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Quantifying Parametric Uncertainty in Turbulence Model for the Numerical Simulation of Atmospheric Boundary Layer Flows Using Dimensionality Reduction and Bayesian Regression

par Li, Haoyan ;Amaduzzi, Ruggero ;Mosca, Gabriele M.G.;Raghunathan Srikumar, Sampath Kumar;Gambale, Alessandro ;Parente, Alessandro
Référence Journal of wind engineering and industrial aerodynamics, 272, page (106397)
Publication Publié, 2026-02-26

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Titre:	Quantifying Parametric Uncertainty in Turbulence Model for the Numerical Simulation of Atmospheric Boundary Layer Flows Using Dimensionality Reduction and Bayesian Regression
Auteur:	Li, Haoyan; Amaduzzi, Ruggero; Mosca, Gabriele M.G.; Raghunathan Srikumar, Sampath Kumar; Gambale, Alessandro; Parente, Alessandro
Informations sur la publication:	Journal of wind engineering and industrial aerodynamics, 272, page (106397)
Statut de publication:	Publié, 2026-02-26
Sujet CREF:	Sciences de l'ingénieur
Mots-clés:	Gaussian process regression
	Parameter calibration
	Parametric uncertainty
	Proper orthogonal decomposition
	Surrogate model
	Turbulence model
	Uncertainty quantification
Note générale:	SCOPUS: ar.j
Langue:	Anglais
Identificateurs:	urn:issn:0167-6105
	info:doi/10.1016/j.jweia.2026.106397
	info:pii/S0167610526000668
	info:scp/105031436965