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 language | English |
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Pages (from-to) | 111-131 |
Number of pages | 21 |
Journal | Journal of Computational Physics |
Volume | 345 |
DOIs | |
State | Published - 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