A study of longitudinal processes and interactions in compressible viscous flows

Mao, Feng; Kang, Linlin; Wu, Jing; Yu, Jia-Long; Gao, An-Kang; Su, Weidong; Lu, Xi‐Yun

doi:10.1017/jfm.2020.213

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…As pointed out by Mao et al. (2020), the negative values usually occur at the vortex core location where rotation dominates, and the positive values at the place where shear dominates. Here, near the serration edge, there is side vortex pair resulting in the negative value of .…”

Section: Resultsmentioning

confidence: 68%

“…Recently, Mao et al. (2020) derived a more general form of the acoustic equation, in which the noise source has been defined in a more physical way. Here, the following is a brief introduction to the theoretical derivation of the acoustic equation.…”

Section: Numerical Proceduresmentioning

confidence: 99%

“…This source term () can be regarded as the pseudo-‘inviscid work rate’ done by the nonlinear inertial force over a unit volume (Mao et al. 2020).…”

Section: Numerical Proceduresmentioning

confidence: 99%

“…On the other hand, a more physical flow noise source defined in the wave equation proposed in Mao et al. (2020) is utilized, which allows us to quantitatively assess the effect of serration on flow noise sources.…”

Section: Introductionmentioning

confidence: 99%

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Noise reduction mechanisms for insert-type serrations of the NACA-0012 airfoil

Wan

et al. 2022

J. Fluid Mech.

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Trailing-edge serrations inspired by owls are capable of reducing broadband noise. In this study, the wall-resolved large-eddy simulations (LES) are carried out on the flow over NACA-0012 airfoil with additional serrated trailing edges. The computations are conducted with the high-order flux reconstruction method on unstructured meshes. Three kinds of serrations with different lengths are studied and compared with the straight trailing-edge case, and all three types of serration achieved a certain degree of noise reduction. Presently, the medium-length serration achieves the best noise reduction effect. The maximum decrease of overall sound pressure level is approximately 2.4 dB, implying that the length of serration has a substantial impact on the noise reduction effect. The serration has no significant effect on the upstream turbulence statistics, but it changes the flow structure near the serration, such as inducing side vortex pairs attached to the serration edges. Moreover, dynamic mode decomposition shows that the pressure structures vary with the serration length. For the most unstable hydrodynamic wave, the spanwise coherence of the mode structure of pressure in the upstream boundary layer is weakened. In addition, serrations can redistribute the dipole sources on the surfaces of airfoil and serrations. The destructive interference is enhanced to some extent, which is favourable for noise reduction. In contrast with LES simulations, the pure dipole analysis shows that the longest serration case seems to be the best. Furthermore, a recently developed noise theory is used to evaluate the influence of serrations on the flow noise sources qualitatively and quantitatively. It is found that the serrations can mitigate noise source intensity near the serration edges but increase the source intensity in the near wake. The combined effect of serration on the dipole source and flow noise source determines the overall noise reduction effect. To conclude, destructive interference plays a primary role in suppressing noise radiation by serrated trailing edges, and the dual effect of flow noise sources should be considered in future serration designs. As the influence of turbulence structure will make it more difficult to find the optimal serration parameters, the position of high-fidelity simulation will become increasingly important.

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Section: Resultsmentioning

confidence: 68%

Section: Numerical Proceduresmentioning

confidence: 99%

“…This source term () can be regarded as the pseudo-‘inviscid work rate’ done by the nonlinear inertial force over a unit volume (Mao et al. 2020).…”

Section: Numerical Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Noise reduction mechanisms for insert-type serrations of the NACA-0012 airfoil

Wan

et al. 2022

J. Fluid Mech.

Self Cite

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“…Hyperbolic conservation law (HCL) is a long-standing subject in the fields of magnetohydrodynamics [8], hydrodynamics [3], aerodynamic [18], combustion physics [33], and nuclear physics [29]. It is often challenging to develop advanced numerical HCL solvers that is capable of simultaneously resolving small-scale flow structures [12,24] and capturing discontinuities [1] [4].…”

Section: Introductionmentioning

confidence: 99%

RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data

Xiong,

He,

Tong

et al. 2020

Preprint

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We introduce Roe Neural Networks (RoeNets) that can predict the discontinuity of the hyperbolic conservation laws (HCLs) based on short-term discontinuous and even continuous training data. Our methodology is inspired by Roe approximate Riemann solver (P. L. Roe, J. Comput. Phys., vol. 43, 1981, pp. 357-372), which is one of the most fundamental HCLs numerical solvers. In order to accurately solve the HCLs, Roe argues the need to construct a Roe matrix that fulfills "Property U", including diagonalizable with real eigenvalues, consistent with the exact Jacobian, and preserving conserved quantities. However, the construction of such matrix cannot be achieved by any general numerical method. Our model made a breakthrough improvement in solving the HCLs by applying Roe solver under a neural network perspective. To enhance the expressiveness of our model, we incorporate pseudoinverses into a novel context to enable a hidden dimension so that we are flexible with the number of parameters. The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches. We demonstrate that our model can generate highly accurate predictions of evolution of convection without dissipation and the discontinuity of hyperbolic systems from smooth training data.

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RoeNet: Predicting discontinuity of hyperbolic systems from continuous data

Tong,

Xiong,

et al. 2023

Numerical Meth Engineering

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Predicting future discontinuous phenomena that are unobservable from training data sets has long been a challenging problem in scientific machine learning. We introduce a novel paradigm to predict the emergence and evolution of various discontinuities of hyperbolic partial differential equations (PDEs) based on given training data over a short window with limited discontinuity information. Our method is inspired by the classical Roe solver [P. L. Roe, J Comput Phys., vol. 43, 1981], a basic tool for simulating various hyperbolic PDEs in computational physics. By carefully designing the computing primitives, the data flow, and the novel pseudoinverse processing module, we enable our data‐driven predictor to satisfy all the essential mathematical criteria of a Roe solver and hence deliver accurate predictions of hyperbolic PDEs. We demonstrate through various examples that our data‐driven Roe predictor outperforms original human‐designed Roe solver and deep neural networks with weak priors in terms of accuracy and robustness.

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A study of longitudinal processes and interactions in compressible viscous flows

Cited by 12 publications

References 38 publications

Noise reduction mechanisms for insert-type serrations of the NACA-0012 airfoil

Noise reduction mechanisms for insert-type serrations of the NACA-0012 airfoil

RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data

RoeNet: Predicting discontinuity of hyperbolic systems from continuous data

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