A data-driven adaptive Reynolds-averaged Navier–Stokes k–ω model for turbulent flow

Zhiyong Li, Huaibao Zhang, Sean C.C. Bailey, Jesse B. Hoagg, Alexandre Martin

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

This paper presents a new data-driven adaptive computational model for simulating turbulent flow, where partial-but-incomplete measurement data is available. The model automatically adjusts the closure coefficients of the Reynolds-averaged Navier–Stokes (RANS) k–ω turbulence equations to improve agreement between the simulated flow and the measurements. This data-driven adaptive RANS k–ω (D-DARK) model is validated with 3 canonical flow geometries: pipe flow, backward-facing step, and flow around an airfoil. For all test cases, the D-DARK model improves agreement with experimental data in comparison to the results from a non-adaptive RANS k–ω model that uses standard values of the closure coefficients. For the pipe flow, adaptation is driven by mean stream-wise velocity data from 42 measurement locations along the pipe radius, and the D-DARK model reduces the average error from 5.2% to 1.1%. For the 2-dimensional backward-facing step, adaptation is driven by mean stream-wise velocity data from 100 measurement locations at 4 cross-sections of the flow. In this case, D-DARK reduces the average error from 40% to 12%. For the NACA 0012 airfoil, adaptation is driven by surface-pressure data at 25 measurement locations. The D-DARK model reduces the average error in surface-pressure coefficients from 45% to 12%.

Original languageEnglish
Pages (from-to)111-131
Number of pages21
JournalJournal of Computational Physics
Volume345
DOIs
StatePublished - Sep 15 2017

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Inc.

Keywords

  • Adaptive
  • Data-driven
  • RANS
  • Turbulence
  • k–ω

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

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