Annual Review of Fluid Mechanics· 2018Q1· Review
Turbulence Modeling in the Age of Data
- 1,450citations
- Q1SCImago
- 2018year
Short summary
Data-driven approaches, leveraging foundational knowledge and physical constraints, are yielding improved predictive turbulence models by systematically informing Reynolds-averaged Navier–Stokes (RANS) equations with experimental and simulation data.
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Key points
- Data from experiments and direct simulations are increasingly used to systematically inform turbulence models.
- Methods include bounding RANS model uncertainties with physical constraints.
- Statistical inference is adopted to characterize model coefficients and estimate discrepancy.
- Machine learning is being used to improve turbulence models.
- Combining data-driven approaches with foundational knowledge and physical constraints yields useful predictive models.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Annual Review of Fluid Mechanics, 2018 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy