[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Shan et al., 2023 - Google Patents

Turbulence modeling via data assimilation and machine learning for separated flows over airfoils

Shan et al., 2023

View PDF
Document ID
4633219346109591413
Author
Shan X
Liu Y
Cao W
Sun X
Zhang W
Publication year
Publication venue
AIAA Journal

External Links

Snippet

Reynolds-averaged Navier–Stokes (RANS) models, which are known for their efficiency and robustness, are widely used in engineering applications. However, RANS models do not provide satisfactory predictive accuracy in many engineering-relevant flows with separation …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G06F17/504—Formal methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G06F17/5036—Computer-aided design using simulation for analog modelling, e.g. for circuits, spice programme, direct methods, relaxation methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5086—Mechanical design, e.g. parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/46—Fuselage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/16—Numerical modeling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/80—Thermal analysis and optimization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education

Similar Documents

Publication Publication Date Title
Singh et al. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils
Thuerey et al. Deep learning methods for Reynolds-averaged Navier–Stokes simulations of airfoil flows
Chen et al. Padgan: Learning to generate high-quality novel designs
Yu et al. Flowfield reconstruction method using artificial neural network
Ray et al. Bayesian parameter estimation of ak-ε model for accurate jet-in-crossflow simulations
Shan et al. Turbulence modeling via data assimilation and machine learning for separated flows over airfoils
Li et al. Deep learning for multifidelity aerodynamic distribution modeling from experimental and simulation data
Liu et al. Modeling multiresponse surfaces for airfoil design with multiple-output-Gaussian-process regression
Edeling et al. Bayesian predictions of Reynolds-averaged Navier–Stokes uncertainties using maximum a posteriori estimates
CN113420507B (en) Fluid flow simulation method, device and medium
Taghizadeh et al. Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks
Mendonça et al. Model order reduction in aerodynamics: Review and applications
Peng et al. Multi-fidelity nonlinear unsteady aerodynamic modeling and uncertainty estimation based on Hierarchical Kriging
Gao et al. On the dispersion mechanism of the flutter boundary of the AGARD 445.6 wing
Galbraith et al. Verification of unstructured grid adaptation components
Alguacil et al. Deep learning surrogate for the temporal propagation and scattering of acoustic waves
Roychowdhury Computational fluid dynamics for incompressible flows
Lowe et al. Efficient Flutter Prediction Using Reduced-Order Modeling
Zhang et al. A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier–Stokes simulations
Fusi et al. Multifidelity physics-based method for robust optimization applied to a hovering rotor airfoil
Rozza et al. Reduced basis methods and a posteriori error estimators for heat transfer problems
Nguyen Model reduction techniques for parametrized nonlinear partial differential equations
Immordino et al. Steady-state transonic flowfield prediction via deep-learning framework
Belligoli et al. Reconstruction of turbulent flows at high Reynolds numbers using data assimilation techniques
Holland Integrated field inversion and machine learning with embedded neural network training for turbulence modeling