Výsledky vyhledávání - "Matha, Marcel"

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Physically constrained eigenspace perturbation for turbulence model uncertainty estimation

Autor: Matha, Marcel, Morsbach, Christian

Publikováno v: Physics of Fluids, Vol.36, Issue 2, 2024

Aerospace design is increasingly incorporating Design Under Uncertainty based approaches to lead to more robust and reliable optimal designs. These approaches require dependable estimates of uncertainty in simulations for their success. The key contr

Externí odkaz: http://arxiv.org/abs/2311.01355

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Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

Autor: Matha, Marcel, Morsbach, Christian

To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to account for epistemic uncertainty of turbulence models. In order to eliminat

Externí odkaz: http://arxiv.org/abs/2306.13370

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Improved self-consistency of the Reynolds stress tensor eigenspace perturbation for uncertainty quantification

Autor: Matha, Marcel, Morsbach, Christian

Publikováno v: Physics of Fluids, Vol.35, Issue 6, 2023

The limitations of turbulence closure models in the context of Reynolds-averaged NavierStokes (RANS) simulations play a significant part in contributing to the uncertainty of Computational Fluid Dynamics (CFD). Perturbing the spectral representation

Externí odkaz: http://arxiv.org/abs/2303.06149

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Applicability of machine learning in uncertainty quantification of turbulence models

Autor: Matha, Marcel, Kucharczyk, Karsten

White paper: The aim of this work is to apply and analyze machine learning methods for uncertainty quantification of turbulence models. In this work we investigate the classical and data-driven variants of the eigenspace perturbation method. This met

Externí odkaz: http://arxiv.org/abs/2210.16358

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Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification

Autor: Matha, Marcel, Kucharczyk, Karsten, Morsbach, Christian

Publikováno v: Computers & Fluids, 255, 2023, 105837

In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the overall un

Externí odkaz: http://arxiv.org/abs/2210.00002

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Extending turbulence model uncertainty quantification using machine learning

Autor: Matha, Marcel, Morsbach, Christian

In order to achieve a more virtual design and certification process of jet engines in aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of a machine learning methodology to qu

Externí odkaz: http://arxiv.org/abs/2202.01560

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