Parametric Uncertainty Quantification of an Inflatable/Rigidizable Hexapod

2004 ◽  
Author(s):  
Jiann-Shiun Lew ◽  
Lucas Horta
Keyword(s):  
Uncertainty Quantification ◽  
Parametric Uncertainty

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods

2015 ◽  
Vol 24 (3) ◽  
pp. 307 ◽  
Author(s):  
Yaning Liu ◽  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Giray Ökten ◽  
Scott Goodrick
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Monte Carlo Methods ◽  
Wildland Fire ◽  
Parametric Uncertainty ◽  
Monte Carlo Sampling ◽  
Model Parameters ◽  
Quasi Monte Carlo ◽  
Control Variate ◽  
Fuel Model

Rothermel's wildland surface fire model is a popular model used in wildland fire management. The original model has a large number of parameters, making uncertainty quantification challenging. In this paper, we use variance-based global sensitivity analysis to reduce the number of model parameters, and apply randomised quasi-Monte Carlo methods to quantify parametric uncertainties for the reduced model. The Monte Carlo estimator used in these calculations is based on a control variate approach applied to the sensitivity derivative enhanced sampling. The chaparral fuel model, selected from Rothermel's 11 original fuel models, is studied as an example. We obtain numerical results that improve the crude Monte Carlo sampling by factors as high as three orders of magnitude.

scholarly journals Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1

2021 ◽  
Author(s):  
Donghui Xu ◽  
Gautam Bisht ◽  
Khachik Sargsyan ◽  
Chang Liao ◽  
L. Ruby Leung
Keyword(s):  
Uncertainty Quantification ◽  
Water Cycle ◽  
Computational Cost ◽  
Model Performance ◽  
Parametric Uncertainty ◽  
Surrogate Models ◽  
Earth System ◽  
Runoff Generation ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

scholarly journals Separability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Benjamin B. Schroeder ◽  
Humberto Silva ◽  
Kyle D. Smith
Keyword(s):  
Thermal Analysis ◽  
Uncertainty Quantification ◽  
Computational Simulation ◽  
Parametric Uncertainty ◽  
Engineering Systems ◽  
Margin Assessment ◽  
Full System ◽  
Numerical Errors ◽  
Uncertainty Sources ◽  
Current Paradigm

When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.

scholarly journals Inverse and forward uncertainty quantification of models for foam–assisted enhanced oil recovery

2021 ◽  
Author(s):  
◽  
Andrés Ricardo Valdez
Keyword(s):  
Uncertainty Quantification ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Computational Cost ◽  
Parametric Uncertainty ◽  
Posterior Density ◽  
Two Phase ◽  
Output Variability ◽  
Recovery Models ◽  
Laboratory Experiences

Like many other engineering applications, oil recovery and enhanced oil recovery are sensitive to the correct administration of economic resources. Pilot tests and core flood experiments are crucial elements to design an enhanced oil recovery (EOR) project. In this direction, numerical simulators are accessible alternatives for evaluating different engineering configurations at many diverse scales (pore, laboratory, and field scales). Despite the advantages that numerical simulators possess over laboratory experiences, they are not fully protected against uncertainties. In this thesis, we show advances in analyzing uncertainties in two-–phase reservoir simulations, focusing on foam–based EOR. The methods employed in this thesis analyze how experimental uncertainties affect reservoir simulator’s responses. Our framework for model calibration and uncertainty quantification uses the Markov Chain Monte Carlo method. The parametric uncertainty is tested against identifiability studies revealing situations where posterior density distributions with high variability are related to high uncertainties and practical non–identifiability issues. The model’s reliability was evaluated by adopting surrogate models based on polynomial chaos expansion when the computational cost was an issue for the analysis. Once we quantified the model’s output variability, we performed a global sensitivity analysis to map the model’s uncertainty to the input parameters distributions. Main and total Sobol indices were used to investigate the model’s uncertainty and highlight how key parameters and their interactions influence the simulation’s output. As a consequence of the results presented in this thesis, we show a technique for parameter and uncertainty estimation that can be explored to reduce the uncertainty in foam–assisted oil recovery models, which in turn can provide reliable computational simulations. Such conclusions are of utmost interest and relevance for the design of adequate techniques for enhanced oil recovery.

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