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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution

Optimal wind power plant design requires understanding of wind turbine wake physics and validation of engineering wake models under wake-controlled operating conditions. In this work, we have developed and investigated several different wake identification and characterization methods for analyzing wake evolution and dynamics. The accuracy and robustness of these methods, based on Gaussian function fitting and adaptive contour identification, have been assessed by application to a large-eddy simulation data set. A new contour-based method based on downstream momentum deficit has been considered. Uncertainties arising from wake-identification errors result in characterizations of the wake expansion, recovery, and meandering motion that differ by 19% of the rotor area, 4% of the freestream, and 15% rotor diameter, respectively.

17 WIND ENERGY↗
DOE OSTI2022

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗
DOE OSTI2021

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

17 WIND ENERGY↗
DOE OSTI2020

Relationships Between Precipitation Properties and Large-Scale Conditions During Subsidence at the Eastern North Atlantic Observatory

Three years of reanalysis and ground-based observations collected at the Eastern North Atlantic (ENA) observatory are analyzed to document the properties of rain and boundary layer clouds, and their relationship with the large-scale environment during general subsidence conditions and following cold front passages. Clouds in the wake of cold fronts exhibit on average a 10% higher propensity to precipitate and higher rain-to-cloud fraction than cloud found in general subsidence conditions. Similarities in the seasonal cycle of rain and of large-scale properties suggest that the large-scale conditions created by the cold front passage are responsible for the unique properties of the rain forming in its wake. The identification of monotonic relationships between rain-to-cloud fraction and rain rate with surface forcing and boundary layer stability parameters as well as between virga base height with stability and humidity measures further supports that large-scale conditions impact precipitation variability. Likewise, these relationships between the large-scale and rain properties are less clear than those established between cloud and rain properties suggesting that cloud macrophysics have a more direct impact on the properties of rain than the large-scale environment. The applicability of previously documented relationships between rain properties and cloud thickness is tested and the relationships adjusted to accommodate the complex shallow clouds and melting precipitation observed to occur in the ENA region. Furthermore, establishing these relationships opens up opportunities for parametrization development and suggest that a realistic representation of precipitation properties in models relies on the accurate representation of both clouds and the large-scale environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗
DOE OSTI2024

Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign

Abstract. High-fidelity flow modeling with data assimilation enables accurate representation of the wind farm operating environment under realistic, nonstationary atmospheric conditions. Capturing the temporal evolution of the turbulent atmospheric boundary layer is critical to understanding the behavior of wind turbines under operating conditions with simultaneously varying inflow and control inputs. This paper has three parts: the identification of a case study during a field evaluation of wake steering; the development of a tailored mesoscale-to-microscale coupling strategy that resolved local flow conditions within a large-eddy simulation (LES), using observations that did not completely capture the wind and temperature fields throughout the simulation domain; and the application of this coupling strategy to validate high-fidelity aeroelastic predictions of turbine performance and wake interactions with and without wake steering. The case study spans 4.5 h after midnight local time, during which wake steering was toggled on and off five times, achieving yaw offset angles ranging from 0 to 17°. To resolve nonstationary nighttime conditions that exhibited shear instabilities, the turbulence field was evolved starting from the diurnal cycle of the previous day. These background conditions were then used to drive wind farm simulations with two different models: an LES with actuator disk turbines and a steady-state engineering wake model. Subsequent analysis identified two representative periods during which the up- and downstream turbines were most nearly aligned with the mean wind direction and had observed yaw offsets of 0 and 15°. Both periods corresponded to partial waking on the downstream turbine, which had errors in the LES-predicted power of 4 % and 6 %, with and without wake steering. The LES was also able to capture conditions during which an upstream turbine wake induced a speedup at a downstream turbine and increased power production by up to 13 %.

17 WIND ENERGY↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2021

Experimental Measurements of the Wake of a Sphere at Subcritical Reynolds Numbers

This work experimentally investigated the flow phenomena and vortex structures in the wake of a sphere located in a water loop at Reynolds numbers of Re = 850, 1,250, and 1,700. Velocity fields in the wake region were obtained by applying the time-resolved stereoscopic particle image velocimetry (TR-SPIV) technique. From the acquired TR-SPIV velocity vector fields, the statistical values of mean and fluctuating velocities were computed. Spectral analysis, two-point velocity–velocity cross-correlation, proper orthogonal decomposition (POD) and vortex identification analyses were also performed. The velocity fields show a recirculation region that decreases in length with an increase of Reynolds numbers. The power spectra from the spectral analysis had peaks corresponding to a Strouhal number of St = 0.2, which is a value commonly found in the literature studies of flow over a sphere. The two-point cross-correlation analysis revealed elliptical structures in the wake, with estimated integral length scales ranging between 12% and 63% of the sphere diameter. The POD analysis revealed the statistically dominant flow structures that captured the most flow kinetic energy. It is seen that the flow kinetic energy captured in the smaller scale flow structures increased as Reynolds number increased. The POD modes contained smaller structure as the Reynolds number increased and as mode order increased. In addition, spectral analysis performed on the POD temporal coefficients revealed peaks corresponding to St = 0.2, similar to the spectral analysis on the fluctuating velocity. The ability of POD to produce low-order reconstructions of the flow was also utilized to facilitate vortex identification analysis, which identified average vortex sizes of 0.41D for Re1, 0.33D for Re2, and 0.32D for Re3.

Engineering↗
DOE OSTI2024

Imaging the wakes of jets with energy-energy-energy correlators

As the partons in a high energy jet propagate through the droplet of quark-gluon plasma (QGP) produced in a heavy-ion collision they lose energy to, kick, and are kicked by the medium. The resulting modifications to the parton shower encode information about the microscopic nature of QGP. A direct consequence, however, is that the momentum and energy lost by the parton shower are gained by the medium and, since QGP is a strongly coupled liquid, this means that the jet excites a wake in the droplet of QGP. After freezeout, this wake becomes soft hadrons with net momentum in the jet direction meaning that what an experimentalist later reconstructs as a jet includes hadrons originating from both the modified parton shower and its wake. This has made it challenging to find experimental observables that provide an unambiguous view of the dynamical response of a droplet of QGP to a jet shooting through it. Recent years have seen significant substantial advances in the theoretical and experimental understanding of the substructure of jets, in particular, using correlation functions, $\langle$ ($\vec{n}$ 1 )•••$\mathcal{E}$($\vec{n}$ k ) $\rangle$, of the energy flux operator in proton-proton collisions and, recently, in heavy-ion collisions. So far, such studies have focused primarily on the two-point correlator, which allows for the identification of the angular scale of the underlying dynamics. Higher-point correlators hold the promise of mapping out the dynamics themselves. In this paper we perform the first study of the shape-dependent three-point energy-energy-energy correlator in heavy-ion collisions. Using the Hybrid Model to simulate the interactions of high energy jets with the QGP medium, we show that the three-point correlator presents us with a striking new opportunity. We find that hadrons originating from wakes are the dominant contribution to the three-point correlator in the kinematic regime in which the three points are well-separated in angle, forming a roughly equilateral triangle. This equilateral region of the correlator is far from the region populated by collinear vacuum emissions, making it a canvas on which jet wakes are laid out, where experimentalists can map their shapes. Our work provides a key step towards the systematic use of energy correlators to image and unravel the dynamical response of a droplet of QGP that has been probed by a passing jet, and motivates numerous experimental and theoretical studies.

Jets and Jet Substructure↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗
DOE OSTI2021

Cylinders and jets in crossflow: Wake formations as a result of varying geometric proximities

The combined flow physics of several canonical flow configurations is experimentally studied. Here, we analyze an array of jets issuing into a crossflow, then immediately navigating past an array of cylinders. This is achieved with a 2 × 3 triangular pattern of jets and symmetric cylinders at three jets to crossflow velocity ratios, enabling near-complete optical access of each jet, with velocities measured by time-resolved particle image velocimetry. Jet trajectories reveal that each configuration adheres to a power-law trend and that greater penetration is achieved by the downstream and confined jets compared to the more conventional upstream one. Recirculation regions of the upstream and downstream jets are nearly independent, with the confined jet encompassing regions of overlap with both. Turbulent statistics reveal the influence of geometric placement and velocity ratio on the time-averaged velocity, anisotropy, and Reynolds stresses incurred by each jet. Galilean decomposition utilizes a supplemental crossflow-only velocity field to delineate the influence of each jet's low- and high-pressure regions on the otherwise uniform stream. Proper orthogonal decomposition suggests that increased jet penetration decreases the number of modes required for truncation in the investigated spanwise plane. Vortex identification algorithms are applied to the reconstructed flow fields, reaffirming that with increasing velocity ratio, the jets generate vortices of their own in similar statistical formations as the cylinders. This investigation provides a foundation to aid future modeling efforts toward characterizing flow physics of importance in designing and passively controlling transverse jets with varying blockage proximities in a crossflow.

Kristo, Paul J. (ORCID:0000000225605175)↗
DOE OSTI2021

Looking through the photoionisation wake: Vela X-1 at φ orb ≈ 0.75 with Chandra /HETG

In this work, we detect and identify five narrow radiative recombination continua (Mg XI-XII, Ne IX-X, O VIII) and several emission lines from Fe, S, Si, Mg, Ne, Al, and Na, including four He-like triplets (S XV, Si XIII, Mg XI, and Ne IX). Photoionisation models reproduce the overall spectrum well, except for the near-neutral fluorescence lines of Fe, S, and Si. We conclude that the plasma is mainly photoionised, but more than one component is most likely present, which is consistent with a multi-phase plasma scenario, where denser and colder clumps of matter are embedded in the hot, photoionised wind of the companion star. Simulations with the future X-ray satellites Athena and XRISM show that a few hundred seconds of exposure is sufficient to disentangle the lines of the Fe Kα doublet and the He-like Fe XXV, improving, in general, the determination of the plasma parameters.

79 ASTRONOMY AND ASTROPHYSICS↗