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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Gaussian FLOWERS: Wind-rose-based analytical integration of Gaussian wake model for extremely fast AEP estimation

A major cost in the study of wind farm layout optimization is the repeated evaluation of the annual energy production (AEP). The current approach to estimating AEP requires a large set of flow simulations to be performed that cover each discrete wind speed and direction combination contained within the wind rose, followed by a probability-weighted sum of the power production resulting from each simulation. Even with inexpensive engineering wake models, this numerical integration scheme can lead to high computational costs. In this paper, we derive an analytical formulation for estimating farm AEP across every wind direction, based on a Gaussian wake velocity model, which reduces the number of wind farm simulations to a single function evaluation. As a result, we find that the Gaussian-FLOWERS approach reduces the time for AEP calculations by more than two orders of magnitude with a small trade-off in accuracy when compared to a conventional approach. This massive reduction in computation cost is useful to reduce overall costs in wind farm layout optimization studies.

17 WIND ENERGY↗
DOE OSTI2017

Large-eddy simulations of wind-farm wake characteristics associated with a low-level jet

Here, we performed a suite of flow simulations for a 12-wind-turbine array with varying inflow conditions and lateral spacings, and compared the impacts of the flow on velocity deficit and wake recovery. We imposed both laminar inflow and turbulent inflows, which contain turbulence for the Ekman layer and a low-level jet (LLJ) in the stable boundary layer. To solve the flow through the wind turbines and their wakes, we used a large-eddy simulation technique with an actuator-line method. We compared the time series for the velocity deficit at the first and rear columns to observe the temporal change in velocity deficit for the entire wind farm. The velocity deficit at the first column for LLJ inflow was similar to that for laminar inflow. However, the magnitude of velocity deficit at the rear columns for the case with LLJ inflow was 11.9% greater because of strong wake recovery, which was enhanced by the vertical flux of kinetic energy associated with the LLJ. In order to observe the spatial transition and characteristics of wake recovery, we performed statistical analyses of the velocity at different locations for both the laminar and LLJ inflows. These studies indicated that strong wake recovery was present, and a kurtosis analysis showed that the probability density function for the streamwise velocity followed a Gaussian distribution. In a quadrant analysis of the Reynolds stress, we found that the ejection and sweep motions for the LLJ inflow case were greater than those for the laminar inflow case.

17 WIND ENERGY↗
DOE OSTI2024

Predicting turbulent wake flow of marine hydrokinetic turbine arrays in large-scale waterways via physics-enhanced convolutional neural networks

We present a physics-enhanced convolutional neural network (PECNN) algorithm for reconstructing the mean flow and turbulence statistics in the wake of marine hydrokinetic (MHK) turbine arrays installed in large-scale meandering rivers. The algorithm embeds the mass and momentum conservation equations into the loss function of the PECNN algorithm to improve the physical realism of the reconstructed flow fields. The PECNN is trained using large eddy simulation (LES) results of the wake flow of a single row of turbines in a virtual meandering river. Subsequently, the trained PECNN is applied to predict the wake flow of MHK turbines with arrangements and positionings different than those considered during the training process. The PECNN predictions are validated using the results of separately performed LES. The results show that the PECNN algorithm can accurately predict the wake flow of MHK turbine farms at a small fraction of the cost of LES. The PECNN can improve the accuracy by around 1% and reduce the physical constraint indices by around 50% compared to the CNN without physical constraints. This work underscores the potential of PECNN to develop reduced-order models for control co-design and optimization of MHK turbine arrays in natural riverine environments.

Mechanics↗
DOE OSTI2023

A Three-Dimensional, Analytical Wind Turbine Wake Model: Flow Acceleration, Empirical Correlations, and Continuity

A new, three-dimensional, analytical, steady-state wake model is presented that includes local flow acceleration near the rotor, improving the wake description compared to existing models. Wake structures such as the momentum deficit and regions of accelerated flow are concisely described with compound and normal Gaussian functions. Large-eddy simulations (LES) are used as training data to develop the model using two in-line turbines under various inflow conditions parameterized by hub-height wind speed and turbulence intensity. Mass conservation is considered by fixing two components of the wake velocity model and optimizing the third to best satisfy continuity; after which, the model performs comparably if not better than existing work with regards to both relative error and mass consistency. The final model demonstrates a high degree of flexibility, making use of empirical correlations to scale across different inflow conditions. The inclusion of these effects is capable of revealing unused opportunities for enhanced power generation by aligning wake trajectories with these regions of accelerated flow.

modeling↗
DOE OSTI2025

Search for the jet-induced diffusion wake in the quark-gluon plasma via measurements of jet-track correlations in photon-jet events in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ATLAS detector

This paper presents a measurement of jet-track correlations in photon-jet events, using 1.72 nb −1 of Pb+Pb data at $\sqrt{s_{NN}}$ = 5.02TeV recorded with the ATLAS detector at the LHC. Events with energetic photon-jet pairs are selected, where the photon and jet are approximately back-to-back in azimuth. The angular correlation between jets and charged-particle tracks with transverse momentum (𝑝 T ) in the range 0.5–2.0 GeV in the hemisphere opposite to the jet, |Δ⁢𝜙(jet,track)|>𝜋/2, is measured as a function of their relative pseudorapidity difference, |Δ⁢𝜂(jet,track)|. In central Pb+Pb collisions, these correlations are predicted to be sensitive to the diffusion wake in the quark-gluon plasma resulting from the lost energy of high-𝑝T partons traversing the plasma, with a characteristic modification as a function of |Δ⁢𝜂(jet,track)|. The correlations are examined with different selections on the jet-to-photon 𝑝 T ratio to select events with different degrees of energy loss. No diffusion wake signal is observed within the current sensitivity and upper limits at 95% confidence level on the diffusion wake amplitude are reported.

heavy flavor physics↗
DOE OSTI2022

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Here, we use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of epistemic uncertainty. This capability supports its use for a wide range of problems in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Correlation between ferroelectricity and ferroelectric orthorhombic phase of Hf x Zr 1-x O 2 thin films using synchrotron x-ray analysis

The change in the interplanar spacing ( d -spacing) including the ferroelectric orthorhombic (O) phase in the low-temperature fabricated Hf x Zr 1-x O 2 (HZO) films was studied using synchrotron grazing-incidence wide-angle x-ray scattering analysis. The 10-nm-thick HZO films were fabricated by thermal and plasma-enhanced atomic layer deposition (TH- and PE-ALD) methods using H 2 O gas and O 2 plasma as oxidants, respectively, and a post-metallization annealing (PMA) was performed at 300–400 °C. The d -spacing of the mixture of (111)-, (101)-, and (111)-planes of O, tetragonal (T), and cubic (C) phases, respectively, for the TH- and PE-ALD HZO films increased up to 2.99 Å with an increase in PMA temperature, while the d -spacing estimated by conventional x-ray diffraction was 2.92 Å regardless of the PMA temperature. The remanent polarization (2 P r = P r + - P r - ) of the HZO films increased as the PMA temperature increased. It is clear that the 2 P r value satisfied a linear relationship as a function of the d -spacing of O(111)/T(101)/C(111) phases. Furthermore, the wake-up effect was found to depend on the ferroelectric O phase formation. The wake-up effect was significantly reduced in both the TH- and PE-ALD HZO films after the PMA at 400 °C due to the increase in the ferroelectric O phase formation. The leakage current density ( J )–electric field properties of the PE-ALD HZO film with the lowest d -spacing were divided into three steps, such as low, middle, and large J values, in the wake-up (10 3 cycles), pristine (10 0 cycle), and fatigue (10 7 cycles) states, respectively. Therefore, an analysis of the ferroelectric O phase is very important for understanding the ferroelectricity including endurance.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Wind Plant Power Optimization through Yaw Control using a Parametric Model for Wake Effects -- A CFD Simulation Study

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects, called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high-fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect.

17 WIND ENERGY↗
DOE OSTI2017

Analytic Characterization of the Wake Behind In-Stream Hydrokinetic Turbines

Analytical algorithms developed and optimized for quantifying the wake behind in-stream hydrokinetic turbines are presented. These algorithms are based on wake expressions originally developed for wind turbines. Unlike previous related studies, the optimization of empirical coefficients contained in these algorithms is conducted using centerline velocity data from multiple published experimental studies of the wake velocities behind in-stream hydrokinetic turbine models or porous disks and not using computational fluid dynamics. Empirical coefficients are first individually optimized based on each set of experimental data, and then empirically based coefficient expressions are created using all of the data sets collectively, such that they are functions of ambient turbulence intensity. This expands the applicability of the created algorithms to cover the expected range of operating conditions for in-stream hydrokinetic turbines. Wind turbine wake model expressions are also modified to characterize the dependence of wake velocities on radial location from the centerline of in-stream hydrokinetic turbines. Thus, expressions with empirically optimized coefficients for calculating wake velocities behind in-stream hydrokinetic turbines are described in terms of both centerline and radial positions. Wake predictions made using the Larsen model for radial dependence are shown to diverge from experimental measurements near the wake radius defined by the Jensen model, suggesting that this is a good indication of the cutoff point beyond which numerical estimations no longer apply. Results suggest that using a combined Larsen/Ainslie approach or combined Jensen/Ainslie approach for characterizing wake have similar mean errors to using only a Larsen approach.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

An Advanced Actuator Line Method for Wind Energy Applications and Beyond: Preprint

The actuator line method to represent rotor aerodynamics within computational fluid dynamics has been in use for over a decade. This method applies a body force to the flow field along rotating lines corresponding to the individual rotor blades and employs tabular airfoil data to compute the force distribution. The actuator line method is attractive because compared to blade-resolved simulations, the required mesh is much simpler and the computational cost is lower. This work proposes a higher fidelity variant of the actuator line method meant to fill the space between current actuator line and blade-resolved simulations. It contains modifications in two key areas. The first is that of freestream velocity vector estimation along the line, which is necessary to compute the lift and drag along the line using tabular airfoil data. Most current methods rely on point sampling in which the location of sampling is ambiguous. Here we test a velocity sampling method that uses a properly weighted integral over space, removing this ambiguity. The second area of improvement is the function used to project the one-dimensional actuator line force onto the three-dimensional fluid mesh as a body force. We propose and test a projection function that spreads the force over a region that looks something like a real blade with the hope that it will produce the blade local and near wake flow features with more accuracy and higher fidelity. Our goal is that between these two improvements, not only will the flow field predictions be enhanced, but also the spanwise loading will be made more accurate. We refer to this combination of improvements as the advanced actuator line method. We apply these improvements to two different wind turbine cases. Although there is a strong wind energy motivation in our work, there is no reason these advanced actuator line ideas cannot be used in other applications, such as helicopter rotors.

17 WIND ENERGY↗
DOE OSTI2015

DYNAMICS OF SELF-GRAVITY WAKES IN DENSE PLANETARY RINGS. I. PITCH ANGLE

We investigate the dynamics of self-gravity wakes in dense planetary rings. In particular, we examine how the pitch angles of self-gravity wakes depend on ring parameters using N-body simulations. We calculate the pitch angles using the two-dimensional autocorrelation function of the ring surface density. We obtain the pitch angles for the inner and outer parts of the autocorrelation function separately. We confirm that the pitch angles are 15°–30° for reasonable ring parameters, which are consistent with previous studies. We find that the inner pitch angle increases with the Saturnicentric distance, while it barely depends on the optical depth and the restitution coefficient of ring particles. The increase of the inner pitch angle with the Saturnicentric distance is consistent with the observations of the A ring. The outer pitch angle does not have a clear dependence on any ring parameters and is about 10°–15°. This value is consistent with the pitch angle of spiral arms in collisionless systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Three-dimensional analysis of microbunched electron cooling

Microbunched electron cooling (MBEC, Ratner, ), a special category of coherent electron cooling, has been under consideration as a candidate method for the strong hadron cooling component of the Electron Ion Collider (EIC) at BNL. Most of the theoretical treatments of this scheme so far have been based on simplified models for the key space charge effect in which electrons and/or hadrons are approximated by charged disks. Although such disk-based models provide a flexible tool that captures a good part of the underlying physics, they lack the fidelity and robustness of a rigorous analysis that explicitly treats the hadron and cooler electron beams as collections of point charges. In this paper, we present such a rigorous, three-dimensional (3D) model, both from a theory-based and a simulation perspective. Specifically, we calculate the generalized wakefield (or Green’s function) of the cooling system—a crucial quantity that determines the overall performance—paying special attention to the dependence of the wake on the hadron transverse positions. Additionally, our treatment takes into account previously neglected 3D effects that can cause damping of the plasma oscillations in the amplification section of the cooler, such as focusing and the angular spread of the electron beam. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Multifidelity multiobjective optimization for wake-steering strategies

Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

17 WIND ENERGY↗
DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

17 WIND ENERGY↗
DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations

We describe the sample design for the SDSS-IV MaNGA survey and present the final properties of the main samples along with important considerations for using these samples for science. Our target selection criteria were developed while simultaneously optimizing the size distribution of the MaNGA integral field units (IFUs), the IFU allocation strategy, and the target density to produce a survey defined in terms of maximizing signal-to-noise ratio, spatial resolution, and sample size. Our selection strategy makes use of redshift limits that only depend on i-band absolute magnitude (M {sub i}), or, for a small subset of our sample, M {sub i} and color (NUV − i). Such a strategy ensures that all galaxies span the same range in angular size irrespective of luminosity and are therefore covered evenly by the adopted range of IFU sizes. We define three samples: the Primary and Secondary samples are selected to have a flat number density with respect to M {sub i} and are targeted to have spectroscopic coverage to 1.5 and 2.5 effective radii (R {sub e}), respectively. The Color-Enhanced supplement increases the number of galaxies in the low-density regions of color–magnitude space by extending the redshift limits of the Primary sample in the appropriate color bins. The samples cover the stellar mass range 5×10{sup 8}⩽M{sub ∗}⩽3×10{sup 11} M{sub ⊙} h{sup −2} and are sampled at median physical resolutions of 1.37 and 2.5 kpc for the Primary and Secondary samples, respectively. We provide weights that will statistically correct for our luminosity and color-dependent selection function and IFU allocation strategy, thus correcting the observed sample to a volume-limited sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Fracture dynamics in implanted silicon

Crack propagation in implanted silicon for thin layer transfer is experimentally studied. The crack propagation velocity as a function of split temperature is measured using a designed optical setup. Interferometric measurement of the gap opening is performed dynamically and shows an oscillatory crack “wake” with a typical wavelength in the centimetre range. The dynamics of this motion is modelled using beam elasticity and thermodynamics. The modelling demonstrates the key role of external atmospheric pressure during crack propagation. A quantification of the amount of gas trapped inside pre-existing microcracks and released during the fracture is made possible, with results consistent with previous studies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗