Wednesday, October 18

Parviz Moin - Stanford University

From dynamic subgrid scale to wall modeling Charles’ journey (1989-2023)

In this presentation I will review Charles Meneveau’s important contributions to turbulence theory and modeling during his very productive postdoctoral tenure at CTR, and his participation in subsequent summer programs. These include study of turbulence dynamics and energy cascade and backscatter in wavelet representation, subgrid scale parameterizations, including development of the Lagrangian dynamic subgrid scale model, corrections for anisotropic grids and low computational cell based Reynolds numbers, and flame stretching and quenching in turbulent combustion. Charles’ work on large eddy simulation at Hopkins includes wall modeling which has been a pacing item in large eddy simulation of high Reynolds number wall bounded flows. I will conclude by presenting recent progress in wall modeled large eddy simulation of complex flows at CTR. Our recent work has demonstrated that leveraging large eddy simulation with appropriate wall/subgrid-scale models and low dissipation numerical methods suitable for complex geometries on modern computer architectures offers a tractable path towards meeting industry’s stringent accuracy and affordability requirements.

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Ivan Marusic - University of Melbourne

Active and inactive motions and the k-1 law in wall turbulence

Townsend introduced the concept of active and inactive motions for wall-bounded turbulent flows, where the active motions are solely responsible for producing the Reynolds shear stress. In this talk, we present a method to segregate the active and inactive components. The inactive motions are found to consist of contributions from classical (self-similar) attached eddies and very-large scale (superstructure) motions. The pure attached eddy contributions are shown to produce clear k -1 scaling for the streamwise component of velocity for both streamwise and spanwise wavenumbers, consistent with a logarithmic profile for the inner-scaled streamwise velocity variance across the inertial region.

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Ugo Piomelli - Queen’s University

Wall-Modelled LES of Heterogeneous Rough Surfaces

Surface roughness is present in many applications in engineering and natural sciences. Its effect is not only that of increasing drag, but also of modifying the turbulence-generation cycle. Roughness has an even more significant impact on the flow-field when it is heterogeneously distributed, since the presence of local discontinuities results in non-equilibrium effects. Resolved numerical simulations can provide useful insights on the flow state over rough walls, but are limited to low Reynolds-numbers. Wall-modelled large-eddy simulations (WMLES), on the other hand, allow us to reach high Reynolds numbers, at the expense of additional modelling. In this work, WMLES were performed to simulate the flow over roughness strips placed normal to the mean flow. We compared the standard log-law based equilibrium wall-model with the generalized Moody-diagram model of Meneveau [J. Turbulence, 21(11):650–673, 2020] and with the Lagrangian Relaxation-Towards-Equilibrium (LaRTE) wall model [Fowler et al., J. Fluid Mech., 934(A44):1–37, 2022]. This model allows the inclusion of non-equilibrium effects in the response of the wall shear-stress to perturbations. A new formulation of the LaRTE model to rough walls is also proposed that allows the model to switch seamlessly between smooth-wall behaviour and transitionally or fully rough flow conditions. Applications of the extended LaRTE wall model are presented in the homogeneous rough-walls channel configuration and the in the normal-strips case.

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Dennice Gayme - Johns Hopkins University

A journey through from turbulence to wind farms

Two key challenges that need to be overcome for wind power to achieve its full potential as one of the main sources of electric power (1) obtaining accurate predictions for wind farm power output levels under different wind conditions, and (2) ensuring that wind farms can successfully operate within the current and anticipated energy markets of the future. The first of these is a modeling problem, whereas the operational concerns can be addressed through appropriate control. This talk describes the JHU Turbulence Research groups ongoing work to address these challenges by exploiting large-eddy simulations (LES) to inform a suite of wind farm models ranging from static engineering models to control oriented dynamic models. Model-based control approaches that enable wind farms to participate in grid services is demonstrated in closed loop control of LES. This work represents more than a decade of close collaboration with Charles Meneveau, whose work first inspired my interest in wind energy. _____________________________________________________________________________________________

Cristina Archer - University of Delaware

On wind farm parameterizations for numerical weather prediction

A parameterization for any numerical model is a way to include the effects of a process of interest that cannot be resolved directly by the numerical model, typically because the spatial resolution of the numerical model is not fine enough to explicitly treat that process. A parameterization is basically a model-within-a-model that uses the resolved variables at each grid cell to calculate the effects of the process of interest on the resolved variables in that cell (but not the process itself). In the case of numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, several processes are parameterized, including convection, boundary layer turbulence, radiation, to name a few. In the context of numerical weather prediction for wind energy applications, a wind farm parameterization (WFP) is needed to determine the effects of the wind turbines in a wind farm on the atmosphere because resolving the flow around the individual blades cannot be accomplished at

the typical resolution of WRF applications (order of 10 km). Existing WFPs, including the Fitch, Jensen, Gaussian-like, and geometric model, will be discussed and compared against wind farm data and recommendations for future improvements will be provided.

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Raúl Bayoán Cal - Portland State University

Marching from land to sea exploring wind energy with Charles

Once upon a time, we started playing with toy motors and RC plane impellers; this led to creating a scaled wind farm. Many things occurred in between leading to operating active grids, a wind/wave facility, laser, high speed cameras, accelerometers, arUco markers and wave cancelling devices for the application of offshore floating wind energy. We quantified the mechanism for energy extraction for an onshore wind farm and characterized the wind-wave-wake coupling through power extraction, dynamics of the turbine and modulation of the wake for an offshore wind farm. In these twenty minutes, we tell that story with Charles as a central character in it.

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 Johan Meyers - KU Leuven

Investigating LES-based MPC for real-time control of windfarms

Large-eddy simulations are commonly considered too slow to serve as a real-time control model for wind farms. However, simulations can always be accelerated by coarsening the mesh. The trade-off between control performance and speed is investigated for different set-up choices (mesh, time horizon, ...) using a time-decoupled model-predictive control (TD-MPC) approach. A fine-grid LES is used as a wind-farm emulator to which the TD-MPC signals are applied. For now, we make abstraction of state estimation, using an exact state initialization based on filtering the emulator field. However, we will briefly touch upon recent progress on 4Dvar state estimation of the wind-farm flow field at the end of the talk.

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Aleksander Szalay - Johns Hopkins University

From Turbulence Simulations to Petascale Interactive Numerical Laboratories

Scientists in many disciplines would like to compare the results of their experiments or theoretical hypotheses to data emerging from numerical simulations based on first principles. This requires not only that we can run sophisticated simulations and models, but that at least a selected subset of the results of these simulations are available publicly, through an easy-to-use portal. We have to turn our simulations into open numerical laboratories in which anyone can perform their own experiments.  For a scalable analysis we must have an inherently scalable data access. Flat files violate this principle: the user cannot do anything until a very large file has been physically transferred.

The JHU Turbulence databases provide an immersive environment, where users can insert their virtual sensors into the simulation, sending a data stream back to the user. The sensors can be pinned to Eulerian locations or they can move with the flow. They can feed back data on multiple channels, have a variety of operators, e.g.  Laplacian, or various filters. This model also enables users to run time backwards, impossible in a direct numerical simulation involving dissipation. The snapshots are saved frequently enough that one can smoothly interpolate velocities. This simple interface has provided a very flexible, yet powerful way to do science with large data sets from anywhere in the world – we have served over 12 trillion measurements to the community.

Soon we will have Exascale systems, with memory footprints of several petabytes. As a result, only a small fraction of the complete output can ever be saved for later reuse and much of the analysis will have to be done in-situ.  This will make it increasingly harder for scientists outside the core simulation team to reuse this data. The talk will explore ideas how this challenge can be potentially resolved in a satisfactory fashion.

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Hassan Nagib - Illinois Tech 

Wall-Bounded Turbulence: Recent Lessons from Experiments-Asymptotics-ComputationWall-Bounded Turbulence: Recent Lessons from Experiments-Asymptotics-Computation= 

Utilizing the three-pronged approach of experimental measurements, computational results (DNS) and matched asymptotic analysis, we have reexamined the three canonical wall-bounded turbulent flows of pipe, channel and zero pressure gradient boundary layer. Detailed and systematic study confirmed the non-universality of the Kármán constant (k) reported in 2008 by Nagib, H. M. & Chauhan, K. A. “Variations of von Kármán coefficient in canonical flows,” Phys. Fluids 20, 101518. Recently, a new matching approach also revealed an inner-outer overlap consisting of a superposition of log-law and a linear term, as detailed in paper by Monkewitz, P. A. & Nagib, H. M. “The hunt for the Kármán “constant” Revisited,” J. Fluid Mechanics, vol. 967, A15, 2023. DOI: https://doi.org/10.1017/jfm.2023.448.

A similar linear term was suggested by Afzal & Yajnik, J. Fluid Mech. (1973 & 1970) and Luchini (2017) Phys. Rev. Lett. 118, 224501. In our results, we find that the coefficients of both terms are dependent on the pressure gradient of the flow. A new and robust method is devised to simultaneously determine the coefficients of the log and linear terms, in pressure driven flows at currently accessible Reynolds numbers, and yields k values that are consistent with the k values deduced from the Reynolds number dependence of centerline velocities.

After many decades of experience with “canonical” wall-bounded turbulent flows, we recognize fully developed pipe flow as the ideal flow to compare computations and experiments. With collaborators at several universities, we have conducted experiments, with Ret up to 33,000, and DNS for pipe flow at Ret = 550 & 1,000, with several resolutions, and extended Eddy Turnover Times (ETT) of up to 200. New criteria for resolution and an existing criterion for convergence of DNS are being developed and confirmed, respectively.

We find that higher resolution DNS and longer computational times are required for wall-bounded turbulence, compared to values commonly used. Finally, the impact of the new combined log-law and linear term on Textbooks, Lecture Notes, RANS Codes and Turbulence Models will be highlighted.

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Marc Calaf - University of Utah

Is turbulence anisotropy the missing ingredient in classical atmospheric surface layer turbulence theory?

Traditionally, land-atmosphere turbulent exchanges of momentum, energy, and mass, are interpreted through Monin-Obukhov similarity theory (MOST). Based on dimensional analysis, MOST states that for high Reynolds number flows, in the absence of mean downward vertical flow, steady-state conditions, and horizontal homogeneity, turbulence is dictated by the balance between shear and buoyancy production/destruction, represented by a single non-dimensional length scale, . Thus, based on MOST, any mean quantity  representing the land-atmosphere turbulent exchanges, when properly non-dimensionalized with the respective turbulent scaling variable , can be expressed as a universal function  of the scaling parameter , such that . The specific functional forms of the scaling relations  have been obtained experimentally by curve fitting through years of experimental campaigns. These relations are widely used in atmospheric surface layer parametrizations for most Earth System Models of different scales.

However MOST suffers from significant failures that limit its applicability like the lack of scaling of horizontal velocity variances under unstable thermal stratification, the non-scaling of surface-normal velocity and temperature variances in stable stratification, as well as the general breakdown of scaling for intermittent turbulence. Furthermore, MOST also fails in representing land-atmosphere turbulent exchanges over perturbed surfaces (e.g. heterogeneous landscapes, complex terrain, etc.), where the original MOST assumptions breakdown. Therefore, it has now been long hypothesized the need for the inclusion of an additional non-dimensional parameter that is able to encapsulate the missing information. In this presentation, the use of turbulence anisotropy as this additional non-dimensional variable that can generalize the representation of near surface turbulent exchanges over perturbed surface conditions is explored. To demonstrate its potential, an unprecedented set of atmospheric datasets representative of a wide range of different surface and flow conditions is used. The resulting novel scaling relations not only offer a path-forward in addressing a 70-year old problem in ABL meteorology, but also provide a deeper understanding of turbulence, and its role in the surface-atmosphere exchange over realistic terrain.      

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Michael Howland - Massachusetts Institute of Technology 

From experiments and modeling to utility-scale wind farm flow control

Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that can reduce energy 10-20%. Wind farm flow control has demonstrated potential to increase collective power production. Large eddy simulations and experiments have demonstrated that yaw misaligned wind turbines generate large-scale counter-rotating vortices that deflect and deform the wake. To achieve maximum farm power production, the models used for wind farm flow control must accurately represent the flow physics and must be computationally efficient. However, existing wake models used for control often estimate the thrust and power of yaw misaligned turbines using simplified empirical expressions which require expensive calibration data and do not accurately extrapolate between turbine models. The thrust, wake velocity deficit, wake deflection, and power production of a yawed wind turbine depend on its induced velocity. The induced velocity depends on both the yaw angle and thrust coefficient of the wind turbine. We extend classical one-dimensional momentum theory to model the induction of a yaw misaligned actuator disk. Analytical expressions for the induction, thrust, initial wake velocity deficit, initial transverse velocity, and power are developed as a function of the yaw misalignment angle and the thrust coefficient. The analytical model is validated against large eddy simulations of a yaw misaligned actuator disk over a range of yaws and thrust coefficients. Leveraging the rotor aerodynamic model coupled with a wake model, we designed a physics-based, data-assisted wake steering control method to increase the power production of wind farms, which utilizes data assimilation and gradient-based optimization. The method was first validated, demonstrating that it predicts the true power maximizing operation, and then tested in a multi-turbine array at a utility-scale wind farm, where it statistically significantly increased the energy production over standard, individual operation.

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Xiang Yang - Pennsylvania State University

A universal velocity transformation for non-equilibrium boundary layers and a predictive near-wall model

The logarithmic law of the wall does not capture the mean flow when a boundary layer is subjected to, e.g., a strong pressure gradient. In such a boundary layer, the mean flow is affected by the spatio-temporal history of the imposed pressure gradient; and accounting for history effects is a challenge. This talk will discuss a universal mean flow scaling for boundary layers subjected to arbitrary adverse or/and favorable pressure gradients. We derive from the Navier-Stokes equation a velocity transformation that accounts for the history effects and maps the mean flow to the canonical law of the wall. The validity of the transformation is demonstrated in channel flows with a suddenly imposed adverse or favorable pressure gradient, boundary layer flows subjected to an adverse pressure gradient, and Couette-Poiseuille flows with a streamwise pressure gradient. The transformed velocity profiles are found to closely follow the equilibrium law of the wall. We then exploit the established mean flow scaling and develop a predictive model. The developed model is found to capture incipient flow separation.

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Marcelo Chamecki - University of California – Los Angeles

Turbulence and similarity in stratified boundary layers

In this work we use LES to study turbulence structure and its implications for mean velocity and temperature similarity in thermally stratified boundary layers. The approach of identifying uniform momentum zones (UMZs) is extended to the temperature field (UTZs) and applied to instantaneous LES fields. Results show that the vertical thickness of UMZs and UTZs is proportional to height above the surface for near-neutral and weak stratification but becomes thinner and less dependent on height as the stability increases (i.e., the z-less regime). Deviations from the logarithmic mean profiles for velocity and temperature observed under neutral conditions are therefore predominantly due to the reduction in “eddy size” with increasing stratification. We find that surface layer scaling following Monin-Obukhov Similarity fails to collapse the mean profiles, and propose a new similarity based on a mixed scaling parameter including the boundary layer depth. The proposed mixed scaling and relevance of the BL height can be explained by physical arguments related to the limit of z-less stratification that is reached asymptotically above the surface layer.

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Tamer Zaki - Johns Hopkins University

The origin of the increase in skin friction during laminar-to-turbulence transition

In wall-bounded flows, transition to turbulence is accompanied by a substantial increase in skin friction, or in wall vorticity.  To determine the origin of this vorticity, we calculate the expectation of a stochastic Cauchy invariant in backward time.  This calculation allows to separate contributions due to (i) wall vorticity flux (Lighthill source) and (ii) pre-existing interior vorticity evolved by nonlinear advection, viscous diffusion, vortex stretching and tilting. These contributions are quantified along backward stochastic Lagrangian trajectories, which are tracked using the Navier-Stokes solution of bypass transition that is stored in the Johns Hopkins Turbulence Databases.  We repeat the analysis for an ensemble of wall-stress maxima, and demonstrate that the Lighthill source can be either positive or negative and therefore is not the source of the skin-friction increase.  The primary mechanism is the spanwise stretching of earlier-in-time, near-wall spanwise vorticity.   These results are the first rigorous, quantitative determination of the origin of the increase in skin friction during laminar-to-turbulence transition (Wang, Eyink & Zaki, J. Fluid Mech., 941, A32, 2022).

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