10 citations · 27 across the 6 of their papers we have counts for
6 papers
Physically constrained eigenspace perturbation for turbulence model uncertainty estimation
Marcel Matha, Christian Morsbach
Aerospace design is increasingly incorporating Design Under Uncertainty based approaches to lead to more robust and reliable optimal designs. These approaches require dependable es…
Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation
Marcel Matha, Christian Morsbach
To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to accoun…
Improved self-consistency of the Reynolds stress tensor eigenspace perturbation for uncertainty quantification
Marcel Matha, Christian Morsbach
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 Compu…
Applicability of machine learning in uncertainty quantification of turbulence models
Marcel Matha, Karsten Kucharczyk
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 an…
Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification
Marcel Matha, Karsten Kucharczyk, Christian Morsbach
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…
Extending turbulence model uncertainty quantification using machine learning
Marcel Matha, Christian Morsbach
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. T…