Journal ArticleDOI
Artificial neural network mixed model for large eddy simulation of compressible isotropic turbulence
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This article is published in Physics of Fluids.The article was published on 2019-08-01. It has received 63 citations till now. The article focuses on the topics: Large eddy simulation & Isotropy.read more
Citations
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Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence
TL;DR: Spatial artificial neural network (SANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation of turbulence, whose input features are based on the derivatives of the filtered field at different spatial locations as discussed by the authors.
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A new prediction method of unsteady wake flow by the hybrid deep neural network
TL;DR: The predicted flow fields using the trained hybrid DNNs are in good agreement with the flow fields calculated directly by the computational fluid dynamic solver.
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Data-driven modeling for unsteady aerodynamics and aeroelasticity
TL;DR: Three typical data-driven aerodynamic methods are introduced, including system identification, feature extraction and data fusion, which help to gain physical insights on flow mechanism and have shown great potential in engineering applications like flow control, aeroelasticity and optimization.
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A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network
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An interpretable framework of data-driven turbulence modeling using deep neural networks
TL;DR: This paper presents a universally interpretable machine learning (UIML) framework for turbulence modeling, which consists of two parallel machine learning-based modules to directly infer the structural and parametric representations of turbulence physics, respectively.
References
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Journal ArticleDOI
General circulation experiments with the primitive equations
TL;DR: In this article, an extended period numerical integration of a baroclinic primitive equation model has been made for the simulation and the study of the dynamics of the atmosphere's general circulation, and the solution corresponding to external gravitational propagation is filtered by requiring the vertically integrated divergence to vanish identically.
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A dynamic subgrid‐scale eddy viscosity model
TL;DR: In this article, a new eddy viscosity model is presented which alleviates many of the drawbacks of the existing subgrid-scale stress models, such as the inability to represent correctly with a single universal constant different turbulent fields in rotating or sheared flows, near solid walls, or in transitional regimes.
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Compact finite difference schemes with spectral-like resolution
TL;DR: In this article, the authors present finite-difference schemes for the evaluation of first-order, second-order and higher-order derivatives yield improved representation of a range of scales and may be used on nonuniform meshes.
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A proposed modification of the Germano subgrid‐scale closure method
TL;DR: In this paper, the subgrid-scale closure method developed by Germano et al. is modified by use of a least squares technique to minimize the difference between the closure assumption and the resolved stresses.
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Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.