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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

17 WIND ENERGY↗
DOE OSTI2013

Using Mesoscale Weather Model Output as Boundary Conditions for Atmospheric Large-Eddy Simulations and Wind-Plant Aerodynamic Simulations (Presentation)

Wind plant aerodynamics are directly affected by the microscale weather, which is directly influenced by the mesoscale weather. Microscale weather refers to processes that occur within the atmospheric boundary layer with the largest scales being a few hundred meters to a few kilometers depending on the atmospheric stability of the boundary layer. Mesoscale weather refers to large weather patterns, such as weather fronts, with the largest scales being hundreds of kilometers wide. Sometimes microscale simulations that capture mesoscale-driven variations (changes in wind speed and direction over time or across the spatial extent of a wind plant) are important in wind plant analysis. In this paper, we present our preliminary work in coupling a mesoscale weather model with a microscale atmospheric large-eddy simulation model. The coupling is one-way beginning with the weather model and ending with a computational fluid dynamics solver using the weather model in coarse large-eddy simulation mode as an intermediary. We simulate one hour of daytime moderately convective microscale development driven by the mesoscale data, which are applied as initial and boundary conditions to the microscale domain, at a site in Iowa. We analyze the time and distance necessary for the smallest resolvable microscales to develop.

17 WIND ENERGY↗
DOE OSTI2023

AWAKEN Wind Plant Simulation Comparison

A series of numerical simulations of wind farms, using different model fidelities and for different atmospheric stability conditions, were performed as a part of the American WAKE ExperimeNt. The simulations included using FLORIS wake models, a number of microscale AMR-Wind and Nalu-Wind runs, as well as idealized and complex terrain WRF runs. The largest computations used the AMR-Wind LES solver to simulate a 100 km x 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements, while other LES computations focused on sections of the King Plains wind farm. Results of this qualitative comparison illustrate the interactions with wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

17 WIND ENERGY↗
DOE OSTI2016

Computational Fluid Dynamics Simulation Study of Active Power Control in Wind Plants

This paper presents an analysis performed on a wind plant's ability to provide active power control services using a high-fidelity computational fluid dynamics-based wind plant simulator. This approach allows examination of the impact on wind turbine wake interactions within a wind plant on performance of the wind plant controller. The paper investigates several control methods for improving performance in waked conditions. One method uses wind plant wake controls, an active field of research in which wind turbine control systems are coordinated to account for their wakes, to improve the overall performance. Results demonstrate the challenge of providing active power control in waked conditions but also the potential methods for improving this performance.

17 WIND ENERGY↗
DOE OSTI2017

Third-Year Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project

The goal of the mesoscale-to-microscale coupling (MMC) project is to build new high-performance-computing-based multiscale wind-plant simulation tools that couple a broad range of scales to enable the optimization of wind plants to ensure the efficient, reliable production and integration of future wind-generated electricity. To meet this goal, the project seeks to create, assess, and validate state-of-the-science atmospheric simulation methodologies to incorporate important mesoscale flow characteristics into microscale wind-plant simulations.

17 WIND ENERGY↗
DOE OSTI2019

Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project (FY 2018)

The goal of the Mesoscale-to-Microscale Coupling (MMC) project is to build new high-performance-computing-based multiscale wind-plant simulation tools that couple a broad range of scales to enable the optimization of wind plants to ensure the efficient, reliable production and integration of future wind-generated electricity. To meet this goal, the project seeks to create, assess, and validate state-of-the-science atmospheric simulation methodologies to incorporate important mesoscale flow characteristics into microscale wind-plant simulations.

17 WIND ENERGY↗
DOE OSTI2017

Development of a Wind Plant Large-Eddy Simulation with Measurement-Driven Atmospheric Inflow: Preprint

This paper details the development of an aeroelastic wind plant model with large-eddy simulation (LES). The chosen LES solver is the Simulator for Wind Farm Applications (SOWFA) based on the OpenFOAM framework, coupled to NREL's comprehensive aeroelastic analysis tool, FAST. An atmospheric boundary layer (ABL) precursor simulation was constructed based on assessments of meteorological tower, lidar, and radar data over a 3-hour window. This precursor was tuned to the specific atmospheric conditions that occurred both prior to and during the measurement campaign, enabling capture of a night-to-day transition in the turbulent ABL. In the absence of height-varying temperature measurements, spatially averaged radar data were sufficient to characterize the atmospheric stability of the wind plant in terms of the shear profile, and near-ground temperature sensors provided a reasonable estimate of the ground heating rate describing the morning transition. A full aeroelastic simulation was then performed for a subset of turbines within the wind plant, driven by the precursor. Analysis of two turbines within the array, one directly waked by the other, demonstrated good agreement with measured time-averaged loads.

17 WIND ENERGY↗
DOE OSTI2019

Benchmarks for Model Validation based on LiDAR Wake Measurements

Technology development and design decisions in wind energy are often based on results from simulations performed for individual wind turbines or entire wind plants. It is therefore critical to ensure that the models being used for research and industry applications in wind energy be thoroughly validated against measurements. A full-system validation of wind plant simulations must consider the atmospheric inflow, the response of the wind turbines, and their wakes. This task is complicated by the lack of freely available, quality-controlled, high-quality measurements. Here, such measurements are used to offer a validation exercise that can be used to assess the accuracy of models of any fidelity level. When it comes to real-world measurements, the dataset considered herein is simple in terms of terrain but exhibits pronounced diurnal cycles. Instead of a full-scale wind plant, we consider an individual research-scale, utility wind turbine instrumented for power and loads measurements. Three benchmarks are defined, with increasing levels of complexity: near neutral, slightly unstable, and very stable atmospheric stratification. Through comparisons between observations and simulations, the benchmarks provide complementary information about the model performance and its ability to reproduce mean and dynamic wake characteristics. This article describes the measurements and methodology used to define these benchmarks and provides the information required to perform simulations and conduct the model-measurement comparison. The objective is to provide a robust wake model validation exercise open to anyone, which will serve to minimize uncertainty in model validation practices related to varying methodologies across simulation tools and users.

17 WIND ENERGY↗
DOE OSTI2018

Enrichment methods for inflow turbulence generation in the atmospheric boundary layer

Here, we investigate the feasibility of introducing synthetic turbulence into finite-domain large-eddy simulations (LES) of the wind plant operating environment. This effort is motivated by the need for a robust mesoscale-to-microscale coupling strategy in which a microscale (wind plant) simulation is driven by mesoscale data without any resolved microscale turbulence. A neutrally stratified atmospheric boundary layer was simulated in an LES with 10-m grid spacing. We show how such a fully developed turbulence field may be reproduced with spectral enrichment starting from an under-resolved coarse LES field (with 20-m and 40-m grid spacing). The velocity spectra of the under-resolved fields are enriched by superimposing a fluctuating velocity field calculated by two turbulence simulators: TurbSim and Gabor Kinematic Simulation. Both forms of enrichment accurately simulated the autospectra of all three velocity components at high wavenumbers, with agreement between the enriched fields and the full-resolution LES observed at 400 m from the inflow boundary. In contrast, the spectra of the unenriched fields reached the same fully developed state at four times the downstream distance.

17 WIND ENERGY↗
DOE OSTI2020

Multiscale Framework Simulates Utility-Scale Wind Plant in Its Natural Environment. First-ever simulation could lead to a vital understanding of wind plant performance

Wind plant performance depends on local environmental conditions—and understanding those conditions is crucial to research on wind plant dynamics. Factors such as sloping terrain, surface type and cover, atmospheric heating and cooling, and weather events all play a role in determining the winds that turbines convert into power. However, most models used for wind plant design and operation do not sufficiently account for environmental effects. This can limit our understanding of wind plant dynamics and, thus, our ability to design, site, and operate wind plants for maximum power production and reliability. To meet this challenge, Arthur and other LLNL researchers partnered with scientists at the National Center for Atmospheric Research; the University of California, Berkeley; the University of Colorado, Boulder; and Texas Tech University to develop and demonstrate a novel, mesoscale-tomicroscale wind plant modeling framework.

17 WIND ENERGY↗
DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 1: Atmosphere and Plant

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 1 covers the computing challenges in full turbine modelling, then discusses bridging scales in the atmosphere and turbulence modelling, wind forecasting, wind plant flow, and plant level controller design.

17 WIND ENERGY↗
DOE OSTI2023

Investigation of Wind Plant Wake Effects at the AWAKEN Field Campaign

Wind plant wakes characterized by lower momentum flow with increased turbulent kinetic energy (TKE) have been shown to propagate for several kilometers and interact with neighboring plants. To better understand these interactions, an Oklahoma site hosting five wind plants is heavily instrumented as part of the American Wake Experiment (AWAKEN) field campaign. Upstream, interior-plant, and downstream flows are probed using a suite of remote sensing instruments to quantify wake properties such as the magnitude and extent of momentum deficit and TKE increase. The effects of atmospheric inflow (wind speed, turbulence, stability, etc.) are investigated. Under certain conditions, the impact of an upstream plant on its downstream neighbor can be observed using the performance data recorded by the downstream plant. In addition to providing insight into plant wake behavior, the results of this study will be used as a benchmark case for wind plant simulations.

AWAKEN↗
DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗
DOE OSTI2013

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather prediction model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.

17 WIND ENERGY↗
DOE OSTI2019

Chapter 6: Modeling and Simulation of Wind-Farm Flows

The subject of this chapter is the modeling and simulation of the flow through full wind farms, also known as wind plants, which are collections of many wind turbines within a region working together to supply power to the grid like any other type of power plant. This topic is interesting and exciting because of the increasing variety of and uses for wind plant flow modeling and simulation. For example, Figure 6.1 shows the computed flow field from a high-fidelity simulation of the Lillgrund offshore wind farm using computational fluid dynamics (CFD) with large-eddy simulation (LES) turbulence modeling and actuator line rotor aerodynamics models. There are many ways to model this type of flow, ranging from very simple models of wind turbine wakes and their interactions that can run in a matter of seconds on a laptop to high-fidelity, turbulence -resolving LES of the atmospheric flow and wakes that require days of use of thousands of cores on a high-performance computing cluster. The uses of wind plant flow simulations are equally varied. Wind plant layout designers run hundreds or thousands of simulations over all possible wind directions and speeds using simple models to predict a future wind plant's annual energy production. Researchers use LESs of the wind plant to study phenomena such as wind turbine wake interactions with each other and with the atmospheric boundary layer (ABL). As computational resources become more readily available, it is even becoming common for turbine manufacturers to use CFD of some form to model wind plants in complex terrain to help in the wind turbine siting and selection process. We are beginning to see the expensive, high-fidelity tools being used to create data and knowledge that improves or enables the creation of new cheaper, lower fidelity tools. We are also beginning to see the low- and high-fidelity tools used together to perform uncertainty quantification of energy estimates and optimization of wind plant layouts. As new computing technologies mature, such as graphics processing unit (GPU)-based computing, the future of full wind plant flow modeling is exciting.

17 WIND ENERGY↗
DOE OSTI2014

The Wind Integration National Dataset (WIND) toolkit (Presentation)

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

17 WIND ENERGY↗