This report develops an ensemble or statistical eddy viscosity model. The model is parameterized by an ensemble of solutions of an ensemble-Leray regularization. The combined approach of ensemble time stepping and ensemble eddy viscosity modeling allows direct parametrization of the turbulent viscosity coefficient. We prove unconditional stability and that the model\u27s solution approaches statistical equilibrium as t → ∞ the model\u27s variance converges to zero as t → ∞. The ensemble method is used to interrogate a rotating flow, testing its predictability by computing effective averaged Lyapunov exponents
The aim of this note is twofold: we review some basic results which are at the basis of the derivati...
Turbulence appears in many processes in the nature and it is connected with many engineering, biophy...
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressi...
Computing Ensembles occurs frequently in the simulation of complex flows to increase forecasting ski...
This report analyzes an efficient ensemble regularization algorithm for under-resolved and convectio...
This paper introduces an ensemble-based field inversion framework to augment the turbulence models b...
The primary goals of ensemble prediction are the identification of particularly unpredictable situat...
This report presents an efficient, higher order method for fast calculation of an ensemble of soluti...
An ensemble model of turbulence is proposed. The ensemble consists of flow fields in which the flux ...
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, ...
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, ...
Describes \u27current\u27 state of model developed by Taylor (1935), which is applicable to continuo...
The invariance theory in continuum mechanics is applied to analyze Reynolds stresses in high Reynold...
© 2019 by the American Institute of Aeronautics and Astronautics, Inc. In chaotic systems, such as t...
Turbulence readily arises in numerous flows in nature and technology. The large number of degrees of...
The aim of this note is twofold: we review some basic results which are at the basis of the derivati...
Turbulence appears in many processes in the nature and it is connected with many engineering, biophy...
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressi...
Computing Ensembles occurs frequently in the simulation of complex flows to increase forecasting ski...
This report analyzes an efficient ensemble regularization algorithm for under-resolved and convectio...
This paper introduces an ensemble-based field inversion framework to augment the turbulence models b...
The primary goals of ensemble prediction are the identification of particularly unpredictable situat...
This report presents an efficient, higher order method for fast calculation of an ensemble of soluti...
An ensemble model of turbulence is proposed. The ensemble consists of flow fields in which the flux ...
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, ...
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, ...
Describes \u27current\u27 state of model developed by Taylor (1935), which is applicable to continuo...
The invariance theory in continuum mechanics is applied to analyze Reynolds stresses in high Reynold...
© 2019 by the American Institute of Aeronautics and Astronautics, Inc. In chaotic systems, such as t...
Turbulence readily arises in numerous flows in nature and technology. The large number of degrees of...
The aim of this note is twofold: we review some basic results which are at the basis of the derivati...
Turbulence appears in many processes in the nature and it is connected with many engineering, biophy...
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressi...