A highly accurate strategy for data-driven turbulence modeling

Brener, Bernardo P.; Cruz, Matheus A.; Macedo, Matheus S. S.; Thompson, Roney L.

doi:10.1007/s40314-023-02547-9

Cited by 4 publications

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

Macedo,

Cruz,

Brener

et al. 2024

Numerical Methods in Fluids

Self Cite

View full text Add to dashboard Cite

The long lasting demand for better turbulence models and the still prohibitively computational cost of high‐fidelity fluid dynamics simulations, like direct numerical simulations and large eddy simulations, have led to a rising interest in coupling available high‐fidelity datasets and popular, yet limited, Reynolds averaged Navier–Stokes simulations through machine learning (ML) techniques. Many of the recent advances used the Reynolds stress tensor or, less frequently, the Reynolds force vector as the target for these corrections. In the present work, we considered an unexplored strategy, namely to use the modeled terms of the Reynolds stress transport equation as the target for the ML predictions, employing a neural network approach. After that, we solve the coupled set of governing equations to obtain the mean velocity field. We apply this strategy to solve the flow through a square duct. The obtained results consistently recover the secondary flow, which is not present in the baseline simulations that used the model. The results were compared with other approaches of the literature, showing a path that can be useful in the seek of more universal models in turbulence.

show abstract

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

Macedo,

Cruz,

Brener

et al. 2024

Numerical Methods in Fluids

Self Cite

View full text Add to dashboard Cite

show abstract

Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling

Taghizadeh,

Witherden,

Girimaji

2024

Physics of Fluids

View full text Add to dashboard Cite

Complex turbulent flows with large-scale instabilities and coherent structures pose challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods. The difficulty arises due to the strong flow-dependence (the non-universality) of the unsteady coherent structures, which translates to poor generalizability of data-driven models. It is well-accepted that the dynamically active coherent structures reside in the larger scales, while the smaller scales of turbulence exhibit more “universal” (generalizable) characteristics. In such flows, it is prudent to separate the treatment of the flow-dependent aspects from the universal features of the turbulence field. Scale resolving simulations (SRS), such as the partially averaged Navier–Stokes (PANS) method, seek to resolve the flow-dependent coherent scales of motion and model only the universal stochastic features. Such an approach requires the development of scale-sensitive turbulence closures that not only allow for generalizability but also exhibit appropriate dependence on the cut-off length scale. The objectives of this work are to (i) establish the physical characteristics of cut-off dependent closures in stochastic turbulence; (ii) develop a procedure for subfilter stress neural network development at different cut-offs using high-fidelity data; and (iii) examine the optimal approach for the incorporation of the unsteady features in the network for consistent a posteriori use. The scale-dependent closure physics analysis is performed in the context of the PANS approach, but the technique can be extended to other SRS methods. The benchmark “flow past periodic hills” case is considered for proof of concept. The appropriate self-similarity parameters for incorporating unsteady features are identified. The study demonstrates that when the subfilter data are suitably normalized, the machine learning based SRS model is indeed insensitive to the cut-off scale.

show abstract

Turbulence closure modeling with machine learning: a foundational physics perspective

Girimaji

2024

New J. Phys.

View full text Add to dashboard Cite

Turbulence closure modeling using machine learning is at an early crossroads. The extraordinary success of machine learning (ML) in a variety of challenging fields had given rise to an expectation of similar transformative advances in the area of turbulence closure modeling. However, by most accounts, the current rate of progress toward accurate and predictive ML-RANS (Reynolds Averaged Navier-Stokes) closure models has been very slow. Upon retrospection, the absence of rapid transformative progress can be attributed to two factors: theunderestimation of the intricacies of turbulence modeling and the overestimation of ML’s ability to capture all features without employing targeted strategies. To pave the way for moremeaningful ML closures tailored to address the nuances of turbulence, this article seeks toreview the foundational flow physics to assess the challenges in the context of data-drivenapproaches. Revisiting analogies with statistical mechanics and stochastic systems, the keyphysical complexities and mathematical limitations are explicated. It is noted that the currentML approaches do not systematically address the inherent limitations of a statistical approachor the inadequacies of the mathematical forms of closure expressions. The study underscoresthe drawbacks of supervised learning-based closures and stresses the importance of a morediscerning ML modeling framework. As ML methods evolve (which is happening at a rapidpace) and our understanding of the turbulence phenomenon improves, the inferences expressedhere should be suitably modified.

show abstract

A highly accurate strategy for data-driven turbulence modeling

Cited by 4 publications

References 65 publications

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling

Turbulence closure modeling with machine learning: a foundational physics perspective

Contact Info

Product

Resources

About