Stochastic Wind Speed Modeling and Prediction using Historical Wind Data for Aircraft Applications

Matthew Rhudy, Mark Longenberger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Information about the local wind speed is critical for aircraft applications. Though various methods have been developed for estimating the local wind speed experienced by an aircraft, these methods often require a stochastic model of the wind behavior in order to accurately predict the variations in the wind. While existing work has explored the application of these wind models, information regarding how to determine the parameters of stochastic wind models is limited. This work explores the connections between wind distribution modeling and stochastic wind modeling to bridge the gap between these differing frameworks. Specifically, a method for using historical wind data to calculate the parameters of a stochastic wind model is proposed and validated with respect to real measurement data. The proposed approach is shown to accurately model the measured wind speed using a combination of multiple random variables.

Original languageEnglish (US)
Title of host publicationAIAA Aviation Forum and ASCEND, 2024
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624107160
DOIs
StatePublished - 2024
EventAIAA Aviation Forum and ASCEND, 2024 - Las Vegas, United States
Duration: Jul 29 2024Aug 2 2024

Publication series

NameAIAA Aviation Forum and ASCEND, 2024

Conference

ConferenceAIAA Aviation Forum and ASCEND, 2024
Country/TerritoryUnited States
CityLas Vegas
Period7/29/248/2/24

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Aerospace Engineering
  • Space and Planetary Science

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