scholarly journals Orbit uncertainty propagation and sensitivity analysis with separated representations

2017 ◽  
Vol 129 (1-2) ◽  
pp. 105-136 ◽  
Author(s):  
Marc Balducci ◽  
Brandon Jones ◽  
Alireza Doostan
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Propagation ◽  
Separated Representations ◽  
Orbit Uncertainty

scholarly journals Multielement Separated Representations for Orbit Uncertainty Propagation

2019 ◽  
Vol 42 (9) ◽  
pp. 1930-1945
Author(s):  
Marc Balducci ◽  
Brandon A. Jones ◽  
Rodney L. Anderson
Keyword(s):  
Uncertainty Propagation ◽  
Separated Representations ◽  
Orbit Uncertainty

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

scholarly journals UNCERTAINTY PROPAGATION AND SENSITIVITY ANALYSIS IN RAY-TRACING SIMULATIONS

2011 ◽  
Vol 21 ◽  
pp. 149-161 ◽  
Author(s):  
Antoine Haarscher ◽  
P. De Doncker ◽  
David Lautru
Keyword(s):  
Sensitivity Analysis ◽  
Ray Tracing ◽  
Uncertainty Propagation

Multi-fidelity orbit uncertainty propagation

2019 ◽  
Vol 155 ◽  
pp. 406-417 ◽  
Author(s):  
Brandon A. Jones ◽  
Ryan Weisman
Keyword(s):  
Uncertainty Propagation ◽  
Orbit Uncertainty

Optimization of a Centrifugal Compressor Impeller Design for Robustness to Manufacturing Uncertainties

2013 ◽  
Author(s):  
A. Javed ◽  
R. Pecnik ◽  
J. P. van Buijtenen
Keyword(s):  
Sensitivity Analysis ◽  
Surrogate Model ◽  
Uncertainty Propagation ◽  
Computational Cost ◽  
Dimensional Accuracy ◽  
Robust Design Optimization ◽  
Centrifugal Impeller ◽  
Conceptual Design Phase ◽  
Compressor Impeller ◽  
High Production Cost

Compressor impellers for mass-market turbochargers are die-casted and machined with an aim to achieve high dimensional accuracy and acquire specific performance. However, manufacturing uncertainties result in dimensional deviations causing incompatible operational performance and assembly errors. Process capability limitations of the manufacturer can cause an increase in part rejections, resulting in high production cost. This paper presents a study on a centrifugal impeller with focus on the conceptual design phase to obtain a turbomachine that is robust to manufacturing uncertainties. The impeller has been parameterized and evaluated using a commercial computational fluid dynamics (CFD) solver. Considering the computational cost of CFD, a surrogate model has been prepared for the impeller by response surface methodology (RSM) using space-filling Latin hypercube designs. A sensitivity analysis has been performed initially to identify the critical geometric parameters which influence the performance mainly. Sensitivity analysis is followed by the uncertainty propagation and quantification using the surrogate model based Monte Carlo simulation. Finally a robust design optimization has been carried out using a stochastic optimization algorithm leading to a robust impeller design for which the performance is relatively insensitive to variability in geometry without reducing the sources of inherent variation i.e. the manufacturing noise.

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