Dynamics Of Turbulent Flows Research Articles

Investigation of the impact of transient pressure gradients on turbulent channel flow dynamics

AbstractThe effect of transient pressure gradients, or transient pumping in turbulent channel flow configuration, is investigated. Employing a cost‐efficient reduced‐order stochastic method known as one‐dimensional turbulence (ODT) modeling, simulations explore different signal shapes for the modulation of the prescribed pressure gradient forcing, including sinusoidal modulation, step‐like modulation, and piecewise sinusoidal beating. Various cycle periods and active pumping times are investigated. The simulations are conducted at a frictional Reynolds number of and a molecular Prandtl number of . The study adopts a passive scalar formulation to investigate heat transfer properties. The study quantifies the effects of transient pressure gradients on heat transfer rate, drag, and pumping power. Preliminary ODT predictions suggest that all transient cases exhibit lower heat transfer rates and a higher pumping power requirement than the constant pressure gradient case, with the step‐like modulation yields superior skin‐friction drag and heat transfer rate reductions relative to other signals.

Numerical Simulation of Fixed-Free End Beam’s Modal Behaviour using Two-Way Coupled Fluid-Structure Interaction Approach

The fluid flow velocity stands as a pivotal parameter with a great influence on the mutual interaction between the fluid domain and structure. This paper focuses on structural deformation, structural velocity, von-Mises stress and frequency response of a fixed-free end beam using two-way coupled fluid-structure interaction within ANSYS Workbench. The parameters such as deformed fluid pressure and velocity were analysed across three diverse flow regimes: laminar, transitional and turbulent – each aligned with distinct fluid velocities. The outcomes show that the total deformation, velocity and von-Mises stress distribution of the fixed-free end beam increase as the inlet fluid velocity increases. As a result, the pressure and velocity distribution in the fluid flow which receiving the resultant or leftover structure deflection also increases as the incoming fluid velocity increases. Furthermore, the analysis probes the frequency response in turbulent flow conditions reveals higher values compared to laminar and transitional flows, albeit within the same natural frequency domain. This observation marks significant vibrational characteristics found in the presence of turbulent flow dynamics.

PIV investigation of the effects of simulated ice blocks on the turbulent flow dynamics around a partially submerged horizontal circular cylinder

This paper presents a novel particle image velocimetry study of the effects of stationary upstream ice blocks on the turbulent flow dynamics around a single, fixed ice boom pontoon. The pontoon was modelled using a half-submerged horizontal circular cylinder. Different lengths, thicknesses, and undersurfaces of the ice blocks were examined. The Reynolds number was maintained at 25,500. The results show that the cylinder encounters an approach flow with lower momentum but higher turbulence intensity when placed behind a long ice block compared to a shorter one, and the presence of undersurface roughness reduces the momentum but enhances the turbulence intensity of the approach flow. The recirculation length, mean velocities, and turbulence levels around the cylinder reduce with increasing ice block length and subsequent roughening of its undersurface. However, a larger recirculation bubble forms from the ice block, and the mean velocities and turbulence levels increase when the ice block thickness increases.

Computational fluid dynamics: Its carbon footprint and role in carbon reduction

Turbulent flow physics regulates the aerodynamic properties of lifting surfaces, the thermodynamic efficiency of vapor power systems, and exchanges of natural and anthropogenic quantities between the atmosphere and ocean, to name just a few applications of contemporary importance. The space-time dynamics of turbulent flows are described via numerical integration of the non-linear Navier–Stokes equation—a procedure known as computational fluid dynamics (CFD). At the dawn of scientific computing in the late 1950s, it would be many decades before terms such as “carbon footprint” or “sustainability” entered the lexicon, and longer still before these themes attained national priority throughout advanced economies. The environmental cost associated with CFD is seldom considered. Yet, large-scale scientific computing relies on intensive cooling realized via external power generation that is primarily accomplished through the combustion of fossil fuels, which leads to carbon emissions. This paper introduces a framework designed to calculate the carbon footprint of CFD and its contribution to carbon emission reduction strategies. We will distinguish between “hero” and “routine” calculations, noting that the carbon footprint of hero calculations—which demand significant computing resources at top-tier data centers—is largely determined by the energy source mix utilized. We will also review CFD of flows where turbulence effects are modeled, thus reducing the degrees of freedom. Estimates of the carbon footprint are presented for such fully and partially resolved simulations as functions of turbulence activity and calculation year, demonstrating a reduction in carbon emissions by two to five orders of magnitude at practical conditions. Besides generating a carbon footprint, the community's effort to avoid redundant calculations via turbulence databases merits particular attention, with estimates indicating that a single database could potentially reduce CO2 emissions by approximately O(1) × 106 metric tons.

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

This study explores the implementation of physics-informed neural networks (PINNs) to analyze turbulent flow in composite porous-fluid systems. These systems are composed of a fluid-saturated porous medium and an adjacent fluid, where the flow properties are exchanged across the porous-fluid interface. The segregated PINN model employs a novel approach combining supervised learning and enforces fidelity to flow physics through penalization by the Reynolds-averaged Navier-Stokes (RANS) equations. Two cases were simulated for this purpose: solid block, i.e. porous media with zero porosity, and porous block with a defined porosity. The effect of providing internal training data on the accuracy of the PINN predictions for prominent flow features, including flow leakage, channeling effect and wake recirculation was investigated. Additionally, L2 norm error, which evaluates the prediction accuracy for flow variables was studied. Furthermore, PINN training time in both cases with internal training data was considered in this study. Results showed that the PINN model predictions with second-order internal training data achieved high accuracy for the prominent flow features compared to the RANS data, within a 20% L2 norm error of second-order statistics in the solid block case. In addition, for the porous block case, providing training data at the porous-fluid interface showed errors of 18.04% and 19.94% for second-order statistics, representing an increase in prediction accuracy by 7% compared to without interface training data. The study elucidates the impact of the internal training data distribution on the PINN training in complex turbulent flow dynamics, underscoring the necessity of turbulent second-order statistics variables in PINN training and an additional velocity gradient treatment to enhance PINN prediction.

Unveiling Turbulent Flow Dynamics in Blind-Tee Pipelines: Enhancing Fluid Mixing in Subsea Pipeline Systems

Blind tees, as important junctions, are widely used in offshore oil and gas transportation systems to improve mixing flow conditions and measurement accuracies in curved pipes. Despite the significance of blind tees, their unsteady flow characteristics and mixing mechanisms in turbulent flow regimes are not clearly established. Therefore, in this study, Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations, coupled with Explicit Algebraic Reynolds Stress Model (EARSM), are employed to explore the complex turbulent flow characteristics within blind-tee pipes. Firstly, the statistical flow features are investigated based on the time-averaged results, and the swirl dissipation analysis reveals an intense dissipative process occurring within blind tees, surpassing conventional elbows in swirling intensity. Then, the instantaneous flow characteristics are investigated through time and frequency domain analysis, uncovering the oscillatory patterns and elucidating the mechanisms behind unsteady secondary flow motions. In a 2D-length blind tee, a nondimensional dominant frequency of oscillation (Stbt = 0.0361) is identified, highlighting the significant correlation between dominant frequencies inside and downstream of the plugged section, which emphasizes the critical role of the plugged structure in these unsteady motions. Finally, a power spectra analysis is conducted to explore the influence of blind-tee structures, indicating that the blind-tee length of lbt = 2D enhances the flow-mixing conditions by amplifying the oscillation intensities of secondary flow motions.

Enhancing flood wave modelling of reservoir failure: a comparative study of structure-from-motion based 2D and 3D methodologies

Predicting flood wave propagation from reservoir failures is critical to practical flood hazard assessment and risk management. Flood waves are sensitive to topography, channel geometry, structures, and natural features along floodplain paths. Thus, the accuracy of flood wave modelling depends on how precisely those features are represented. This study introduces an enhancing approach to flood wave modelling by accurately representing three-dimensional objects in floodplains using the structure-from-motion (SfM). This method uses an unmanned aerial vehicle to capture topographic complexities and account for ground objects that impact flood propagation. Using the three-dimensional volume of fluid numerical approach significantly improves an enhanced representation of turbulent flow dynamics and computational efficiency, especially in handling large topography datasets. Reproductions from this enhanced three-dimensional approach were validated against recent reservoir failure observations and contrasted with traditional two-dimensional models. The results revealed that the suggested three-dimensional methodology achieved a significant 84.4% reproducibility when juxtaposed with actual inundation traces. It was 35.5%p more accurate than the two-dimensional diffusion wave equation (DWE) and 17.1%p more than the shallow water equation (SWE) methods in predicting flood waves. This suggests that the reproducibility of the DWE and SWE decreases compared to the three-dimensional approach when considering more complex floodplains. These results demonstrate that three-dimensional flood wave analysis with the SfM methodology is optimal for effectively minimising topographic and flood wave reproduction errors across extensive areas. This dual reduction in errors significantly enhances the reliability of flood hazard assessments and improves risk management by providing more precise and realistic predictions of flood waves.

On the prediction of the turbulent flow behind cylinder arrays via echo state networks

This study aims at the prediction of the turbulent flow behind cylinder arrays by the application of Echo State Networks (ESN). Three different arrangements of arrays of seven cylinders are chosen for the current study. These represent different flow regimes: single bluff body flow, transient flow, and co-shedding flow. This allows the investigation of turbulent flows that fundamentally originate from wake flows yet exhibit highly diverse dynamics. The data is reduced by Proper Orthogonal Decomposition (POD) which is optimal in terms of kinetic energy. The Time Coefficients of the POD Modes (TCPM) are predicted by the ESN. The network architecture is optimized with respect to its three main hyperparameters, Input Scaling (INS), Spectral Radius (SR), and Leaking Rate (LR), in order to produce the best predictions in terms of Weighted Prediction Score (WPS), a metric leveling statistic and deterministic prediction. In general, the ESN is capable of imitating the complex dynamics of turbulent flows even for longer periods of several vortex shedding cycles. Furthermore, the mutual interdependencies of the TCPM are well preserved. However, optimal hyperparameters depend strongly on the flow characteristics. Generally, as flow dynamics become faster and more intermittent, larger LR and INS values result in better predictions, whereas less clear trends for SR are observable.

Toward Scale-Adaptive Subgrid-Scale Model in LES for Turbulent Flow Past a Sphere

This study explores the dynamics of turbulent flow around a sphere at a Reynolds number of Re=103 using large-eddy simulation, focusing on the intricate connection between vortices and strain within the recirculation bubble of the wake. Employing a relatively new subgrid-scale modeling approach based on scale adaptivity, this research implements a functional relation to compute ksgs that encompasses both vortex-stretching and strain rate mechanisms essential for the energy cascade process. The effectiveness of this approach is analyzed in the wake of the sphere, particularly in the recirculation bubble, at the specified Reynolds number. It is also evaluated in comparison with two different subgrid-scale models through detailed analysis of the coherent structures within the recirculation bubble. These models—scale-adaptive, k-Equation, and dynamic k-Equation—are assessed for their ability to capture the complex flow dynamics near the wake. The findings indicate that while all models proficiently simulate key turbulent wake features such as vortex formation and kinetic energy distribution, they exhibit unique strengths and limitations in depicting specific flow characteristics. The scale-adaptive model shows a good ability to dynamically adjust to local flow conditions, thereby enhancing the representation of turbulent structures and eddy viscosity. Similarly, the dKE model exhibits advantages in energy dissipation and vortex dynamics due to its capability to adjust coefficients dynamically based on local conditions. The comparative analysis and statistical evaluation of vortex stretching and strain across models deepen the understanding of turbulence asymmetries and intensities, providing crucial insights for advancing aerodynamic design and analysis in various engineering fields and laying the groundwork for further sophisticated turbulence modeling explorations.

On the non-local nature of turbulent fluxes of passive scalars

The parameterization of fluxes associated with representing unresolved dynamics in turbulent flows, especially in the atmosphere and ocean (which have a vast range of scales), remains a challenging task. This is especially true for Earth system models including complex biogeochemistry and requiring very long simulations. The problem of representing the dependence of the mean flux of a passive tracer in terms of the mean has a very long history; in this study, we take a somewhat different approach. We use a formalism showing that the mean flux will be a functional of the mean gradients, a formalism that can be used to calculate the structure of the functional which is non-local in both space and time. Two-dimensional turbulent simulations are used to explore the weight of nearby (in space or time) gradients. We also use stochastic velocities and iterated maps to show that the results are similar. The functional formalism provides an understanding of when non-locality needs to be considered and when a local eddy diffusivity can be a reasonably good approximation. Furthermore, the formalism provides guidance for the development of data-driven parameterizations.

Turbulent flow around submerged foundation arrays for ocean energy

Submerged ocean energy foundations experience dynamic loading due to the non-uniform approach velocity and wake propagation behind them. This paper investigates the unsteady turbulent flow dynamics and propagation of large-scale vortical structures around submerged foundations using a large eddy simulation. The computational domain constituted an array of eight foundations arranged in an inline configuration of two columns. Two streamwise spacing, x = 6D and 8D, are investigated at an approach Reynolds number, based on the channel hydraulic diameter, of 8.5 × 104. The parametric study reveals the expected changes in wake recovery and their impact on the downstream foundations. The narrow spacing promotes intense wake interactions, while larger inline spacing causes the entrainment of ambient flow, thereby limiting the wake interaction with the downstream foundations. A two-point correlation analysis reveals larger integral turbulent timescales for the 6D spacing compared to the 8D spacing. The separated flow around the foundations is characterized by eddies shed at varying frequencies. Multiple spectra peaks dominate at low frequencies, signifying dominant energy-containing large-scale vortices coupled with periodic excitation of the vortices as energy is transferred from the large-to small-scale turbulent structures.

Setting up a CFD model to evaluate the impact of green infrastructures on local air quality

AbstractGreen infrastructures have been pointed out as innovative solutions to deal with current and future challenges related to air pollution and climate change. Although the potential of green infrastructures, such as green walls and green roofs, to mitigate air pollution has been documented, evidence at a local scale is still limited. This work aims to increase knowledge about the potentialities of green infrastructures in improving local air quality, focusing on particulate matter, nitrogen dioxide and ozone pollutants, and by using a local-scale computational fluid dynamics model. The ENVI-met model was applied to a particular hour of a summer day over a built-up environment centred on a main avenue in the city of Lisbon (Portugal). The dimensions of the computational domain are 618 m × 594 m × 143 m, and it contains 184 buildings, with the tallest building being 56 m. In addition to the baseline simulation, modelling was also done considering the application of green walls and green roofs to specific buildings located near the main avenue, together with a green corridor. The overall results show no disturbances exerted by green walls on the turbulent flow dynamics and on the air quality levels when compared to the baseline scenario (without green walls). The integrated scenario, which includes green walls, green roofs and a green corridor, will lead to potential local benefits of green infrastructures on O3 concentrations, followed by variable impacts on NO2 and particulate matter concentrations.

Lagrangian and Eulerian perspectives of turbulent transport mechanisms in a lateral cavity

The dynamics of turbulent flows past lateral cavities is relevant for multiple environmental applications. In rivers and coastal environments, these lateral recirculating regions constitute surface storage zones, where large-scale turbulent coherent structures control the transport and fate of contaminants. Mass transport in these flows is typically represented by one-dimensional first-order equations that predict the evolution of the spatially integrated concentration between the cavity and the main channel. These models, however, cannot represent the long-term evolution of the concentration or incorporate memory effects induced by turbulence. In this investigation, we carry out large-eddy simulations (LES) of the open-channel flow with a lateral square cavity of Mignot et al. (Phys. Fluids, vol. 28, issue 4, 2016, 045104). The model is coupled with an advection–diffusion equation and a Lagrangian particle model to investigate the transport mechanisms in the cavity and across the interface. From the simulations we provide quantitative comparisons of the physical processes from both perspectives, and investigate the effects of turbulent coherent structures on residence times and trajectories from finite-time Lyapunov exponents. From the Lagrangian results, we identify general spatial distributions of time scales in the cavity associated with the dynamics of coherent structures, providing new insights into the mechanisms that drive the global transport. We also show that an upscaled model informed by LES and based on a fractional derivative captures the evolution of concentration, and the exchange between the cavity and the main channel, providing accurate predictions of mass transport and reproducing the temporal dependence observed at larger scales.

Turbulent Pulsating Convective Flow in the Quasi-Steady and Low-Frequency Regimes

Abstract The instantaneous and time-averaged dynamics of turbulent pulsating convective pipe flow is investigated experimentally over Strouhal number, St=3.3×10−4−0.12 that falls in the quasi-steady and low-frequency regimes, pulsation amplitude, βb=0.05−0.2, and bulk Reynolds numbers, Reb=7528−10,920. The analytical expressions for pulsation amplitudes of centerline velocity, bulk velocity, and Nusselt number are derived. The time series of oscillating components of centerline velocity (Uc˜), cross-sectionally averaged bulk velocity (Ub˜), and Nusselt number (Nu˜) depicts that the phase differences between Uc˜, and Ub˜, and between Ub˜, and Nu˜ increase with St nonmonotonically with near zero phase difference at St→0. The time-averaged pulsating Nusselt number Nu¯ is invariant of St for St > 0.01. Nu¯ depends marginally on βb. The relative mean Nusselt number, Nur=Nu¯/Nus<1 for Reb≥8885 and Nur>1 for Reb = 7528. The general observations from this study is that, in the quasi-steady and low-frequency regimes, turbulent pulsating flows leads to marginal changes in the time-averaged Nusselt number Nu¯ compared to the time-averaged Nusselt number Nus in steady flow condition at any Reb.

Heat release and flame scale effects on turbulence dynamics in confined premixed flows

As industry transitions to a net-zero carbon future, turbulent premixed combustion will remain an integral process for power generating gas turbines, aviation engines, and high-speed propulsion due to their ability to minimize pollutant emissions. However, accurately predicting the behavior of a turbulent reacting flow field remains a challenge. To better understand the dynamics of premixed reacting flows, this study experimentally investigates the effects of combustion heat release and flame scales on the evolution of turbulence in a high-speed, confined bluff-body combustor. The combustor is operated across a range of equivalence ratios from 0.7 to 1 to isolate the role of chemical heat release, flame speed, and flame thickness on the evolution of turbulence as the flow progresses from reactants to products. High-speed particle image velocimetry and CH* chemiluminescence imaging systems are simultaneously employed to quantify turbulent flame and flow dynamics. The results notably demonstrate that the flame augments turbulence fluctuations as the flow evolves from reactants to products for all cases, which opposes most simulations of premixed turbulent reactions. Notably, turbulence fluctuations increase monotonically with the heat of combustion and corresponding turbulent flame speed. Spatial profiles of turbulence statistics are conditioned on the mean flame front, and nondimensionalizing the turbulence profiles using laminar flame properties is shown to collapse all conditions onto a single curve. The resulting nondimensional profile confirms that turbulence dynamics scales with the heat of combustion and was used to develop a novel correlation to predict the increase in turbulent fluctuations across the premixed flame. A Reynolds averaged Navier Stokes decomposition is also explored to further characterize the effects of combustion heat release on the dominant mechanisms of turbulent energy transport. The cumulative results can guide modeling capabilities to better predict flame and flow dynamics and accelerate design strategies for premixed turbines with carbon-free fuels.

Statistical moments for simulation calibration with model-bridge

Computer simulations are actively used for analyzing complex phenomena, especially in fields where access to their real-world counterparts is not feasible, such as physics, chemistry, material science, and others. The key to executing successful simulations is making sure that the parameters of a simulator reflect real-world scenarios, a tedious and error-prone effort addressed through simulation calibration. Recently, several methods have been proposed to automatize this task by learning from previously calibrated simulations using a model-bridge paradigm: a complex simulation is replaced by a simpler surrogate model, which can then be bridged to the calibrated simulation parameters. However, designing the surrogate model is a non-trivial problem involving trade-offs between simplicity of representation, interpretability and calibration accuracy, as well as the complexity of the bridge model required to map the surrogate to calibrated parameters. Further, while effective, such approaches can be non-intuitive for practitioners due to their distance from the simulation. In this paper, we view a simulation as a distribution of output variables, which can be easily represented by statistical moments. This yields a very simple and interpretable surrogate that can be bridged to calibrated parameters with a simple linear regression. We show that our method outperforms previous approaches, in terms of calibration accuracy and time, through experiments on simulations of turbulent flow dynamics and synthetic signals.

Random Fourier Surrogate for simulation calibration

In this paper we propose Random Fourier Surrogate (RFS) as a method for simulation calibration. Computer simulations are actively used in various fields to model and analyze complex real-world phenomena, enabling practitioners to explore situations inaccessible by other means. To create a simulation that accurately reflects the desired real-world scenario requires calibrating the simulation parameters, a non-trivial process that requires domain expertise and iterative trial-and-error. The model-bridge paradigm has been proposed to automatize this process, where a more computationally efficient surrogate model is used in lieu of the real simulation, and a bridge model is used to map the noninterpretable surrogate parameters to calibrated simulation parameters, which are physically interpretable. Recently an efficient solution called tangent slope-intercept (TSI) descriptors has been proposed, where tangent lines of a principal curve are used to represent the simulation in low dimensionality. However, we find that it has two key problems: it is prone to overfitting and is sensitive to initialization. We address these issues using Random Fourier Features to construct a surrogate model, thereby reducing calibration time by avoiding complex computations, while retaining high accuracy. We evaluate the effectiveness of our method with different experiments on synthetic signal simulations and physical simulations of turbulent flow dynamics.

Coexistence of different mechanisms underlying the dynamics of supersonic turbulent flow over a compression ramp

Supersonic turbulent flow over a compression ramp is studied using wall-resolved large eddy simulation with a freestream Mach number of 2.95 and a Reynolds number [based on δ0: the thickness of incoming turbulent boundary layer (TBL)] of 63 560. The unsteady dynamics of the present shock wave/turbulent boundary layer interaction (STBLI) flow are investigated by using dynamic mode decomposition techniques, linear and nonlinear disambiguation optimization, local stability analysis (LSA), and global stability analysis (GSA). By analyzing the dynamic system for the STBLI flow, three dynamically important modes with characteristic spanwise wavelengths of 2δ0, 3δ0, and 6δ0 are captured. The 2δ0 mode approximates the spanwise scale of the Görtler-like vortices and Görtler mode of LSA, suggesting the presence of Görtler instability, which is believed to be related to the unsteady motion of streaks downstream of reattachment in the flow. The features of the 3δ0 mode are also observed in large-scale motions of the incoming TBL, implying the existence of a convective mechanism that is excited and maintained by such motions. Additionally, the GSA results show the most unstable mode features a spanwise wavelength of around 6δ0, indicating the existence of global instability that is believed to be related to the oscillating motion of separation shock. The coexistence of these three mechanisms is confirmed. Discussions on the above findings provide an interpretation for low-frequency unsteadiness that the unsteadiness of surface streaks results from the combined effects of the Görtler instability near flow reattachment and the convection of large-scale motions in the incoming boundary layer, while the low-frequency shock motion may be related to a global mode driven by upstream disturbances.

Extinction of buoyant turbulent non-premixed flames under reduced oxygen concentrations

Local and global extinction of 10 kW buoyant turbulent non-premixed flames of methane from a 13.7 cm diameter burner under oxygen concentrations (OCs) from 20.9 % to 12 % are investigated using a combination of OH* chemiluminescence imaging and exhaust-gas sampling. Local flame extinction, indicated by holes or discontinuities in the OH* images, is only observed at OC lower than 17 %. For OC below 16 %, large-scale turbulent flow dynamics causes rapid creation of large extinction holes that do not reclose, and the overall combustion efficiency (η) decreases rapidly as local extinction becomes more probable. Direct observation of local extinction in buoyant turbulent flames provides important physical insights and validation data for models of extinction and re-ignition by distinguishing their respective contributions to η. We observe that local extinction events are associated with large-scale flow features, leading us to hypothesize that extinction behavior correlates with resolved-scale strain rates from large-eddy simulation (LES) in excess of the laminar extinction limit. This idea is investigated by performing LES for the target flame, extracting strain-rate statistics on the flame surface, and comparing these to both the extinction limits for canonical counterflow diffusion flames and to the experimentally observed extinction behavior. Results show that the cumulative distribution of turbulent flame strain rates up to the laminar extinction limit closely follows the measured OC dependence of η. This approach to η works well for different OCs and other fuels such as propane and ethylene, which suggests great potential for the development of computationally efficient extinction models for fire suppression.

Numerical Analysis of Turbulent Air Flow Dynamics in a Rectangular Channel with Perforated Nozzle-Shaped Vertical Baffles

Numerical Analysis of Turbulent Air Flow Dynamics in a Rectangular Channel with Perforated Nozzle-Shaped Vertical Baffles