No AccessDevelopment of Machine Learning Models for Turbulent Wall Pressure FluctuationsJulia Ling, Matthew F. Barone, Warren Davis, Kamaljit Chowdhary and Jeffrey FikeJulia LingSandia National LaboratoriesSearch for more papers by this author, Matthew F. BaroneSandia National LaboratoriesSearch for more papers by this author, Warren DavisSandia National LaboratoriesSearch for more papers by this author, Kamaljit ChowdharySandia National LaboratoriesSearch for more papers by this author and Jeffrey FikeSandia National LaboratoriesSearch for more papers by this authorAIAA 2017-0755Session: Novel CFD MethodsPublished Online:5 Jan 2017https://doi.org/10.2514/6.2017-0755SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookXLinked InRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetailsSee PDF for referencesCited byData-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry15 March 2023 | Nuclear Technology, Vol. 210, No. 7Machine learning approach for turbulence forecasting using support vector machine1 Jan 2020 | IOP Conference Series: Materials Science and Engineering, Vol. 725, No. 1On developing data-driven turbulence model for DG solution of RANS1 Aug 2019 | Chinese Journal of Aeronautics, Vol. 32, No. 8Near-Wall Modeling Using Coordinate Frame Invariant Representations and Neural NetworksNathan E. Miller, Matthew F. Barone, Warren L. Davis and Jeffrey A. Fike15 June 2019Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure FluctuationsMatthew F. Barone, Julia Ling, Kenny Chowdhary, Warren Davis and Jeffrey Fike2 June 2017Approximate Analytical Models for Turbulent Boundary Layer Wall Pressure and Wall Shear Fluctuation Spectra and Coherence FunctionsLawrence J. DeChant, Justin A. Smith and Matthew F. Barone5 January 2017 What's Popular 55th AIAA Aerospace Sciences Meeting 9 - 13 January 2017Grapevine, Texashttps://doi.org/10.2514/6.2017-0755 CrossmarkInformationCopyright © 2017 by the American Institute of Aeronautics and Astronautics, Inc. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. TopicsAerodynamicsAerospace SciencesBoundary LayersComputational Fluid DynamicsConservation of Momentum EquationsEquations of Fluid DynamicsFlow RegimesFluid DynamicsFluid Flow PropertiesFluid MechanicsFluid Structure InteractionTurbulenceTurbulence ModelsVortex Dynamics KeywordsMachine LearningPower Spectral DensityDirect Numerical SimulationConvolutional Neural NetworkCompressible Boundary LayerFluid Structure InteractionTurbulence ModelsReynolds Averaged Navier StokesFree Stream VelocityVelocity ProfilesDigital Topics Fluid Dynamics