Turbulence Modeling in the Age of Data

• Karthik Duraisamy ^[1] ; Gianluca Iaccarino ^[2] ; Heng Xiao
  1. [1] University of Michigan–Ann Arbor

     University of Michigan–Ann Arbor

     City of Ann Arbor, Estados Unidos

     
  2. [2] Stanford University

     Stanford University

     Estados Unidos

     
• Localización: Annual review of fluid mechanics, ISSN 0066-4189, Nº. 51, 2019, págs. 357-377
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier–Stokes (RANS) equations. In the past few years, with the availability of large and diverse data sets, researchers have begun to explore methods to systematically inform turbulence models with data, with the goal of quantifying and reducing model uncertainties. This review surveys recent developments in bounding uncertainties in RANS models via physical constraints, in adopting statistical inference to characterize model coefficients and estimate discrepancy, and in using machine learning to improve turbulence models. Key principles, achievements, and challenges are discussed. A central perspective advocated in this review is that by exploiting foundational knowledge in turbulence modeling and physical constraints, researchers can use data-driven approaches to yield useful predictive models.