Study of fluid–thermal–structural interaction in high-temperature high-speed flow using multi-fidelity multi-variate surrogates

Huang, Daning; Sadagopan, Aravinth; Düzel, Ümran; Hanquist, Kyle M.

doi:10.1016/j.jfluidstructs.2022.103682

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…In the context of dynamical systems, the original MVGPR is defined over a single input [22], time; for the general case of multi-dimensional inputs, see Refs. [20,21].…”

Section: B Multivariate Gaussian Process Regressionmentioning

confidence: 99%

“…Similar to the univariate case, the prediction at a new input 𝑡 * is represented using a multivariate Gaussian distribution; see Refs. [20,21,28] for details.…”

Section: Multivariate Gprmentioning

confidence: 99%

“…The MVGPR identifies the dominant modes and generates the mapping from input to modal coordinates simultaneously [21]. As a result, the MVGPR modes are different from the modes obtained in standard methods, e.g., POD or principle component analysis (PCA), because the latter does not account for the distribution of inputs.…”

Section: Multivariate Gprmentioning

confidence: 99%

“…The error in DMD is attributed to the interpolation procedure that contaminates the frequency components. Figure 5 shows the difference between the identified modes and true DMD modes for a range of retained percentages using the concept of Grassmannian distance [21,31]. Unlike the inner product metric that compares two sets of vectors in a pairwise manner, the Grassmannian distance directly compares the subspace spanned by two sets of vectors.…”

Section: Fig 5 Reconstructed Flow With Cubic Interpolation For 24% Me...mentioning

confidence: 99%

“…Building on the general-purpose MVGPR framework developed previously by the authors [20][21][22], this study aims to demonstrate the feasibility of GPR-based modal analysis of spatiotemporal data, as an alternative to DMD and SPOD methods. Specifically, the goals of this paper are to 1) Present the theoretical formulation of MVGPR for modal analysis of spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

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Modal Analysis of Spatiotemporal Data via Multi-fidelity Multi-variate Gaussian Processes

Song

Huang

2023

AIAA AVIATION 2023 Forum

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Modal analysis has become an essential tool to understand the coherent structure of complex flows. The classical modal analysis methods, such as dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD), rely on a sufficient amount of data that is regularly sampled in time. However, often one needs to deal with sparse temporally irregular data, e.g., due to experimental measurements and simulation algorithm. To overcome the limitations of data scarcity and irregular sampling, we propose a novel modal analysis technique using multi-variate Gaussian process regression (MVGPR). We first establish the connection between MVGPR and the existing modal analysis techniques, DMD and SPOD, from a linear system identification perspective. Next, leveraging this connection, we develop a MVGPR-based modal analysis technique that addresses the aforementioned limitations. The capability of MVGPR is endowed by its judiciously designed kernel structure for correlation function, that is derived from the assumed linear dynamics. Subsequently, the proposed MVGPR

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“…In the context of dynamical systems, the original MVGPR is defined over a single input [22], time; for the general case of multi-dimensional inputs, see Refs. [20,21].…”

Section: B Multivariate Gaussian Process Regressionmentioning

confidence: 99%

“…Similar to the univariate case, the prediction at a new input 𝑡 * is represented using a multivariate Gaussian distribution; see Refs. [20,21,28] for details.…”

Section: Multivariate Gprmentioning

confidence: 99%

Section: Multivariate Gprmentioning

confidence: 99%

Section: Fig 5 Reconstructed Flow With Cubic Interpolation For 24% Me...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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