We consider the frequency domain form of proper orthogonal decomposition (POD), called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space–time POD problem for statistically stationary flows and leads to modes that each oscillate at a single frequency. This form of POD goes back to the original work of Lumley (Stochastic Tools in Turbulence, Academic Press, 1970), but has been overshadowed by a space-only form of POD since the 1990s. We clarify the relationship between these two forms of POD and show that SPOD modes represent structures that evolve coherently in space and time, while space-only POD modes in general do not. We also establish a relationship between SPOD and dynamic mode decomposition (DMD); w...
For many decades, turbulence has been the subject of extensive numerical research and experimental w...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...
We consider the frequency domain form of proper orthogonal decomposition (POD), called spectral prop...
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, ...
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, ...
In this thesis, we introduce a novel model order reduction framework for harmonically and randomly f...
In this thesis, we introduce a novel model order reduction framework for harmonically and randomly f...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
Even though fluid flows possess an exceedingly high number of degrees of freedom, their dynamics oft...
International audienceData-driven low-order modelling has been enjoying rapid advances in fluid mech...
International audienceA novel data-driven modal decomposition of fluid flow is proposed, comprising ...
For many decades, turbulence has been the subject of extensive numerical research and experimental w...
For many decades, turbulence has been the subject of extensive numerical research and experimental w...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...
We consider the frequency domain form of proper orthogonal decomposition (POD), called spectral prop...
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, ...
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, ...
In this thesis, we introduce a novel model order reduction framework for harmonically and randomly f...
In this thesis, we introduce a novel model order reduction framework for harmonically and randomly f...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
This article describes a proper-orthogonal-decomposition (POD) based methodology proposed for the id...
Even though fluid flows possess an exceedingly high number of degrees of freedom, their dynamics oft...
International audienceData-driven low-order modelling has been enjoying rapid advances in fluid mech...
International audienceA novel data-driven modal decomposition of fluid flow is proposed, comprising ...
For many decades, turbulence has been the subject of extensive numerical research and experimental w...
For many decades, turbulence has been the subject of extensive numerical research and experimental w...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...
The Proper Orthogonal Decomposition (POD) uses data to generate an optimal set of basis functions th...