scholarly journals Global Sensitivity Analysis and Quantification of Model Error for Large Eddy Simulation in Scramjet Design

2017 ◽  
Author(s):  
Xun Huan ◽  
Cosmin Safta ◽  
Khachik Sargsyan ◽  
Gianluca Geraci ◽  
Michael S. Eldred ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Large Eddy Simulation ◽  
Global Sensitivity Analysis ◽  
Model Error ◽  
Global Sensitivity ◽  
Eddy Simulation ◽  
Large Eddy

Global Sensitivity Analysis for Patient-Specific Aortic Simulations: The Role of Geometry, Boundary Condition and Large Eddy Simulation Modeling Parameters

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Huijuan Xu ◽  
Davide Baroli ◽  
Alessandro Veneziani
Keyword(s):  
Stochastic Process ◽  
Sensitivity Analysis ◽  
Large Eddy Simulation ◽  
Numerical Simulations ◽  
Global Sensitivity Analysis ◽  
Patient Specific ◽  
Global Sensitivity ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Inflow Conditions

Abstract Numerical simulations for computational hemodynamics in clinical settings require a combination of many ingredients, mathematical models, solvers and patient-specific data. The sensitivity of the solutions to these factors may be critical, particularly when we have a partial or noisy knowledge of data. Uncertainty quantification is crucial to assess the reliability of the results. We present here an extensive sensitivity analysis in aortic flow simulations, to quantify the dependence of clinically relevant quantities to the patient-specific geometry and the inflow boundary conditions. Geometry and inflow conditions are generally believed to have a major impact on numerical simulations. We resort to a global sensitivity analysis, (i.e., not restricted to a linearization around a working point), based on polynomial chaos expansion (PCE) and the associated Sobol' indices. We regard the geometry and the inflow conditions as the realization of a parametric stochastic process. To construct a physically consistent stochastic process for the geometry, we use a set of longitudinal-in-time images of a patient with an abdominal aortic aneurysm (AAA) to parametrize geometrical variations. Aortic flow is highly disturbed during systole. This leads to high computational costs, even amplified in a sensitivity analysis -when many simulations are needed. To mitigate this, we consider here a large Eddy simulation (LES) model. Our model depends in particular on a user-defined parameter called filter radius. We borrowed the tools of the global sensitivity analysis to assess the sensitivity of the solution to this parameter too. The targeted quantities of interest (QoI) include: the total kinetic energy (TKE), the time-average wall shear stress (TAWSS), and the oscillatory shear index (OSI). The results show that these indexes are mostly sensitive to the geometry. Also, we find that the sensitivity may be different during different instants of the heartbeat and in different regions of the domain of interest. This analysis helps to assess the reliability of in silico tools for clinical applications.

Large eddy simulation of multi-regime burner: a reaction mechanism sensitivity analysis

2022 ◽  
Author(s):  
Lorenzo Angelilli ◽  
Pietro Paolo Ciottoli ◽  
Francisco E. Hernandez Perez ◽  
Mauro Valorani ◽  
Hong G. Im ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Reaction Mechanism ◽  
Large Eddy Simulation ◽  
Eddy Simulation ◽  
Large Eddy

1831 Large-Eddy Simulation of Atmospheric Flow by MSSG-A : Parameter Sensitivity Analysis

2008 ◽  
Vol 2008.2 (0) ◽  
pp. 181-182
Author(s):  
Shingo KANAZAWA ◽  
Takeshi KAMIO ◽  
Takeshi SUGIMURA ◽  
Makoto IIDA ◽  
Keiko TAKAHASHI ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Large Eddy Simulation ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Atmospheric Flow ◽  
Eddy Simulation ◽  
Large Eddy

scholarly journals Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

AIAA Journal ◽  
2018 ◽  
Vol 56 (3) ◽  
pp. 1170-1184 ◽  
Author(s):  
Xun Huan ◽  
Cosmin Safta ◽  
Khachik Sargsyan ◽  
Gianluca Geraci ◽  
Michael S. Eldred ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Global Sensitivity Analysis ◽  
Model Error ◽  
Global Sensitivity
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