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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 OSTI2022

Isolated Building Wake Experiment At Texas Tech University’s Wind Engineering Research Field Lab

Texas Tech University’s (TTU’s) Wind Engineering Research Field Lab (WERFL) building is an idealized rectangular building on the scale of a typical suburban house. It is surrounded almost entirely by unobstructed planes and is built on a turn table. WERFL is ideal for providing measurements of an idealized building under real-world conditions including atmospheric stability and variable relative wind angles. The intent of this data set is for verification and validation of computational fluid dynamics model simulations of flow around buildings. The primary focus of this dataset features 24 3D sonic anemometers (20 downwind and 4 upwind) and 10 2D sonic anemometers (9 downwind and 1 upwind) located in the immediate vicinity of the WERFL building. It also includes a variety of instruments intended to measure the ambient meteorological parameters and the undisturbed wind profile.

17 WIND ENERGY↗
NASA NTRS1983

Wind Engineering

Dr. Jack Cermak, Director of Fluid Dynamics and Diffusion Laboratory, developed the first wind tunnel to simulate the changing temperatures, directions and velocities of natural winds. In this work, Cermak benefited from NASA technology related to what is known as the atmospheric boundary layer (ABL).

Source record↗
DOE OSTI2016

Mesoscale to microscale wind farm flow modeling and evaluation

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so-called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to direct future research in this area.

17 WIND ENERGY↗
DOE OSTI2019

IEA Wind TCP Task 37: Systems Engineering in Wind Energy - WP2.1 Reference Wind Turbines

This report describes two wind turbine models developed within the second work package (WP2) of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The wind turbine models aim at acting as references for future research projects on wind energy, representing a modern land-based wind turbine and a next generation offshore wind turbine. The land-based design is a class IIIA geared configuration with a rated electrical power of 3.4-megawatts, a rotor diameter of 130 meters (m), and a hub height of 110 m. The offshore design is a class IA configuration with a rated electrical power of 10.0 megawatts, a rotor diameter of 198 m, and a hub height of 119 m. The offshore turbine adopts a direct-drive generator.

17 WIND ENERGY↗
NASA NTRS1938

Wind-tunnel Tests of a 2-engine Airplane Model as a Preliminary Study of Flight Conditions Arising on the Failure of the Engine

Wind tunnel tests of a 15-foot-span model of a two-engine low wing transport airplane were made as a preliminary study of the emergency arising from the failure of one engine in flight. Two methods of reducing the initial yawing moment resulting from the failure of one engine were investigated and the equilibrium conditions were explored for two basic modes on one engine, one with zero angle of sideslip and the other with several degrees of sideslip. The added drag resulting from the unsymmetrical attitudes required for flight on one engine was determined for the model airplane. The effects of the application of power upon the stability, controllability, lift, and drag of the model airplane were measured. A dynamic pressure survey of the propeller slipstream was made in the neighborhood of the tail surfaces at three angles of attack. The added parasite drag of the model airplane resulting from the unfavorable conditions of flight on one engine was estimated. From 35 to 50 percent of this added drag was due to the drag of the dead engine propeller and the other 50 to 65 percent was due to the unsymmetrical attitude of the airplane. The mode of flight on one engine in which the angle of sideslip was zero was found to require less power than the mode in which the angle of sideslip was several degrees.

Hartman, Edwin P↗
DOE OSTI2012

Wind Turbine Engineering R&D at Los Alamos National Laboratory [Slides]

There are enough wind resources in the US to provide more than 10 times the electric power we currently use, however wind power only accounts for 3% of our total electricity production. DOE’s goal is to increase wind power capacity to 20% of the total by the year 2030. LANL’s Intelligent Wind Turbine Project is applying leading-edge engineering expertise in modeling and simulation, experimental validation, and advanced sensing technologies to challenges faced in the design and operation of modern wind turbines. One of the main limitations to wind use is cost. Wind power currently costs about 8 cents per kilowatt-hour, which is more than 1.4 times the cost of electricity generated by burning natural gas. DOE desires to reduce wind power costs through technological innovation and reliability improvements. LANL’s engineering expertise, which is well-suited to the wind energy application, is born out of the Laboratory’s core national security mission. Our ability to visualize detailed flowfields around operating wind turbines is founded upon experimental turbulence measurement techniques developed for the nuclear weapons program. The capability to model multiple wind turbines on any terrain is built upon past work in modeling urban dispersion, wildland fires, and hurricane intensification. In addition, research using embedded sensing for the structural assessment of civil structures has led to the ability to detect incipient damage in operating wind turbine blades. This presentation will provide an overview of the innovative wind turbine research ongoing at LANL and its connection to LANL’s core mission.

17 WIND ENERGY↗
DOE OSTI2015

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Abstract Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.

Annoni, Jennifer↗
DOE OSTI2017

WindSE: Wind Systems Engineering [SWR-17-03]

WindSE is a tool for modeling atmospheric fluid flow within a wind plant and performing optimization of the turbine positions or operating settings. WindSE is a python package that uses a FEniCS backend to perform wind farm simulations and optimization.

King, Ryan↗
DOE OSTI2015

A Vision for Systems Engineering Applied to Wind Energy (Presentation)

This presentation was given at the Third Wind Energy Systems Engineering Workshop on January 14, 2015. Topics covered include the importance of systems engineering, a vision for systems engineering as applied to wind energy, and application of systems engineering approaches to wind energy research and development.

17 WIND ENERGY↗
DOE OSTI2023

WindSE: Wind Systems Engineering [SWR-17-03]

WindSE is a python package that uses a FEniCS backend to perform wind farm simulations and optimization. Documentation can be found at: https://windse.readthedocs.io/en/latest/

King, Ryan↗
DOE OSTI2012

Computational Mechanics Research and Support for Aerodynamics and Hydraulics at TFHRC, Year 2 Quarter 4 Progress Report

The computational fluid dynamics (CFD) and computational structural mechanics (CSM) focus areas at Argonne’s Transportation Research and Analysis Computing Center (TRACC) initiated a project to support and compliment the experimental programs at the Turner-Fairbank Highway Research Center (TFHRC) with high performance computing based analysis capabilities in August 2010. The project was established with a new interagency agreement between the Department of Energy and the Department of Transportation to provide collaborative research, development, and benchmarking of advanced three-dimensional computational mechanics analysis methods to the aerodynamics and hydraulics laboratories at TFHRC for a period of five years, beginning in October 2010. The analysis methods employ well benchmarked and supported commercial computational mechanics software. Computational mechanics encompasses the areas of Computational Fluid Dynamics (CFD), Computational Wind Engineering (CWE), Computational Structural Mechanics (CSM), and Computational Multiphysics Mechanics (CMM) applied in Fluid-Structure Interaction (FSI) problems. The major areas of focus of the project are wind and water effects on bridges — superstructure, deck, cables, and substructure (including soil), primarily during storms and flood events — and the risks that these loads pose to structural failure. For flood events at bridges, another major focus of the work is assessment of the risk to bridges caused by scour of stream and riverbed material away from the foundations of a bridge. Other areas of current research include modeling of the salt spray transport into bridge girders to address suitability of using weathering steel in bridges, CFD analysis of the operation of the wind tunnel in the TFHRC wind engineering laboratory, and coupling of CFD and CSM software to solve fluid structure interaction problems, primarily analysis of bridge cables in wind. This quarterly report documents technical progress on the project tasks for the period of July through September 2012.

42 ENGINEERING↗
NASA NTRS1981

Applications of the DOE/NASA wind turbine engineering information system

A statistical analysis of data obtained from the Technology and Engineering Information Systems was made. The systems analyzed consist of the following elements: (1) sensors which measure critical parameters (e.g., wind speed and direction, output power, blade loads and component vibrations); (2) remote multiplexing units (RMUs) on each wind turbine which frequency-modulate, multiplex and transmit sensor outputs; (3) on-site instrumentation to record, process and display the sensor output; and (4) statistical analysis of data. Two examples of the capabilities of these systems are presented. The first illustrates the standardized format for application of statistical analysis to each directly measured parameter. The second shows the use of a model to estimate the variability of the rotor thrust loading, which is a derived parameter.

Neustadter, H. E.↗