Analysis of offshore wind spectra and coherence under neutral stability condition using the two LES models PALM and SOWFA

The Parallelized Large-Eddy Model (PALM) and the Simulator for Wind Farm Applications (SOWFA) have been used to simulate the marine boundary layer flows under neutral stability condition. The present work aims to investigate the capability of the two models in reproducing the structure of turbulence in the offshore environment through comparative analysis with a focus on wind spectra and coherence. Wind spectra obtained from the two LES solvers agree well with the empirical spectral model near the surface but show lower turbulence intensity in the low frequency range above the surface layer. Both models also produce highly consistent estimates of coherence with different horizontal and vertical separations, which match well with Davenport and IEC coherence models at height of 180m and 140m respectively. As the height decreases, LES predicts lower vertical coherence compared with the IEC model and the fitted decay coefficient for Davenport model grows as the separation distance increases.publishedVersio

Multiscale Simulation of Offshore Wind Variability During Frontal Passage: Brief Implication on Turbines’ Wakes and Load

Enhancing the performance of offshore wind park power production requires, to a large extent, a better understanding of the interactions of wind farms and individual wind turbines with the atmospheric boundary layer over a wide range of spatiotemporal scales. In this study, we use a multiscale atmospheric model chain coupled offline with the aeroelastic Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code. The multiscale model contains two different components in which the nested mesoscale Weather and Research Forecast (WRF) model is coupled offline with the Parallelized Large-eddy Simulation Model (PALM). Such a multiscale framework enables to study in detail the turbine behaviour under various atmospheric forcing conditions, particularly during transient atmospheric events.publishedVersio

Development of an automatic thresholding method for wake meandering studies and its application to the data set from scanning wind lidar

Wake meandering studies require knowledge of the instantaneous wake evolution. Scanning lidar data are used to identify the wind flow behind offshore wind turbines but do not immediately reveal the wake edges and centerline. The precise wake identification helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with distance from the rotor and require good data resolution for an accurate fit. The thresholding methods, i.e., selection of a threshold that splits the data into background flow and wake, usually imply a fixed value or manual estimation, which hinders the wake identification on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and quality and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake identification, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The automatic thresholding method and the subsequent centerline search algorithm are developed to reduce dependency on the supplementary data such as free-flow wind speed and direction. We focus on the technical aspect of the method and show that the wake shape and centerline found from the thresholded data are in a good agreement with the manually detected centerline and the Gaussian fit method. We also briefly discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.publishedVersio

Self-nested large-eddy simulations in PALM Model System v21.10 for offshore wind prediction under different atmospheric stability conditions

Large-eddy simulation (LES) resolves large-scale turbulence directly and parametrizes small-scale turbulence. Resolving the micro-scale turbulence, e.g., in the wind turbine wakes, requires both a sufficiently small grid spacing and a domain large enough to develop the turbulent flow. Refining the grid locally via a nesting interface effectively decreases the required computational time compared to the global grid refinement. However, interpolating the flow between the nested grid boundaries introduces another source of uncertainty. Previous studies reviewed the nesting effects for a buoyancy-driven flow and observed a secondary circulation in the two-way nested area. Using nesting interface with a shear-driven flow in the wind field simulation, therefore, requires additional verification. We use PALM model system to simulate the boundary layer in a cascading self-nested domain under neutral, convective, and stable conditions, and verify the results based on the wind speed measurements taken at the FINO1 platform in the North Sea. We show that the feedback between the parent and child domain in a two-way nested simulation of a non-neutral boundary layer alters the circulation in the refined domain, despite the spectral characteristics following the reference measurements. Unlike the pure buoyancy-driven flow, the non-neutral shear-driven flow slows down in the two-way nested area and accelerates after exiting the child domain. We also briefly review the nesting effect on the velocity profiles and turbulence anisotropy.</p

On Stochastic Reduced-Order and LES-based Models of Offshore Wind Turbine Wakes

In this paper, the primary objective is to investigate flow structures in the wake of wind turbines based on applying a truncated Proper Orthogonal Decomposition (POD) approach. This scheme decomposes the three-dimensional velocity fields produced by the high-fidelity PArallelized LES Model (PALM) into a number of orthogonal spatial modes and time-dependent weighting coefficients. PALM has been combined with an actuator disk model with rotation to incorporate the effects of a turbine array. The time-dependent deterministic weights from applying the POD scheme are replaced by stochastic weights, estimated from two independent stochastic techniques that aim to account for unresolved small-scale features for a number of POD modes. We then reconstruct the flow field by a small number of stochastic modes to investigate how well the applied stochastic methodologies can reproduce the flow field compared to the original LES results.publishedVersio

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice

Analysis of offshore wind spectra and coherence under neutral stability condition using the two LES models PALM and SOWFA

The Parallelized Large-Eddy Model (PALM) and the Simulator for Wind Farm Applications (SOWFA) have been used to simulate the marine boundary layer flows under neutral stability condition. The present work aims to investigate the capability of the two models in reproducing the structure of turbulence in the offshore environment through comparative analysis with a focus on wind spectra and coherence. Wind spectra obtained from the two LES solvers agree well with the empirical spectral model near the surface but show lower turbulence intensity in the low frequency range above the surface layer. Both models also produce highly consistent estimates of coherence with different horizontal and vertical separations, which match well with Davenport and IEC coherence models at height of 180m and 140m respectively. As the height decreases, LES predicts lower vertical coherence compared with the IEC model and the fitted decay coefficient for Davenport model grows as the separation distance increases

Evaluation of Gaussian wake models under different atmospheric stability conditions: Comparison with large eddy simulation results

The calculation of the velocity deficit in the wake of individual wind turbines is a fundamental part of the wind farm analysis. A good approximation of the wake deficit behind a single wind turbine will improve the power estimation for downwind turbines. Large-eddy simulation (LES) is a research tool widely used in studying the velocity deficit and turbulence intensity in the wake. However, the computational cost of the LES prevents its application in wind farm performance analysis and control. Existing analytical wake models provide a fast estimation of the velocity deficit and the wake expansion rate downstream from the rotor. The Gaussian wake models use a Gaussian distribution to improve the prediction of the wake velocity deficit. With the number of analytical models available, an extensive evaluation of their performance under different flow parameters is needed. In this work, we simulate a wake of a single wind turbine using the LES code PALM (Parallelized LES Model) combined with an actuator disc model with rotation. We compare the computed flow field with the predictions made by Gaussian models and fit their parameters to obtain the best possible fit for the wake field data as computed by LES

Development of an automatic thresholding method for wake meandering studies and its application to the data set from scanning wind lidar

Wake meandering studies require knowledge of the instantaneous wake evolution. Scanning lidar data are used to identify the wind flow behind offshore wind turbines but do not immediately reveal the wake edges and centerline. The precise wake identification helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with distance from the rotor and require good data resolution for an accurate fit. The thresholding methods, i.e., selection of a threshold that splits the data into background flow and wake, usually imply a fixed value or manual estimation, which hinders the wake identification on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and quality and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake identification, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The automatic thresholding method and the subsequent centerline search algorithm are developed to reduce dependency on the supplementary data such as free-flow wind speed and direction. We focus on the technical aspect of the method and show that the wake shape and centerline found from the thresholded data are in a good agreement with the manually detected centerline and the Gaussian fit method. We also briefly discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction

On Stochastic Reduced-Order and LES-based Models of Offshore Wind Turbine Wakes

In this paper, the primary objective is to investigate flow structures in the wake of wind turbines based on applying a truncated Proper Orthogonal Decomposition (POD) approach. This scheme decomposes the three-dimensional velocity fields produced by the high-fidelity PArallelized LES Model (PALM) into a number of orthogonal spatial modes and time-dependent weighting coefficients. PALM has been combined with an actuator disk model with rotation to incorporate the effects of a turbine array. The time-dependent deterministic weights from applying the POD scheme are replaced by stochastic weights, estimated from two independent stochastic techniques that aim to account for unresolved small-scale features for a number of POD modes. We then reconstruct the flow field by a small number of stochastic modes to investigate how well the applied stochastic methodologies can reproduce the flow field compared to the original LES results