Data assimilation for turbulent mean flow and scalar fields with anisotropic formulation

He, Chuangxin; Wang, Peng

doi:10.1007/s00348-021-03213-8

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…The performance of the method can be further improved by increasing the degree of freedom for optimization. He et al [69] use the adjoint-based data assimilation model to optimize an anisotropic Reynolds stress forcing and exceed the performance of He et al [68]. In conclusion, both of the data-driven methods discussed that were applied to jet flows [68,69] demonstrate superior performance in matching the reference velocity field compared to the RL model introduced in this work.…”

Section: Discussionmentioning

confidence: 72%

“…In conclusion, both of the data-driven methods discussed that were applied to jet flows [68,69] demonstrate superior performance in matching the reference velocity field compared to the RL model introduced in this work. However, it should be noted that they incorporate greater knowledge of the reference field [68] and have greater degrees of freedom [69].…”

Section: Discussionmentioning

confidence: 83%

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Deep Reinforcement Learning-Augmented Spalart–Allmaras Turbulence Model: Application to a Turbulent Round Jet Flow

Fuchs,

von Saldern,

Kaiser

et al. 2024

Fluids

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The purpose of this work is to explore the potential of deep reinforcement learning (DRL) as a black-box optimizer for turbulence model identification. For this, we consider a Reynolds-averaged Navier–Stokes (RANS) closure model of a round turbulent jet flow at a Reynolds number of 10,000. For this purpose, we augment the widely utilized Spalart–Allmaras turbulence model by introducing a source term that is identified by DRL. The algorithm is trained to maximize the alignment of the augmented RANS model velocity fields and time-averaged large eddy simulation (LES) reference data. It is shown that the alignment between the reference data and the results of the RANS simulation is improved by 48% using the Spalart–Allmaras model augmented with DRL compared to the standard model. The velocity field, jet spreading rate, and axial velocity decay exhibit substantially improved agreement with both the LES reference and literature data. In addition, we applied the trained model to a jet flow with a Reynolds number of 15,000, which improved the mean field alignment by 35%, demonstrating that the framework is applicable to unseen data of the same configuration at a higher Reynolds number. Overall, this work demonstrates that DRL is a promising method for RANS closure model identification. Hurdles and challenges associated with the presented methodology, such as high numerical cost, numerical stability, and sensitivity of hyperparameters are discussed in the study.

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 83%

Deep Reinforcement Learning-Augmented Spalart–Allmaras Turbulence Model: Application to a Turbulent Round Jet Flow

Fuchs,

von Saldern,

Kaiser

et al. 2024

Fluids

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“…An evaluation and validation of the current DA scheme is conducted using three test cases. As a result of the optimization of F, the average flow field and scalar field can be perfectly reproduced from the observations [106].…”

Section: Data Assimilationmentioning

confidence: 76%

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Zhang

Jiang

2023

JMSE

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Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

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“…2019; Chandramouli et al. 2020; He, Wang & Liu 2021). We focus on 4D-Var as the unsteady state is the current topic of interest.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, variational DA can be implemented in either discrete or continuous form. The former implementation discretises the system before the derivation of the adjoint equation, which has a large memory requirement for expensive matrix computation (Papoutsis-Kiachagias & Giannakoglou 2016), and the latter implementation is thus preferred for complex flow configurations (Foures et al 2014;He et al 2018a;Li et al 2019;Chandramouli et al 2020;He, Wang & Liu 2021). We focus on 4D-Var as the unsteady state is the current topic of interest.…”

Section: Introductionmentioning

confidence: 99%

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

He,

Zeng,

Wang

et al. 2024

J. Fluid Mech.

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The super-temporal-resolution (STR) reconstruction of turbulent flows is an important data augmentation application for increasing the data reach in measurement techniques and understanding turbulence dynamics. This paper proposes a data assimilation (DA) strategy based on weak-constraint four-dimensional variation to conduct an STR reconstruction in a turbulent jet beyond the Nyquist limit from given low-sampling-rate observations. Highly resolved large-eddy simulation (LES) data are used to produce synthetic measurements, which are used as observations and for validation. A segregated assimilation procedure is realised to assimilate the initial condition, inflow boundary condition and model error separately. Different types of observational data are tested. The first type is down-sampled LES data containing many small-scale turbulence structures with or without synthetic noise. The DA results show that the temporal variation of the small-scale structures is well recovered even with noise in the observations. The spectra are resolved to a frequency approximately one order of magnitude higher than what can be captured within the Nyquist limit. The second type of observation is low-sampling-rate tomographic particle image velocimetry (tomo-PIV) data with or without the injection of small-scale structures. The modulation between the large-scale structures contained in the tomo-PIV fields and the small scales injected from the observations is improved. The resultant small scales in the STR reconstruction have the characteristics of authentic turbulence to a considerable extent. Additionally, DA yields much smaller errors in the prediction of particle positions when compared with the Wiener filter, demonstrating the great potential for Lagrangian particle tracking in measurement techniques.

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Data assimilation for turbulent mean flow and scalar fields with anisotropic formulation

Cited by 19 publications

References 34 publications

Deep Reinforcement Learning-Augmented Spalart–Allmaras Turbulence Model: Application to a Turbulent Round Jet Flow

Deep Reinforcement Learning-Augmented Spalart–Allmaras Turbulence Model: Application to a Turbulent Round Jet Flow

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

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