Modal decomposition of the pressure field on a bridge deck under vortex shedding using POD, DMD and ERA with correlation functions as Markov parameters

Andersen, Michael Styrk; Bossen, Niels Skak

doi:10.1016/j.jweia.2021.104699

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…Model complexity in pDMDc for metabotyping, i.e., the choice of

and

(for simplicity we choose

), was assessed by identifying the inflection point in the scree plot of the singular values derived from the SVD

( 28 , 29 ). Further, we classified the

most dominant dynamical profile by clustering the covariance matrix

of the measured data for each metabolite averaged over all individuals and diets

( Equations 23, 24 ).…”

Section: Methodsmentioning

confidence: 99%

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Skantze,

Jirstrand,

Brunius

et al. 2024

Front. Nutr.

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MotivationIn the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures.Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data.Combining the two recent developments “parametric DMD” (pDMD) and “DMD with control” (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data.ResultsWe demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size (Rmax2= 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels.Availability and implementationThe measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.

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“…Model complexity in pDMDc for metabotyping, i.e., the choice of

and

(for simplicity we choose

), was assessed by identifying the inflection point in the scree plot of the singular values derived from the SVD

( 28 , 29 ). Further, we classified the

most dominant dynamical profile by clustering the covariance matrix

of the measured data for each metabolite averaged over all individuals and diets

( Equations 23, 24 ).…”

Section: Methodsmentioning

confidence: 99%

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Skantze,

Jirstrand,

Brunius

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

show abstract

“…The dynamic system’s nonlinear behavior can result in non-classical damping, observed not only in building structures but also in air and spacecraft structures under various loading conditions [ 11 ]. To tackle nonlinear identification, linear models such as the eigenvalue realization algorithm (ERA) have proven to be effective [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian operational modal analysis (BAYOMA) method operating in the frequency domain and applicable for close modes has recently been developed based on an expectation-maximization algorithm that shows promise for simpler algorithm and computer coding [ 13 ]. Recent developments in the field of operational modal analysis (OMA) and blind source separation (BSS) have attracted considerable interest in separating modal response and estimating dynamic properties [ 6 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Verification of the Mode Decomposition Technique for Closely Distributed Modal Systems in the State Space Domain

Noh

Hwang

2023

Sensors

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This study aims to propose and validate the state space mode decomposition technique for precise mode separation of non-classical damping systems and closely distributed modal systems. To assess the reliability and applicability of this technique, a 40-story building with a tuned mass damper is investigated, and acceleration responses measured by the building’s health monitoring system are used for the verification of the technique. The mode separation results reveal that the separated modal power spectrum becomes distorted at neighboring natural frequency ranges when the performance index only considers the concentration of power spectral energy at the target natural frequency. However, by introducing an augmented performance index that includes a constraint condition to account for distortion, more accurate mode decomposition can be achieved.

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Review of OpenFOAM applications in the computational wind engineering: from wind environment to wind structural engineering

Ricci

2024

Meccanica

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The use of computational fluid dynamics (CFD) in the wind engineering (WE) is generally defined as computational wind engineering (CWE). Since its foundation in 2004, the use of OpenFOAM in CWE has been increasing progressively and covers nowadays a wide range of topics, from wind environment to wind structural engineering. This paper was drafted in response to the invitation from the organizers of the 18th OpenFOAM workshop held in Genoa (Italy) on 11–14 July 2023, when a technical session on Civil Engineering and Wind Engineering was organized. In this paper the author briefly reviews the history of WE and surveys the evolution, methods, and future challenges of OpenFOAM in the CWE. Topics are here regrouped into three main research areas and discussed from a physical, engineering and purely computational perspective. The study does not cover the Wind Energy and related topics, since this can be considered nowadays as a stand-alone subfield of the WE. This review confirms that OpenFOAM is a versatile tool widely used for WE applications that often require new models to be developed ad hoc by CFD users. It can be coupled easily with numerical weather prediction models for mesoscale-microscale wind and thermal studies, with building energy simulation models to determine the energy demand, with finite element method for structural engineering design. OpenFOAM represents an extraordinary opportunity for all CFD users worldwide to share codes and case studies, to explore the potential of new functionalities and strengthen the network within the CFD community.

show abstract

Modal decomposition of the pressure field on a bridge deck under vortex shedding using POD, DMD and ERA with correlation functions as Markov parameters

Cited by 11 publications

References 18 publications

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Verification of the Mode Decomposition Technique for Closely Distributed Modal Systems in the State Space Domain

Review of OpenFOAM applications in the computational wind engineering: from wind environment to wind structural engineering

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