Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations.
Abstract not provided.
Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations.
Abstract
Abstract not provided.
- Authors:
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1367203
- Report Number(s):
- SAND2017-5165C
653334
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Conference
- Resource Relation:
- Conference: Proposed for presentation at the AIAA Aviation Conference in Denver, CO.
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Barone, Matthew F., Fike, Jeffrey A., Chowdhary, Kamaljit Singh, Davis, Warren Leon,, Ling, Julia, and Martin, Shawn. Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations.. United States: N. p., 2017.
Web. doi:10.2514/6.2017-3979.
Barone, Matthew F., Fike, Jeffrey A., Chowdhary, Kamaljit Singh, Davis, Warren Leon,, Ling, Julia, & Martin, Shawn. Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations.. United States. doi:10.2514/6.2017-3979.
Barone, Matthew F., Fike, Jeffrey A., Chowdhary, Kamaljit Singh, Davis, Warren Leon,, Ling, Julia, and Martin, Shawn. Mon .
"Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations.". United States.
doi:10.2514/6.2017-3979. https://www.osti.gov/servlets/purl/1367203.
@article{osti_1367203,
title = {Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations.},
author = {Barone, Matthew F. and Fike, Jeffrey A. and Chowdhary, Kamaljit Singh and Davis, Warren Leon, and Ling, Julia and Martin, Shawn},
abstractNote = {Abstract not provided.},
doi = {10.2514/6.2017-3979},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}
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Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations.
Abstract not provided. -
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations.
Abstract not provided. -
Implementation and assessment of turbine wake models in the Weather Research and Forecasting model for both mesoscale and large-eddy simulation
Flow dynamics in large wind projects are influenced by the turbines located within. The turbine wakes, regions characterized by lower wind speeds and higher levels of turbulence than the surrounding free stream flow, can extend several rotor diameters downstream, and may meander and widen with increasing distance from the turbine. Turbine wakes can also reduce the power generated by downstream turbines and accelerate fatigue and damage to turbine components. An improved understanding of wake formation and transport within wind parks is essential for maximizing power output and increasing turbine lifespan. Moreover, the influence of wakes from large wind projects onmore »