scholarly journals Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO$_2$ from Satellite Data

2017 ◽  
Vol 5 (1) ◽  
pp. 956-985 ◽  
Author(s):  
Jonathan Hobbs ◽  
Amy Braverman ◽  
Noel Cressie ◽  
Robert Granat ◽  
Michael Gunson
Keyword(s):  
Uncertainty Quantification ◽  
Satellite Data ◽  
Simulation Based

scholarly journals Uncertainty Quantification of Leakages in a Multistage Simulation and Comparison With Experiments

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Cosimo Maria Mazzoni ◽  
Richard Ahlfeld ◽  
Budimir Rosic ◽  
Francesco Montomoli
Keyword(s):  
Uncertainty Quantification ◽  
Numerical Study ◽  
Turbine Blades ◽  
Industrial Practice ◽  
Turbine Efficiency ◽  
Simulation Based ◽  
Multistage Turbine ◽  
Computational Fluid Dynamics Cfd ◽  
Yaw Angle ◽  
The Impact

This paper presents a numerical study of the impact of tip gap uncertainties in a multistage turbine. It is well known that the rotor gap can change the gas turbine efficiency, but the impact of the random variation of the clearance height has not been investigated before. In this paper, the radial seals clearance of a datum shroud geometry, representative of steam turbine industrial practice, was systematically varied and numerically tested by means of unsteady computational fluid dynamics (CFD). By using a nonintrusive uncertainty quantification (UQ) simulation based on a sparse arbitrary moment-based approach, it is possible to predict the radial distribution of uncertainty in stagnation pressure and yaw angle at the exit of the turbine blades. This work shows that the impact of gap uncertainties propagates radially from the tip toward the hub of the turbine, and the complete span is affected by a variation of the rotor tip gap. This amplification of the uncertainty is mainly due to the low-aspect ratio of the turbine, and a similar behavior is expected in high pressure (HP) turbines.

A dimension-reduction metamodeling approach to simulation-based uncertainty quantification problems with high dimensionalities

2021 ◽  
pp. 095440622199118
Author(s):  
YP Ju
Keyword(s):  
Dimension Reduction ◽  
Uncertainty Quantification ◽  
Computational Cost ◽  
Dynamics Simulation ◽  
High Dimensional ◽  
Support Vector ◽  
Compressor Cascade ◽  
Support Vector Regression Model ◽  
Simulation Based ◽  
Spatially Varying

A common strategy to handle simulation-based uncertainty quantification problems is adopting a metamodel to replace time-demanding calculations such as computational fluid dynamics simulation or finite element analysis within Monte Carlo simulation process. However, most of the so far metamodel-assisted uncertainty quantification methods suffer from the ‘curse of dimensionality.’ The required number of evaluations, which determines the computational cost, increases exponentially as the dimensionality of the input uncertainty increases, resulting in unaffordable computational cost for high-dimensional problems. Another challenge emerges when the output uncertainties are a spatially varying field accommodating a huge number of spatial nodes. To solve these issues, here we propose a dimension-reduction metamodeling approach, in which active subspace method is utilized to reduce the input dimensionality and proper orthogonal decomposition method is utilized to reduce the output dimensionality of the spatially varying field. The relationship between the two methods is established by using the support vector regression model. Through uncertainty quantification of seven stochastic analytical functions and one stochastic convection-diffusion equation, the proposed approach was verified to be fairly accurate in propagating high-dimensional input uncertainties to either a scalar value or a spatially varying output. The accuracy and efficiency of the proposed approach in dealing with even more practical simulation-based problems were then validated by uncertainty quantification of a compressor cascade with stochastic protrusions/dents distributed on the blade surface. This work provides an effective and versatile approach for simulation-based high-dimensional uncertainty quantification problems.

Simulation-Based Uncertainty Quantification for Additively Manufactured Cellular Structures

2015 ◽  
Vol 44 (10) ◽  
pp. 4035-4041 ◽  
Author(s):  
Seung-Kyum Choi ◽  
Recep M. Gorguluarslan ◽  
Sang-In Park ◽  
Thomas Stone ◽  
Jack K. Moon ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Cellular Structures ◽  
Simulation Based
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