Turbulence Modeling in the Age of Data

By Karthik Duraisamy, Gianluca Iaccarino, Heng Xiao. Annual Review of Fluid Mechanics, 2018

1,481 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cd74acb268404cde24f5131a22d04d48776b283e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Deep Generative Models in Engineering Design: A Review (2021, methodology): “Shu et al. [70] take an iterative retraining approach by retraining a GAN on high performance models evaluated using Computational Fluid Dynamics (CFD) evaluation.”
Data-driven discovery of coordinates and governing equations (2019, neutral): “Indeed, the data-driven discovery of dynamical systems is the focus of intense research efforts across the physical and engineering sciences [1–15].”
Machine learning methods for turbulence modeling in subsonic flows around airfoils (2018, methodology): “A detailed description about turbulence modeling with data-driven techniques is reviewed in [25].”
Subgrid modelling for two-dimensional turbulence using neural networks (2018, background): “The reader may ﬁnd a thorough review of concurrent ideas in Duraisamy et al. (2018).”
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (2021, background): “Recently, deep learning techniques have become increasingly popular in both computational and experimental fluid dynamics (Brunton et al. 2020; Duraisamy et al. 2019).”
Hamiltonian Neural Networks for solving differential equations (2020, neutral): “…and particularly neural networks (NNs), have been used to explore those data and forecast the future behavior of complex dynamical systems [1]–[5], improve turbulence models [6]–[9], discover differential equations (DEs) [10]– [13], and ﬁnd approximate solutions for those equations [14], [15].”
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network (2018, methodology): “It is common practice in model reduction to use a function approximator to express the unknown terms in the reduced models, such as approximating subgrid stress for large-eddy simulation[27, 28, 29] or approximating interatomic forces for coarse-grained molecular dynamic systems[30, 31].”
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (2020, background): “Applications of machine learning for turbulence modeling have been particularly active in ﬂuid dynamics (Duraisamy et al. 2019; Kutz 2017).”
Enhancing computational fluid dynamics with machine learning (2021, methodology): “A wide range of ML methods have recently been used to improve RANS turbulence modeling [13], focusing on the challenge of improving the accuracy of the Reynolds stresses for general conditions.”
Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “Parish & Duraisamy (2016), Wang et al. (2017), and other researchers have actively engaged in improving RANS models (e.g., Kutz (2017); Duraisamy et al. (2019)).”