Uncertainty Quantification and Model Validation under Epistemic Uncertainty due to Sparse and Imprecise data

2011 ◽  
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan
Keyword(s):  
Uncertainty Quantification ◽  
Model Validation ◽  
Epistemic Uncertainty ◽  
Imprecise Data

Experimental Assessment, Model Validation, and Uncertainty Quantification of a Pilot-Scale Gasifier

2016 ◽  
Vol 55 (25) ◽  
pp. 6961-6970 ◽  
Author(s):  
M. Hossein Sahraei ◽  
Marc A. Duchesne ◽  
Robin W. Hughes ◽  
Luis A. Ricardez-Sandoval
Keyword(s):  
Uncertainty Quantification ◽  
Model Validation ◽  
Pilot Scale ◽  
Assessment Model ◽  
Experimental Assessment

Models, Uncertainty, and the Sandia V&V Challenge Problem

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
George A. Hazelrigg ◽  
Georgia-Ann Klutke
Keyword(s):  
Probability Theory ◽  
Decision Theory ◽  
Uncertainty Quantification ◽  
Model Validation ◽  
Current Literature ◽  
Current Approach ◽  
Ill Posed ◽  
Solution Techniques

Abstract The purpose of this paper is not to present new results; rather, it is to show that the current approach to model validation is not consistent with the accepted mathematics of probability theory. Specifically, we argue that the Sandia V&V Challenge Problem is ill-posed in that the answers sought do not, mathematically, exist. We apply our arguments to show the types of mistakes present in the papers presented in the Journal of Verification, Validation and Uncertainty Quantification, Volume 1,1 along with the challenge problem. Further, we argue that, when the problem is properly posed, both the applicable methodology and the solution techniques are easily drawn from the well-developed mathematics of probability and decision theory. The unfortunate aspect of the challenge problem as currently stated is that it leads to incorrect and inappropriate mathematical approaches that should be avoided and corrected in the current literature.

Uncertainty Quantification of Time-Dependent Reliability Analysis in the Presence of Parametric Uncertainty

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan ◽  
Xiaoping Du
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Reliability Index ◽  
Integration Method ◽  
Epistemic Uncertainty ◽  
Random Variables ◽  
Time Dependent ◽  
Data Uncertainty ◽  
State Function

Limited data of stochastic load processes and system random variables result in uncertainty in the results of time-dependent reliability analysis. An uncertainty quantification (UQ) framework is developed in this paper for time-dependent reliability analysis in the presence of data uncertainty. The Bayesian approach is employed to model the epistemic uncertainty sources in random variables and stochastic processes. A straightforward formulation of UQ in time-dependent reliability analysis results in a double-loop implementation procedure, which is computationally expensive. This paper proposes an efficient method for the UQ of time-dependent reliability analysis by integrating the fast integration method and surrogate model method with time-dependent reliability analysis. A surrogate model is built first for the time-instantaneous conditional reliability index as a function of variables with imprecise parameters. For different realizations of the epistemic uncertainty, the associated time-instantaneous most probable points (MPPs) are then identified using the fast integration method based on the conditional reliability index surrogate without evaluating the original limit-state function. With the obtained time-instantaneous MPPs, uncertainty in the time-dependent reliability analysis is quantified. The effectiveness of the proposed method is demonstrated using a mathematical example and an engineering application example.

A Collaboration-based Approach to CFD Model Validation and Uncertainty Quantification (VUQ) Using Data from a Laminar Helium Plume

2016 ◽  
Vol 97 (2) ◽  
pp. 427-449
Author(s):  
Weston M. Eldredge ◽  
Pál Tóth ◽  
Laurie Centauri ◽  
Eric G. Eddings ◽  
Kerry E. Kelly ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Model Validation ◽  
Cfd Model ◽  
Using Data ◽  
Helium Plume

Uncertainty Quantification of NOx Emission Due to Operating Conditions and Chemical Kinetic Parameters in a Premixed Burner

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Sajjad Yousefian ◽  
Gilles Bourque ◽  
Rory F. D. Monaghan
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Gas Turbine ◽  
Flow Reactor ◽  
Epistemic Uncertainty ◽  
Plug Flow ◽  
Chemical Kinetic ◽  
Operating Conditions ◽  
Nox Emission ◽  
Gas Turbine Combustion

Many sources of uncertainty exist when emissions are modeled for a gas turbine combustion system. They originate from uncertain inputs, boundary conditions, calibration, or lack of sufficient fidelity in a model. In this paper, a nonintrusive polynomial chaos expansion (NIPCE) method is coupled with a chemical reactor network (CRN) model using Python to quantify uncertainties of NOx emission in a premixed burner. The first objective of uncertainty quantification (UQ) in this study is development of a global sensitivity analysis method based on the NIPCE method to capture aleatory uncertainty on NOx emission due to variation of operating conditions. The second objective is uncertainty analysis (UA) of NOx emission due to uncertain Arrhenius parameters in a chemical kinetic mechanism to study epistemic uncertainty in emission modeling. A two-reactor CRN consisting of a perfectly stirred reactor (PSR) and a plug flow reactor (PFR) is constructed in this study using Cantera to model NOx emission in a benchmark premixed burner under gas turbine operating conditions. The results of uncertainty and sensitivity analysis (SA) using NIPCE based on point collocation method (PCM) are then compared with the results of advanced Monte Carlo simulation (MCS). A set of surrogate models is also developed based on the NIPCE approach and compared with the forward model in Cantera to predict NOx emissions. The results show the capability of NIPCE approach for UQ using a limited number of evaluations to develop a UQ-enabled emission prediction tool for gas turbine combustion systems.

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