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  • Xie, Chenyue  (8)
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  • 1
    Online Resource
    Online Resource
    Dynamic iterative approximate deconvolution models for large-eddy simulation of turbulence
    AIP Publishing ; 2021
    In:  Physics of Fluids Vol. 33, No. 8 ( 2021-08-01)
    Details
    In: Physics of Fluids, AIP Publishing, Vol. 33, No. 8 ( 2021-08-01)
    Abstract: Dynamic iterative approximate deconvolution (DIAD) models with Galilean invariance are developed for subgrid-scale (SGS) stress in the large-eddy simulation (LES) of turbulence. The DIAD models recover the unfiltered variables using the filtered variables at neighboring points and iteratively update model coefficients without any a priori knowledge of direct numerical simulation (DNS) data. The a priori analysis indicates that the DIAD models reconstruct the unclosed SGS stress much better than the classical velocity gradient model and approximate deconvolution model with different filter scales ranging from viscous to inertial regions. We also propose a small-scale eddy viscosity (SSEV) model as an artificial dissipation to suppress the numerical instability based on a scale-similarity-based dynamic method without affecting large-scale flow structures. The SSEV model can predict a velocity spectrum very close to that of DNS data, similar to the traditional implicit large-eddy simulation. In the a posteriori testing, the SSEV-enhanced DIAD model is superior to the SSEV model, dynamic Smagorinsky model, and dynamic mixed model, which predicts a variety of statistics and instantaneous spatial structures of turbulence much closer to those of filtered DNS data without significantly increasing the computational cost. The types of explicit filters, local spatial averaging methods, and initial conditions do not significantly affect the accuracy of DIAD models. We further successfully apply DIAD models to the homogeneous shear turbulence. These results illustrate that the current SSEV-enhanced DIAD approach is promising in the development of advanced SGS models in the LES of turbulence.
    Type of Medium: Online Resource
    ISSN: 1070-6631 , 1089-7666
    URL: Article
    DOI: 10.1063/5.0059643
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 1472743-2
    detail.hit.zdb_id: 241528-8
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  • 2
    Online Resource
    Online Resource
    A dynamic spatial gradient model for the subgrid closure in large-eddy simulation of turbulence
    AIP Publishing ; 2021
    In:  Physics of Fluids Vol. 33, No. 7 ( 2021-07-01)
    Details
    In: Physics of Fluids, AIP Publishing, Vol. 33, No. 7 ( 2021-07-01)
    Abstract: A dynamic spatial gradient model (DSGM) is proposed for the subgrid-scale (SGS) closure of large-eddy simulation (LES). The velocity gradients at neighboring LES grids are incorporated to improve the accuracy of the SGS stress. Compared to the previous machine-learning-based multi-point gradient models, the current model is free from the need of a priori knowledge. The model coefficients are dynamically determined by the least-square method using the Leonard stress. The a priori tests show that the correlation coefficients of the SGS stress for the DSGM framework are much larger than the traditional velocity gradient model over different tested filter widths from viscous to inertial scales. The analysis of the model coefficients in the a priori test suggests that the number of the model coefficients can be significantly reduced, leading to a simpler version of the model. A small-scale eddy viscosity (SSEV) model is introduced as an artificial viscosity to mimic the flux of kinetic energy to smaller scales which cannot be resolved at an LES grid. The velocity spectrum predicted by SSEV-based implicit LES is very close to that of direct numerical simulation (DNS) data. In the a posteriori tests, both the flow statistics and the instantaneous field are accurately recovered with the SSEV-enhanced DSGM model. Compared with the SSEV-based implicit LES, the dynamic Smagorinsky model, and the dynamic mixed model, the results predicted by the current model have overall closer agreements with the filtered DNS result, suggesting that the DSGM framework is well-suited for highly accurate LES of turbulence.
    Type of Medium: Online Resource
    ISSN: 1070-6631 , 1089-7666
    URL: Article
    DOI: 10.1063/5.0058542
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 1472743-2
    detail.hit.zdb_id: 241528-8
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  • 3
    Online Resource
    Online Resource
    Temporally sparse data assimilation for the small-scale reconstruction of turbulence
    AIP Publishing ; 2022
    In:  Physics of Fluids Vol. 34, No. 6 ( 2022-06-01)
    Details
    In: Physics of Fluids, AIP Publishing, Vol. 34, No. 6 ( 2022-06-01)
    Abstract: Previous works have shown that the small-scale information of incompressible homogeneous isotropic turbulence is fully recoverable as long as sufficient large-scale structures are continuously enforced through temporally continuous data assimilation (TCDA). In the current work, we show that the assimilation time step can be relaxed to values about 1–2 orders larger than that for TCDA, using a temporally sparse data assimilation (TSDA) strategy, while the accuracy is still maintained or even slightly better in the presence of non-negligible large-scale errors. One-step data assimilation (ODA) is examined to unravel the mechanism of TSDA. It is shown that the relaxation effect for errors above the assimilation wavenumber ka is responsible for the error decay in ODA. Meanwhile, the errors contained in the large scales can propagate into small scales and make the high-wavenumber (k & gt;ka) error noise decay slower with TCDA than TSDA. This mechanism is further confirmed by incorporating different levels of errors in the large scales of the reference flow field. The advantage of TSDA is found to grow with the magnitude of the incorporated errors. Thus, it is potentially more beneficial to adopt TSDA if the reference data contain non-negligible errors. Finally, an outstanding issue raised in previous works regarding the possibility of recovering the dynamics of sub-Kolmogorov scales using direct numerical simulation data at a Kolmogorov scale resolution is also discussed.
    Type of Medium: Online Resource
    ISSN: 1070-6631 , 1089-7666
    URL: Article
    DOI: 10.1063/5.0091391
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 1472743-2
    detail.hit.zdb_id: 241528-8
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  • 4
    Online Resource
    Online Resource
    Deconvolutional artificial-neural-network framework for subfilter-scale models of compressible turbulence
    Springer Science and Business Media LLC ; 2021
    In:  Acta Mechanica Sinica Vol. 37, No. 12 ( 2021-12), p. 1773-1785
    Details
    In: Acta Mechanica Sinica, Springer Science and Business Media LLC, Vol. 37, No. 12 ( 2021-12), p. 1773-1785
    Type of Medium: Online Resource
    ISSN: 0567-7718 , 1614-3116
    URL: Article
    DOI: 10.1007/s10409-021-01150-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2181030-8
    detail.hit.zdb_id: 591522-3
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  • 5
    Online Resource
    Online Resource
    Artificial neural network-based spatial gradient models for large-eddy simulation of turbulence
    AIP Publishing ; 2021
    In:  AIP Advances Vol. 11, No. 5 ( 2021-05-01)
    Details
    In: AIP Advances, AIP Publishing, Vol. 11, No. 5 ( 2021-05-01)
    Abstract: The subgrid-scale stress (SGS) of large-eddy simulation (LES) is modeled by artificial neural network-based spatial gradient models (ANN-SGMs). The velocity gradients at neighboring stencil locations are incorporated to improve the accuracy of the SGS stress. The consideration of the gradient terms in the stencil locations is in a semi-explicit form so that the deployed artificial neural network (ANN) can be considerably simplified. This leads to a much higher LES efficiency compared with previous “black-box” models while still retaining the level of accuracy in the a priori test. The correlation coefficients of the ANN-SGMs can be larger than 0.98 for the filter width in the inertial range. With the current formulation, the significances of the individual modeling terms are transparent, giving clear guidance to the potential condensation of the model, which further improves the LES efficiency. The computational cost of the current ANN-SGM method is found to be two orders lower than previous “black-box” models. In the a posteriori test, the ANN-SGM framework predicts more accurately the flow field compared with the traditional LES models. Both the flow statistics and the instantaneous field are accurately recovered. Finally, we show that the current model can be adapted to different filter widths with sufficient accuracy. These results demonstrate the advantage and great potential of the ANN-SGM framework as an attractive solution to the closure problem in large-eddy simulation of turbulence.
    Type of Medium: Online Resource
    ISSN: 2158-3226
    URL: Article
    DOI: 10.1063/5.0053590
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2583909-3
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  • 6
    Online Resource
    Online Resource
    Deconvolutional artificial neural network models for large eddy simulation of turbulence
    AIP Publishing ; 2020
    In:  Physics of Fluids Vol. 32, No. 11 ( 2020-11-01)
    Details
    In: Physics of Fluids, AIP Publishing, Vol. 32, No. 11 ( 2020-11-01)
    Abstract: Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN models to reconstruct the unfiltered velocity. The grid width of the DANN models is chosen to be smaller than the filter width in order to accurately model the effects of SGS dynamics. The DANN models can predict the SGS stress more accurately than the conventional approximate deconvolution method and velocity gradient model in the a priori study: the correlation coefficients can be made larger than 99% and the relative errors can be made less than 15% for the DANN model. In an a posteriori study, a comprehensive comparison of the DANN model, the implicit LES (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) shows that the DANN model is superior to the ILES, DSM, and DMM models in the prediction of the velocity spectrum, various statistics of velocity, and the instantaneous coherent structures without increasing the considerable computational cost; the time for the DANN model to calculate the SGS stress is about 1.3 times that of the DMM model. In addition, the trained DANN models without any fine-tuning can predict the velocity statistics well for different filter widths. These results indicate that the DANN framework with the consideration of SGS spatial features is a promising approach to develop advanced SGS models in the LES of turbulence.
    Type of Medium: Online Resource
    ISSN: 1070-6631 , 1089-7666
    URL: Article
    DOI: 10.1063/5.0027146
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 1472743-2
    detail.hit.zdb_id: 241528-8
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  • 7
    Online Resource
    Online Resource
    Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence
    AIP Publishing ; 2020
    In:  Physics of Fluids Vol. 32, No. 11 ( 2020-11-01)
    Details
    In: Physics of Fluids, AIP Publishing, Vol. 32, No. 11 ( 2020-11-01)
    Abstract: In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at the Taylor Reynolds number Reλ ranging from 180 to 250. An ANN architecture is applied to construct the coefficients of the general NAM for the SGS anisotropy stress. It is shown that the ANN-NAMs can reconstruct the SGS stress accurately in the a priori test. Furthermore, the ANN-NAMs are analyzed by calculating the average, root mean square values, and probability density functions of dimensionless model coefficients. In an a posteriori analysis, we compared the performance of the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and ANN-NAM. The ANN-NAM yields good agreement with a filtered direct numerical simulation dataset for the spectrum, structure functions, and other statistics of velocity. Besides, the ANN-NAM predicts the instantaneous spatial structures of SGS anisotropy stress much better than the DSM and DMM. The NAM based on the ANN is a promising approach to deepen our understanding of SGS modeling in LES of turbulence.
    Type of Medium: Online Resource
    ISSN: 1070-6631 , 1089-7666
    URL: Article
    DOI: 10.1063/5.0025138
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 1472743-2
    detail.hit.zdb_id: 241528-8
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  • 8
    Online Resource
    Online Resource
    Dynamic nonlinear algebraic models with scale-similarity dynamic procedure for large-eddy simulation of turbulence
    Springer Science and Business Media LLC ; 2022
    In:  Advances in Aerodynamics Vol. 4, No. 1 ( 2022-04-08)
    Details
    In: Advances in Aerodynamics, Springer Science and Business Media LLC, Vol. 4, No. 1 ( 2022-04-08)
    Abstract: A dynamic nonlinear algebraic model with scale-similarity dynamic procedure (DNAM-SSD) is proposed for subgrid-scale (SGS) stress in large-eddy simulation of turbulence. The model coefficients of the DNAM-SSD model are adaptively calculated through the scale-similarity relation, which greatly simplifies the conventional Germano-identity based dynamic procedure (GID). The a priori study shows that the DNAM-SSD model predicts the SGS stress considerably better than the conventional velocity gradient model (VGM), dynamic Smagorinsky model (DSM), dynamic mixed model (DMM) and DNAM-GID model at a variety of filter widths ranging from inertial to viscous ranges. The correlation coefficients of the SGS stress predicted by the DNAM-SSD model can be larger than 95% with the relative errors lower than 30%. In the a posteriori testings of LES, the DNAM-SSD model outperforms the implicit LES (ILES), DSM, DMM and DNAM-GID models without increasing computational costs, which only takes up half the time of the DNAM-GID model. The DNAM-SSD model accurately predicts plenty of turbulent statistics and instantaneous spatial structures in reasonable agreement with the filtered DNS data. These results indicate that the current DNAM-SSD model is attractive for the development of highly accurate SGS models for LES of turbulence.
    Type of Medium: Online Resource
    ISSN: 2524-6992
    URL: Article
    DOI: 10.1186/s42774-022-00107-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2960941-0
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