Secondary Flow in Smooth and Rough Turbulent Circular Pipes: Turbulence Kinetic Energy Budgets

Direct Numerical Simulations have been performed for turbulent flow in circular pipes with smooth and corrugated walls. The numerical method, based on second-order finite discretization together with the immersed boundary technique, was validated and applied to various types of flows. The analysis is focused on the turbulence kinetic energy and its budget. Large differences have been found in the near-wall region at low Reynolds number. The change in the near-wall turbulent structures is responsible for increase of drag and turbulence kinetic energy. To investigatselinae the effects of wall corrugations, the velocity fields have been decomposed so as to isolate coherent and incoherent motions. For corrugated walls, we find that coherent motions are strongest for walls covered with square bars aligned with the flow direction. In particular, the coherent contribution is substantial when the bars are spaced apart by a distance larger than their height. Detailed analysis of the turbulence kinetic energy budget shows for this set-up a very different behavior than for the other types of corrugations.

Large Eddy Simulation of Leakage Flow in a Stepped Labyrinth Seal

Large eddy simulation (LES) and Reynolds averaged Navier-Stokes simulation (RANS) of leakage flow in straight-through and stepped labyrinth seals were performed in order to compare their performances in sealing the secondary flow passage of the gas turbine based on the respective discharge coefficients. The results indicate a 17.8% higher leakage prevention performance for the stepped seal relative to that of the straight seal. Further, while the LES predicts an ~7% reduction in the discharge coefficient due to shaft rotation, this effect is underestimated by the RANS. Moreover, the LES correctly predicts a laminarized flow pattern in the clearance, whereas the RANS overestimates the turbulence kinetic energy. In addition, a turbulence kinetic energy spectrum analysis was performed based on the vorticity at selected points in order to identify the flow structure that has a dominant influence on the oscillation of the discharge coefficient. This analysis also enabled identification of the changes in the flow structure due to shaft rotation.

Role of Advection of Parameterized Turbulence Kinetic Energy in Idealized Tropical Cyclone Simulations

Horizontal homogeneity is typically assumed in the design of planetary boundary layer (PBL) parameterizations in weather prediction models. Consistent with this assumption, PBL schemes with predictive equations for subgrid turbulence kinetic energy (TKE) typically neglect advection of TKE. However, tropical cyclone (TC) boundary layers are inhomogeneous, particularly in the eyewall. To gain further insight, this study examines the effect of advection of TKE using the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme in idealized TC simulations. The analysis focuses on two simulations, one that includes TKE advection (CTL) and one that does not (NoADV). Results show that relatively large TKE in the eyewall above 2 km is predominantly attributable to vertical advection of TKE in CTL. Interestingly, buoyancy production of TKE is negative in this region in both simulations; thus, buoyancy effects cannot explain observed columns of TKE in TC eyewalls. Both horizontal and vertical advection of TKE tends to reduce TKE and vertical viscosity (Km) in the near-surface inflow layer, particularly in the eyewall of TCs. Results also show that the simulated TC in CTL has slightly stronger maximum winds, slightly smaller radius of maximum wind (RMW), and ~5% smaller radius of gale-force wind than in NoADV. These differences are consistent with absolute angular momentum being advected to smaller radii in CTL. Sensitivity simulations further reveal that the differences between CTL and NoADV are more attributable to vertical advection (rather than horizontal advection) of TKE. Recommendations for improvements of PBL schemes that use predictive equations for TKE are also discussed.

Assessing the Turbulence Kinetic Energy Budget in the Boundary Layer Using WRF-LES: Impact of Momentum Perturbation

<p>Mesoscale-to-Large Eddy Simulation (LES) grid nesting is an important tool for many atmospheric model applications, ranging from wind energy to wildfire spread studies. Different techniques are used in such applications to accelerate the development of turbulence in the LES domain. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method (SCPM) to accelerate the generation of turbulence in the Weather Research and Forecasting (WRF) LES model on the Turbulence Kinetic Energy (TKE) budget. In a convective boundary layer, we study the variation of TKE budget terms under the initial conditions of the Scaled Wind Farm Technology (SWiFT) facility located in West Texas. In this study, WRF LES is used with a horizontal grid resolution of 12 m, and is one-way nested within an idealized mesoscale domain. It is crucial to understand how forced perturbation shifts the balance between the terms of the TKE budget. Here, we quantify the shear production, and buoyant production in an unstable case. Since additional production terms are introduced in the SCPM method, we investigate the dissipation term of TKE. In addition, we also study the generation of turbulent transport. Generally, it integrates over height to null in a planar homogeneous case without subsidence, indicating it is positive over some heights and negative over other heights. Furthermore, we also study the variation of the TKE transport term after extending the random perturbation up to a certain height. The findings of this study will provide a better understanding of the contribution of different budget terms in a forced LES simulation.</p>

Nonequilibrium scaling vs. the temporal evolution of turbulence

<p>Despite many airborne measurements and research campaigns our understanding of turbulence in free atmosphere is still far from sufficient. Part of the problem is the limited amount of measurement data, another part is measurement errors and last, but not least element is inadequate or not satisfactory data analysis. This presentation addresses some aspects of this last issue. The simplest way to characterize turbulence is to define/measure characteristic velocity U and length L (or time T) scales of turbulent  eddies. Two quantities necessary to estimate them are the turbulence kinetic energy K and the turbulence kinetic energy dissipation rate. A universal scaling relation between dissipation rate, turbulence kinetic energy and the turbulence length scale follows from the classical picture of the equilibrium Richardson-Kolmogorov cascade. There, the energy is transported between scales in a downward cascade until it is dissipated into heat by the smallest eddies. This universal scaling is a basis of many turbulence models, also in the context of atmospheric applications. However, a number of recent papers suggest that a universal, although different from the classical, scaling could also be observed in unsteady turbulent flows, which ate typical in the free atmosphere. In this study we investigate the nonequilibrium scaling relation between the integral length scale of turbulence and dissipation rate using velocity signals from various airborne measurements of atmospheric cloud turbulence, including that in and around convective clouds. A research aircraft measures 1D intersection of turbulent velocity field as a time series collected along the flight tajectory. Hence,  information on  temporal behavior (decay or development) of turbulence is not directly available. In this study we show how this important information can be recovered based on the observed scaling relations.</p>

Composited structure of shallow cumulus clouds

<div> <div> <div> <div> <p>The normalized distributions of thermodynamic and dynamical variables both within and outside shallow clouds are investigated through a composite algorithm using large eddy simulation of the BOMEX case. The normalized magnitude is maximum near cloud center and decreases outwards. While relative humidity (RH) and cloud liquid water (<em>q<sub>l </sub></em>) decrease smoothly to match the environment, the vertical velocity, virtual potential temperature (<em>θ<sub>v </sub></em>) and potential temperature (<em>θ</em>) perturbations have more complicated behaviour towards the cloud boundary. Below the inversion layer, <em>θ<sub>v</sub></em> becomes <span>negative before the vertical velocity has turned from updraft to subsiding shell outside the cloud, indicating the presence of a transition zone where the updraft is negatively buoyant. Due to the downdraft outside the cloud and the enhanced horizontal turbulent mixing across the edge, the normalized turbulence kinetic energy (TKE) and horizontal turbulence kinetic energy (HTKE) decrease more slowly from the cloud center outwards than the thermodynamic variables. The distributions all present asymmetric structures in response to the vertical wind shear, with more negatively buoyant air, stronger downdrafts and larger TKE on the downshear side. We discuss several implications of the distributions for theoretical models and parameterizations. Positive buoyancy near cloud base is mostly due to </span><span>the virtual effect of water vapor, emphasising the role of moisture in triggering. The mean vertical velocity is found </span><span>to be approximately half the maximum vertical velocity within each cloud, providing a constraint on some models. Finally, products of normalized distributions for different variables are shown to be able to well represent the vertical heat and moisture fluxes, but they underestimate fluxes in the inversion layer because they do not capture cloud top downdrafts.</span></p> </div> </div> </div> </div>

Multi-step wind variability prediction based on deep learning neural network

<p>Wind energy is widely used in renewable energy systems but the randomness and the intermittence of the wind make its accurate prediction difficult. This study develops an advanced and reliable model for multi-step wind variability prediction using long short-term memory (LSTM) network based on deep learning neural network (DLNN). A 20 Hz Ultrasonic anemometer was positioned in northern France (LOG site) to measure the random wind variability for the duration of thirty-four days. Real-time turbulence kinetic energy is computed from the measured wind velocity components, and multi-resolution features of wind velocity and turbulent kinetic energy are used as input for the prediction model. These multi-resolution features of wind variability are extracted using one-dimensional discrete wavelet transformation. The proposed DLNN is framed to implement multi-step prediction ranging from 10 min to 48 h. For velocity prediction, the root mean square error, mean absolute error and mean absolute percentage error are 0.047 m/s, 0.19 m/s, and 11.3% respectively. These error values indicate a good reliability of the proposed DLNN for predicting wind variability. We found that the present model performs well for mid-long-term (6-24h) wind velocity prediction. The model is also good for the long-term (24-48h) turbulence kinetic energy prediction.</p>