The Experts below are selected from a list of 3483 Experts worldwide ranked by ideXlab platform
Jinjun Wang - One of the best experts on this subject based on the ideXlab platform.
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vortex to velocity reconstruction for wall bounded turbulence via the field based linear stochastic estimation
Journal of Fluid Mechanics, 2021Co-Authors: Chengyue Wang, Qi Gao, Biao Wang, Chong Pan, Jinjun WangAbstract:Representing complex flows by evolving vortex structures is an important principle in many investigations of wall-bounded turbulence. The practice of this principle benefits from the bi-directional transformation between the velocity field and the corresponding vortex field. While the velocity-to-vortex transformation could be implemented by various vortex identification criteria, few efforts have been devoted to the inverse process. This work develops a linear reconstruction method, which allows an effective reconstruction for the velocity field of wall turbulence based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The reconstructed velocity fields are calculated by convolution operations on the vortex fields, with the kernel functions derived by the field-based linear stochastic estimation. The method can effectively recover the turbulent motions in a large scale range, showing clear advantages over the Biot–Savart formula in the near-wall region. The method is also employed to investigate the inducing effects of vortices at different heights. The wall-bounding effect on the induced motions is observed from the contribution spectra of vortices. The higher-order moments of the reconstructed streamwise velocity component present larger deviations from the original data, which is discussed and explained reasonably. At last, the vortex fields filtered by prescribed thresholds are employed to reconstruct the velocity fields. It is found that the strongest vortex components occupying 5 % of the total volume can reasonably recover the main flow features including both the near-wall streaks and the large-scale motions.
Qi Gao - One of the best experts on this subject based on the ideXlab platform.
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vortex to velocity reconstruction for wall bounded turbulence via the field based linear stochastic estimation
Journal of Fluid Mechanics, 2021Co-Authors: Chengyue Wang, Qi Gao, Biao Wang, Chong Pan, Jinjun WangAbstract:Representing complex flows by evolving vortex structures is an important principle in many investigations of wall-bounded turbulence. The practice of this principle benefits from the bi-directional transformation between the velocity field and the corresponding vortex field. While the velocity-to-vortex transformation could be implemented by various vortex identification criteria, few efforts have been devoted to the inverse process. This work develops a linear reconstruction method, which allows an effective reconstruction for the velocity field of wall turbulence based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The reconstructed velocity fields are calculated by convolution operations on the vortex fields, with the kernel functions derived by the field-based linear stochastic estimation. The method can effectively recover the turbulent motions in a large scale range, showing clear advantages over the Biot–Savart formula in the near-wall region. The method is also employed to investigate the inducing effects of vortices at different heights. The wall-bounding effect on the induced motions is observed from the contribution spectra of vortices. The higher-order moments of the reconstructed streamwise velocity component present larger deviations from the original data, which is discussed and explained reasonably. At last, the vortex fields filtered by prescribed thresholds are employed to reconstruct the velocity fields. It is found that the strongest vortex components occupying 5 % of the total volume can reasonably recover the main flow features including both the near-wall streaks and the large-scale motions.
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Vortex-to-velocity reconstruction for wall-bounded turbulence via a data-driven model
2020Co-Authors: Wang Chengyue, Qi Gao, Wang Biao, Pan Chong, Wang JinjunAbstract:Modelling the vortex structures and then translating them into the corresponding velocity fields are two essential aspects for the vortex-based modelling works in wall-bounded turbulence. This work develops a datadriven method, which allows an effective reconstruction for the velocity field based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The distinctive properties for the vortex field are investigated, with the relationship between the vortex magnitude and orientation revealed by the differential geometry. The vortex-to-velocity reconstruction method incorporates the vortex-vortex and vortex-velocity correlation information and derives the inducing model functions under the framework of the linear stochastic estimation. Fast Fourier transformation is employed to improve the computation efficiency in implementation. The reconstruction accuracy is accessed and compared with the widely-used Biot-Savart law. Results show that the method can effectively recover the turbulent motions in a large scale range, which is very promising for the turbulence modelling. The method is also employed to investigate the inducing effects of vortices at different heights, and some revealing results are discussed and linked to the hot research topics in wall-bounded turbulence
Chengyue Wang - One of the best experts on this subject based on the ideXlab platform.
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vortex to velocity reconstruction for wall bounded turbulence via the field based linear stochastic estimation
Journal of Fluid Mechanics, 2021Co-Authors: Chengyue Wang, Qi Gao, Biao Wang, Chong Pan, Jinjun WangAbstract:Representing complex flows by evolving vortex structures is an important principle in many investigations of wall-bounded turbulence. The practice of this principle benefits from the bi-directional transformation between the velocity field and the corresponding vortex field. While the velocity-to-vortex transformation could be implemented by various vortex identification criteria, few efforts have been devoted to the inverse process. This work develops a linear reconstruction method, which allows an effective reconstruction for the velocity field of wall turbulence based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The reconstructed velocity fields are calculated by convolution operations on the vortex fields, with the kernel functions derived by the field-based linear stochastic estimation. The method can effectively recover the turbulent motions in a large scale range, showing clear advantages over the Biot–Savart formula in the near-wall region. The method is also employed to investigate the inducing effects of vortices at different heights. The wall-bounding effect on the induced motions is observed from the contribution spectra of vortices. The higher-order moments of the reconstructed streamwise velocity component present larger deviations from the original data, which is discussed and explained reasonably. At last, the vortex fields filtered by prescribed thresholds are employed to reconstruct the velocity fields. It is found that the strongest vortex components occupying 5 % of the total volume can reasonably recover the main flow features including both the near-wall streaks and the large-scale motions.
Chong Pan - One of the best experts on this subject based on the ideXlab platform.
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vortex to velocity reconstruction for wall bounded turbulence via the field based linear stochastic estimation
Journal of Fluid Mechanics, 2021Co-Authors: Chengyue Wang, Qi Gao, Biao Wang, Chong Pan, Jinjun WangAbstract:Representing complex flows by evolving vortex structures is an important principle in many investigations of wall-bounded turbulence. The practice of this principle benefits from the bi-directional transformation between the velocity field and the corresponding vortex field. While the velocity-to-vortex transformation could be implemented by various vortex identification criteria, few efforts have been devoted to the inverse process. This work develops a linear reconstruction method, which allows an effective reconstruction for the velocity field of wall turbulence based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The reconstructed velocity fields are calculated by convolution operations on the vortex fields, with the kernel functions derived by the field-based linear stochastic estimation. The method can effectively recover the turbulent motions in a large scale range, showing clear advantages over the Biot–Savart formula in the near-wall region. The method is also employed to investigate the inducing effects of vortices at different heights. The wall-bounding effect on the induced motions is observed from the contribution spectra of vortices. The higher-order moments of the reconstructed streamwise velocity component present larger deviations from the original data, which is discussed and explained reasonably. At last, the vortex fields filtered by prescribed thresholds are employed to reconstruct the velocity fields. It is found that the strongest vortex components occupying 5 % of the total volume can reasonably recover the main flow features including both the near-wall streaks and the large-scale motions.
Biao Wang - One of the best experts on this subject based on the ideXlab platform.
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vortex to velocity reconstruction for wall bounded turbulence via the field based linear stochastic estimation
Journal of Fluid Mechanics, 2021Co-Authors: Chengyue Wang, Qi Gao, Biao Wang, Chong Pan, Jinjun WangAbstract:Representing complex flows by evolving vortex structures is an important principle in many investigations of wall-bounded turbulence. The practice of this principle benefits from the bi-directional transformation between the velocity field and the corresponding vortex field. While the velocity-to-vortex transformation could be implemented by various vortex identification criteria, few efforts have been devoted to the inverse process. This work develops a linear reconstruction method, which allows an effective reconstruction for the velocity field of wall turbulence based on a given vortex field. The vortex field is defined as a vector field by combining the swirl strength and the Real Eigenvector of the velocity gradient tensor. The reconstructed velocity fields are calculated by convolution operations on the vortex fields, with the kernel functions derived by the field-based linear stochastic estimation. The method can effectively recover the turbulent motions in a large scale range, showing clear advantages over the Biot–Savart formula in the near-wall region. The method is also employed to investigate the inducing effects of vortices at different heights. The wall-bounding effect on the induced motions is observed from the contribution spectra of vortices. The higher-order moments of the reconstructed streamwise velocity component present larger deviations from the original data, which is discussed and explained reasonably. At last, the vortex fields filtered by prescribed thresholds are employed to reconstruct the velocity fields. It is found that the strongest vortex components occupying 5 % of the total volume can reasonably recover the main flow features including both the near-wall streaks and the large-scale motions.
