dynamics of coherent passive scalar transport in the DNS with very high accuracy and low numerical costs. Furthermore, we demonstrate that turbulent mixing has a significant effect on the transport of the passive scalar fluctuations. Finally, we apply the method to explain experimental observations of transport of equivalence ratio fluctuations in the mixing duct of a model burner.

scholarly journals Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning

2018 ◽  
Author(s):  
Mustafa Usta ◽  
Ali Tosyali
Keyword(s):  
Machine Learning ◽  
Turbulent Flows ◽  
Three Dimensional ◽  
Turbulence Models ◽  
Machine Learning Algorithms ◽  
Mean Flow ◽  
Flow Properties ◽  
Significant Discrepancy ◽  
Model Based ◽  
Flow Features

This work determines the inaccuracy of using Reynolds averaged Navier Stokes (RANS) turbulence models in transition to turbulent flow regimes by predicting the model-based discrepancies between RANS and large eddy simulation (LES) models. Then, it incorporates the capabilities of machine learning algorithms to characterize the discrepancies which are defined as a function of mean flow properties of RANS simulations. First, three-dimensional CFD simulations using k-omega Shear Stress Transport (SST) and dynamic one-equation subgrid-scale models are conducted in a wall-bounded channel containing a cylinder for RANS and LES, respectively, to identify the turbulent kinetic energy discrepancy. Second, several flow features such as viscosity ratio, wall-distance based Reynolds number, and vortex stretching are calculated from the mean flow properties of RANS. Then the discrepancy is regressed on these flow features using the Random Forests regression algorithm. Finally, the discrepancy of the test flow is predicted using the trained algorithm. The results reveal that a significant discrepancy exists between RANS and LES simulations, and ML algorithm successfully predicts the increased model uncertainties caused by the employment of k-omega SST turbulence model for transitional fluid flows.

scholarly journals Numerical simulations of aggregate breakup in bounded and unbounded turbulent flows

2015 ◽  
Vol 766 ◽  
pp. 104-128 ◽  
Author(s):  
Matthaus U. Babler ◽  
Luca Biferale ◽  
Luca Brandt ◽  
Ulrike Feudel ◽  
Ksenia Guseva ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Turbulent Flows ◽  
Isotropic Turbulence ◽  
Turbulent Channel Flow ◽  
Stochastic Flow ◽  
Homogeneous Isotropic Turbulence ◽  
Mean Flow ◽  
Flow Properties ◽  
Developed Turbulence ◽  
Flow Configurations

AbstractBreakup of small aggregates in fully developed turbulence is studied by means of direct numerical simulations in a series of typical bounded and unbounded flow configurations, such as a turbulent channel flow, a developing boundary layer and homogeneous isotropic turbulence. The simplest criterion for breakup is adopted, whereby aggregate breakup occurs when the local hydrodynamic stress ${\it\sigma}\sim {\it\varepsilon}^{1/2}$, with ${\it\varepsilon}$ being the energy dissipation at the position of the aggregate, overcomes a given threshold ${\it\sigma}_{cr}$, which is characteristic for a given type of aggregate. Results show that the breakup rate decreases with increasing threshold. For small thresholds, it develops a scaling behaviour among the different flows. For high thresholds, the breakup rates show strong differences between the different flow configurations, highlighting the importance of non-universal mean-flow properties. To further assess the effects of flow inhomogeneity and turbulent fluctuations, the results are compared with those obtained in a smooth stochastic flow. Furthermore, we discuss the limitations and applicability of a set of independent proxies.

scholarly journals Voltage rise anemometry in turbulent flows applied to internal combustion engines

2021 ◽  
Vol 62 (6) ◽  
Author(s):  
Michael Wörner ◽  
Gregor Rottenkolber
Keyword(s):  
Turbulent Flows ◽  
Internal Combustion Engines ◽  
Clear Correlation ◽  
Combustion Engines ◽  
Mean Flow ◽  
Voltage Rise ◽  
Mathematical Correlation ◽  
Thermodynamic Conditions ◽  
Secondary Voltage ◽  
Cylinder Flow

AbstractIn an experimental procedure, a voltage rise anemometry is developed as a measurement technique for turbulent flows. Initially, fundamental investigations on a specific wind tunnel were performed for basic understanding and calibration purpose. Thus, a mathematical correlation is derived for calculating flow from measured secondary voltage of an ignition system under different thermodynamic conditions. Subsequently, the derived method was applied on a spark-ignited engine to measure in-cylinder flow. Therefore, no changes on combustion chamber were necessary avoiding any interferences of the examined flow field. Comparing four different engine configurations, a study of mean flow and turbulence was performed. Moreover, the results show a clear correlation between measured turbulence and analysed combustion parameters. Graphic abstract

Flow features and thermal stress evaluation in turbulent mixing flows

2021 ◽  
Vol 178 ◽  
pp. 121605
Author(s):  
Cenk Evrim ◽  
Xu Chu ◽  
Fabian E. Silber ◽  
Alexander Isaev ◽  
Stefan Weihe ◽  
...  
Keyword(s):  
Thermal Stress ◽  
Turbulent Mixing ◽  
Stress Evaluation ◽  
Flow Features

scholarly journals Measurements and Characterization of Turbulence in the Tip Region of an Axial Compressor Rotor

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Yuanchao Li ◽  
Huang Chen ◽  
Joseph Katz
Keyword(s):  
Strain Rate ◽  
Shear Layer ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Suction Side ◽  
Stress And Strain ◽  
Spatial And Temporal Variability ◽  
Mean Flow ◽  
The Mean ◽  
Stress And Strain Rate

Modeling of turbulent flows in axial turbomachines is challenging due to the high spatial and temporal variability in the distribution of the strain rate components, especially in the tip region of rotor blades. High-resolution stereo-particle image velocimetry (SPIV) measurements performed in a refractive index-matched facility in a series of closely spaced planes provide a comprehensive database for determining all the terms in the Reynolds stress and strain rate tensors. Results are also used for calculating the turbulent kinetic energy (TKE) production rate and transport terms by mean flow and turbulence. They elucidate some but not all of the observed phenomena, such as the high anisotropy, high turbulence levels in the vicinity of the tip leakage vortex (TLV) center, and in the shear layer connecting it to the blade suction side (SS) tip corner. The applicability of popular Reynolds stress models based on eddy viscosity is also evaluated by calculating it from the ratio between stress and strain rate components. Results vary substantially, depending on which components are involved, ranging from very large positive to negative values. In some areas, e.g., in the tip gap and around the TLV, the local stresses and strain rates do not appear to be correlated at all. In terms of effect on the mean flow, for most of the tip region, the mean advection terms are much higher than the Reynolds stress spatial gradients, i.e., the flow dynamics is dominated by pressure-driven transport. However, they are of similar magnitude in the shear layer, where modeling would be particularly challenging.

Direct numerical simulation of turbulent channel flow up to

2015 ◽  
Vol 774 ◽  
pp. 395-415 ◽  
Author(s):  
Myoungkyu Lee ◽  
Robert D. Moser
Keyword(s):  
Numerical Simulation ◽  
Reynolds Number ◽  
Direct Numerical Simulation ◽  
Turbulent Flows ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Streamwise Velocity ◽  
Mean Velocity ◽  
Layer Structures ◽  
High Reynolds

A direct numerical simulation of incompressible channel flow at a friction Reynolds number ($\mathit{Re}_{{\it\tau}}$) of 5186 has been performed, and the flow exhibits a number of the characteristics of high-Reynolds-number wall-bounded turbulent flows. For example, a region where the mean velocity has a logarithmic variation is observed, with von Kármán constant ${\it\kappa}=0.384\pm 0.004$. There is also a logarithmic dependence of the variance of the spanwise velocity component, though not the streamwise component. A distinct separation of scales exists between the large outer-layer structures and small inner-layer structures. At intermediate distances from the wall, the one-dimensional spectrum of the streamwise velocity fluctuation in both the streamwise and spanwise directions exhibits $k^{-1}$ dependence over a short range in wavenumber $(k)$. Further, consistent with previous experimental observations, when these spectra are multiplied by $k$ (premultiplied spectra), they have a bimodal structure with local peaks located at wavenumbers on either side of the $k^{-1}$ range.

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