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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗
DOE OSTI2020

Tools Assessing Performance

For the distributed wind industry, it can be challenging to accurately predict the performance and annual energy production of projects prior to their installation. The U.S. Department of Energy’s Tools Assessing Performance (TAP) project aims to improve wind resource characterization, thereby reducing the uncertainty of project performance and financing costs, increasing consumer confidence, and lowering the levelized cost of distributed wind energy. A collaborative effort among DOE National Laboratories, TAP will create a computational framework that provides the distributed wind community with access to newly developed wind resource data and modeling capabilities. These capabilities will allow users to perform timely and accurate performance assessments for distributed wind projects at locations across the United States.

wind, distributed, tools, performance↗
DOE OSTI2023

Energy I-Corps 2023: Distributed Wind Energy Toolkit

This presentation covers the purpose and scope of an Energy I-Corps project focused on refining a suite of analytical tools designed to support distributed wind deployment. These tools - the Distributed Generation Market Demand (dGen), Distributed Wind Tools Assessing Performance (TAP), and the Hybrid Optimization and Performance Platform (HOPP) - combine to provide users with the ability to simulate consumer purchasing behavior, accurately assess wind resource to better predict turbine performance, and effectively design and optimize hybrid systems that include distributed wind.

deployment↗
DOE OSTI2024

Distributed Wind-Energy-Based Hybrids

Presentation defining distributed wind-based hybrids and introducing the Hybrid Optimization Performance Platform (HOPP) an open-source tool that helps design and optimize buildable hybrid power plants.

distributed wind-based hybrids↗
DOE OSTI2023

Distributed Wind Aeroelastic Modeling (dWAM)

Aeroelastic modeling is the primary method for the structural and performance assessment of any wind turbine. Despite the advantages afforded by aeroelastic modeling tools, their use in the distributed wind energy industry is limited. dWAM started from the NREL Aeroelastic Modeling for Distributed Wind Turbines project with Damiani & Davis (2022) researching current needs, including input from an industry workshop. NREL's efforts will focus on OpenFAST code improvements, validation using research turbines at NREL's Flatirons Campus, code-to-code verification activities, and development of guidance documents and improved user manuals. Partner lab, Sandia National Laboratories, will focus their efforts on vertical axis wind turbine (VAWT) modeling including modeling code development, validation, and user-experience improvements.

aeroelastic↗
DOE OSTI2023

Distributed Wind Aeroelastic Modeling (dWAM)

Aeroelastic modeling is the primary method for the structural and performance assessment of any wind turbine. These tools provide an understanding of the impact of design parameters on turbine loading and power response before operating in the field. Despite these advantages, the use of aeroelastic modeling in the distributed wind energy industry is limited. This project aims to improve the aeroelastic modeling tools for distributed wind turbines to enable the design and certification of optimized turbine technology with a competitive cost of energy.

aeroelastic modeling tools↗
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
DOE OSTI2024

Tools Assessing Performance (TAP) 2.0

Dmitry Duplyakin will be presenting on the latest research and results in the Tools Assessing Performance (TAP) 2.0 project. This presentation will include updates on the latest data the group has produced, integration of obstacle models in the computational pipeline for distributed wind siting, and the plans for the near-term analysis and validation efforts. The talk will acknowledge the work of collaborators from NREL and three other national labs - ANL, LANL, and PNNL - all contributing to this multi-year project.

distributed wind↗
DOE OSTI2022

Data-Driven Generic Turbines for Distributed Wind Modeling, Optimization, and Economic Studies

As distributed energy resources (DER) become less expensive and more popular, utilities, project developers, and customers have an increasing need to model the performance of existing and proposed DER systems. Distributed wind has been shown to have widespread economic potential but is often represented by a simplified model in - or excluded from - DER modeling tools and studies. There is often no economic imperative to extend models and studies to give full consideration to distributed wind. We present a set of data-driven generic turbines derived from 16 years of annual distributed wind market survey data. The proposed methodology can be used to derive generic turbines from separate or updated data sets. Finally, a mixed-integer linear programming approach to optimal distributed wind project sizing is used to demonstrate the generic turbine models. Combined, these models and methods can reduce barriers to considering distributed wind in modeling tools and studies.

Reiman, Andrew P.↗
DOE OSTI2021

OpenDSS-wrapper (Distribution System Co-simulator with Distributed Energy Resource Controls)

Electric grid transformation with the proliferation of distributed energy resources (DER), such as solar photovoltaic (PV), wind, advanced energy storage technologies, and electric vehicles, and the growing use of communication technologies in both transmission and distribution systems are increasing the need to capture the interactions among these systems. Advanced modeling, control, and simulation tools that can perform co-simulation of electric power systems with other domains become indispensable to accurately model these interactions. To address this need, we have developed a codebase that integrates an electric power distribution system simulator, DER models, and DER controls. The codebase is based on OpenDSS, a distribution system simulator, and an existing open source co-simulation framework called HELICS. The contribution and uniqueness of the proposed codebase is that it tailors the generic HELICS framework specifically for distribution grid-related applications. The codebase includes an OpenDSS wrapper that controls the simulation, implements advanced DER controls, and extracts power flows, voltages, and other power system element information from the the distribution network modeled in OpenDSS. Sample HELICS federates, including a federate for OpenDSS, are provided that can communicate messages through the HELICS interface. Sample federates can be modified and additional HELICS federates can be added by the user depending on their use case requirements. SEE ALSO: https://github.com/NREL/dss-cosim

Blonsky, Michael↗
DOE OSTI2021

OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design

Abstract This paper provides a summary of the work done within the OC6 Phase II project, which was focused on the implementation and verification of an advanced soil–structure interaction model for offshore wind system design and analysis. The soil–structure interaction model comes from the REDWIN project and uses an elastoplastic, macroelement model with kinematic hardening, which captures the stiffness and damping characteristics of offshore wind foundations more accurately than more traditional and simplified soil–structure interaction modeling approaches. Participants in the OC6 project integrated this macroelement capability to coupled aero‐hydro‐servo‐elastic offshore wind turbine modeling tools and verified the implementation by comparing simulation results across the modeling tools for an example monopile design. The simulation results were also compared to more traditional soil–structure interaction modeling approaches like apparent fixity, coupled springs, and distributed springs models. The macroelement approach resulted in smaller overall loading in the system due to both shifts in the system frequencies and increased energy dissipation. No validation work was performed, but the macroelement approach has shown increased accuracy within the REDWIN project, resulting in decreased uncertainty in the design. For the monopile design investigated here, that implies a less conservative and thus more cost‐effective offshore wind design.

17 WIND ENERGY↗
DOE OSTI2021

Diverse Super-Resolution (diversity_SR) [SWR-21-60]

Deep learning tools for enhancing the spatial resolution of wind data. The software is developed in Python using the TensorFlow deep learning package. Models for diversity super-resolution is provided. Included in the package are pretrained models with example code/data to perform the super-resolution as well as tools of training models for different enhancement- or data-types. The super-resolution is an inherently ill-conditioned problem, with multiple high-resolution fields plausibly mapping to the same coarse field. Considitional GANs provide a framework for generating a distribution of high-resolution realizations from a given low-resolution input. Stochastic estimation is used to inform the network of the expected degree and location of sub-grid diversity. The package includes a pretrained network to generate distributions of 10x-enhanced fields of wind data.

Glaws, Andrew↗
DOE OSTI2022

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Atmosphere Modeling and Performance Sensitivity for the Mars Sample Return Earth Entry System

The Capture, Containment, and Return System (CCRS) mission is a key element of the joint NASA-European Space Agency (ESA) planned Mars Sample Return (MSR) Campaign. The CCRS assembled Earth Entry System (EES) will enter the mission’s final segment in its Approach, Entry, Descent, and Landing (AEDL) Phase. The EES AEDL aims to deliver a highly reliable, safe, and accurate return while maintaining strict containment assurance targets established by the campaign. As currently designed, the EES would be the first fully passive sample return capsule with no parachute or onboard control system, prompting a significant effort in Earth atmosphere characterization and modeling. Earth’s atmosphere, specifically winds, have a strong influence on EES flight mechanics and landing footprint during its free fall landing. This paper describes Earth atmosphere modeling, atmosphere characterization, and performance sensitivities incorporated into the teams’s efforts to ensure AEDL success. By utilizing high resolution balloon radiosondes, analyzing wind structural and distributional compositions, and investigating flight mechanics sensitivities, the AEDL atmosphere team has been able to better understand and simulate local wind conditions at the Utah Test and Training Range (UTTR). Utilizing tools such as horizontal turbulent kinetic energy, vertical wind shear, or integrated wind, the team have been able to reveal valuable information about wind profiles in a deeper context than previously conducted, ultimately improving understanding of AEDL flight mechanics sensitivity and EES design.

Kaustubh Ray↗
NASA NTRS1992

Reynolds number and cowl position effects for a generic sidewall compression scramjet inlet at Mach 10 - A computational and experimental investigation

Reynolds number and cowl position effects on the internal shock structure and the resulting performance of a generic three-dimensional sidewall compression scramjet inlet with a leading edge sweep of 45 degrees at Mach 10 have been examined both computationally and experimentally. Prior to the experiment, a three-dimensional Navier-Stokes code was adapted to perform preliminary parametric studies leading to the design of the present configuration. Following this design phase, the code was then utilized as an analysis tool to provide a better understanding of the flow field and the experimental static pressure data for the final experimental configuration. The wind tunnel model possessed 240 static pressure orifices distributed on the forebody plane, sidewalls, and cowl and was tested in the NASA Langley 31 Inch Mach 10 Tunnel.

Holland, Scott D.↗
NASA NTRS1992

Reynolds Number and Cowl Position Effects for a Generic Sidewall Compression Scramjet Inlet at Mach 10: A Computational and Experimental Investigation

Reynolds number and cowl position effects on the internal shock structure and the resulting performance of a generic three-dimensional sidewall compression scramjet inlet with a leading edge sweep of 45 degrees at Mach 10 have been examined both computationally and experimentally. Prior to the experiment, a three-dimensional Navier-Stokes code was adapted to perform preliminary parametric studies leading to the design of the present configuration. Following this design phase, the code was then utilized as an analysis tool to provide a better understanding of the flow field and the experimental static pressure data for the final experimental configuration. The wind tunnel model possessed 240 static pressure orifices distributed on the forebody plane, sidewalls, and cowl and was tested in the NASA Langley 31 Inch Mach 10 Tunnel.

Holland, Scott D.↗
DOE OSTI2021

Tools for Assessing Performance Project: FY2021 Quarter 4 Report

The rotatable building located at Texas Tech’s Reese Technology Center is approximately 14 m wide (width being defined as more normal to the wind than parallel), 9 m long (aligned more with the wind than perpendicular), and 4 m tall. By placing 29 sonic anemometers downwind of the building, see Fig. 1, this facility provided a chance to collect data concerning both the wake velocity deficit distribution in the downstream and crosssteam directions (relative to the mean wind) behind an isolated building. A preliminary comparison between this data and the recently- developed fast-running diffusive wake model, which was developed based on wind tunnel and LES (performed with JOULES) simulations, for the purpose of either validating this model of understanding potential persistent differences between the idealized wind tunnel or LES conditions and those of full-scale real world phenomena. For this preliminary exploration, we utilized the diffusive wake model implemented in the QUIC model.

42 ENGINEERING↗
NASA NTRS2017

Experimental Investigation of Mars Science Laboratory Entry Vehicle Aeroheating in AEDC Hypervelocity Tunnel 9

An experimental investigation of the aeroheating environment of the Mars Science Laboratory entry vehicle was conducted in the Arnold Engineering Development Complex Hypervelocity Wind Tunnel 9. Testing was performed on a 6-in. (0.1524 m) diameter model in the tunnel's Mach 8 and Mach 10 nozzles at free stream Reynolds numbers from 4.1×10*exp 6)/ft to 49×10(exp 6)/ft and from 1.2×10(exp 6)/ft to 19×10(exp 6)/ft, respectively, using pure nitrogen test gas. These conditions spanned the boundary layer flow regimes from completely laminar to fully turbulent flow over the entire forebody. A computational fluid dynamics study was conducted in support of the wind tunnel testing. Laminar and turbulent solutions were generated for all wind tunnel test conditions and comparisons of predicted heating distributions were performed with the data. These comparisons showed agreement for most cases to within the estimated +/-12% experimental uncertainty margin for fully-laminar or fully-turbulent conditions, while transitional heating data were bounded by laminar and turbulent predictions. These results helped to define uncertainty margins on the use of computational tools for vehicle design.

Hollis, Brian R.↗