A Review of Interval Field Approaches for Uncertainty Quantification in Numerical Models

2021 ◽  
pp. 95-110
Author(s):  
Matthias Faes ◽  
Maurice Imholz ◽  
Dirk Vandepitte ◽  
David Moens
Keyword(s):  
Uncertainty Quantification ◽  
Numerical Models ◽  
Field Approaches

Uncertainty quantification and global sensitivity analysis with dependent inputs: Application to the 2D hydraulic model of the Loire River

2020 ◽  
Author(s):  
Lucie Pheulpin ◽  
Vito Bacchi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Large Scale ◽  
Global Sensitivity Analysis ◽  
Numerical Models ◽  
Hydraulic Model ◽  
Model Parameters ◽  
Traditional Methods ◽  
Global Sensitivity ◽  
Loire River

<p>Hydraulic models are increasingly used to assess the flooding hazard. However, all numerical models are affected by uncertainties, related to model parameters, which can be quantified through Uncertainty Quantification (UQ) and Global Sensitivity Analysis (GSA). In traditional methods of UQ and GSA, the input parameters of the numerical models are considered to be independent which is actually rarely the case. The objective of this work is to proceed with UQ and GSA methods considering dependent inputs and comparing different methodologies. At our knowledge, there is no such application in the field of 2D hydraulic modelling.</p><p>At first the uncertain parameters of the hydraulic model are classified in groups of dependent parameters. Within this aim, it is then necessary to define the copulas that better represent these groups. Finally UQ and GSA based on copulas are performed. The proposed methodology is applied to the large scale 2D hydraulic model of the Loire River. However, as the model computation is high time-consuming, we used a meta-model instead of the initial model. We compared the results coming from the traditional methods of UQ and GSA (<em>i.e.</em> without taking into account the dependencies between inputs) and the ones coming from the new methods based on copulas. The results show that the dependence between inputs should not always be neglected in UQ and GSA.</p>

scholarly journals Blending measurements and numerical models: a novel methodological approach for land subsidence prediction with uncertainty quantification

2020 ◽  
Vol 382 ◽  
pp. 457-462
Author(s):  
Laura Gazzola ◽  
Massimiliano Ferronato ◽  
Matteo Frigo ◽  
Pietro Teatini ◽  
Claudia Zoccarato ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Land Subsidence ◽  
Ad Hoc ◽  
Numerical Models ◽  
Methodological Approach ◽  
Hydrocarbon Reservoirs ◽  
Subsidence Prediction ◽  
New Information ◽  
Deep Rock ◽  
Synthetic Models

Abstract. The use of numerical models for land subsidence prediction above producing hydrocarbon reservoirs has become a common and well-established practice since the early '90s. Usually, uncertainties in the deep rock behavior, which can affect the forecast capability of the models, have been taken into account by running multiple simulations with different constitutive laws and mechanical properties. Then, the most uncertain parameters were calibrated to reproduce available subsidence measurements. The objective of this work is to propose a novel methodological approach for land subsidence prediction and uncertainty quantification by integrating the available monitoring information in numerical models using ad hoc Data Assimilation techniques. The proposed approach allows to: (i) train the model with the available data and improve its accuracy as new information comes in, (ii) quantify the prediction uncertainty by providing confidence intervals and probability measures instead of deterministic outcomes, and (iii) identify the most appropriate rock constitutive model and geomechanical parameters. The methodology is tested in synthetic models of production from hydrocarbon reservoirs. The numerical experiments show that the proposed approach is a promising way to improve the effectiveness and reliability of land subsidence models.

Polynomial chaos assessment of design tolerances for vortex-induced vibrations of two cylinders in tandem

2017 ◽  
Vol 31 (2) ◽  
pp. 185-198 ◽  
Author(s):  
Gianluca Geraci ◽  
Marco Donato De Tullio ◽  
Gianluca Iaccarino
Keyword(s):  
Uncertainty Quantification ◽  
Polynomial Chaos ◽  
Numerical Models ◽  
Numerical Procedure ◽  
Navier Stokes ◽  
Immersed Boundary ◽  
Physical Interaction ◽  
Vortex Induced Vibrations ◽  
Quantification Analysis ◽  
The Difference

AbstractThe presence of aerodynamics loadings makes the design of some classes of elastic structures, as, for instance, marine structures and risers, very challenging. Moreover, capturing the complex physical interaction between the structure and the fluid is challenging for both theoretical and numerical models. One of the most important phenomena that appear in these situations is vortex-induced vibrations. The picture is even more complicated when multiple elastic elements are close enough to interact by modifying the fluid flow pattern. In the present work, we show how the common design practice for these structures, which is entirely based on deterministic simulations, needs to be complemented by the uncertainty quantification analysis. The model problem is a structure constituted by two elastically mounted cylinders exposed to a two-dimensional uniform flow at Reynolds number 200. The presence of a manufacturing tolerance in the relative position of the two cylinders, which we consider to be a source of uncertainty, is addressed. The overall numerical procedure is based on a Navier–Stokes immersed boundary solver that uses a flexible moving least squares approach to compute the aerodynamics loadings on the structure, whereas the uncertainty quantification propagation is obtained by means of a nonintrusive polynomial chaos technique. A range of reduced velocities is considered, and the quantification, in a probabilistic sense, of the difference in the performances of this structure with respect to the case of an isolated cylinder is provided. The numerical investigation is also complemented by a global sensitivity analysis based on the analysis of variance.

scholarly journals Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3685 ◽  
Author(s):  
Anna Kalinina ◽  
Matteo Spada ◽  
David F. Vetsch ◽  
Stefano Marelli ◽  
Calvin Whealton ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Numerical Models ◽  
Wave Model ◽  
Dam Break ◽  
Standard Methods ◽  
Computational Costs ◽  
Dam Breaks ◽  
Reservoir Volume ◽  
Sensitivity Indices ◽  
Flood Model

Uncertainties in instantaneous dam-break floods are difficult to assess with standard methods (e.g., Monte Carlo simulation) because of the lack of historical observations and high computational costs of the numerical models. In this study, polynomial chaos expansion (PCE) was applied to a dam-break flood model reflecting the population of large concrete dams in Switzerland. The flood model was approximated with a metamodel and uncertainty in the inputs was propagated to the flow quantities downstream of the dam. The study demonstrates that the application of metamodeling for uncertainty quantification in dam-break studies allows for reduced computational costs compared to standard methods. Finally, Sobol’ sensitivity indices indicate that reservoir volume, length of the valley, and surface roughness contributed most to the variability of the outputs. The proposed methodology, when applied to similar studies in flood risk assessment, allows for more generalized risk quantification than conventional approaches.

Data assimilation, sensitivity analysis and uncertainty quantification in a semi-arid terminal catchment subject to rainfall decline

2021 ◽  
Author(s):  
Eduardo De Sousa ◽  
Matthew Hipsey ◽  
Ryan Vogwill
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Uncertainty Quantification ◽  
Numerical Models ◽  
Point Of View ◽  
Small Scale ◽  
Ramsar Convention ◽  
Ungauged Catchments ◽  
Groundwater Levels ◽  
Hydrologic Change

<p>Quantification of long-term hydrologic change in groundwater often requires the comparison of states pre- and post- change. The assessment of these changes in ungauged catchments is particularly difficult from a conceptual point of view and due to parameter non-uniqueness and associated uncertainty of quantitative frameworks. In these contexts, the use of data assimilation, sensitivity analysis and uncertainty quantification techniques are critical to maximise the use of available data both in terms of conceptualisation and quantification. This paper summarises findings of a study undertaken in the Lake Muir-Unicup Natural Diversity Recovery Catchment (MUNDRC), where a number of techniques were applied to inform both conceptual and numerical models. The MUNDRC is and small-scale endorheic basin located in southwestern Australia listed under the Ramsar Convention as a Wetland of International Importance and have been subject to a systematic decline in rainfall rates since 1970. Conceptual and numerical frameworks have been development to understand and quantify impacts of rainfall decline on the catchment using a variety of metrics involving groundwater and lake levels, as well as fluxes between these compartments and mass balance components. Conceptualisation was facilitated with the use a novel data-driven method relating rainfall and groundwater response running backwards in time, allowing the establishment of baseline conditions prior to rainfall decline, estimation of net recharge rates and providing initial heads for the forward numerical modelling. Parameter and predictive uncertainties associated with data gaps have been minimised and quantified utilising an Iterative Ensemble Smoother (White, 2018), while further refinement of conceptual model was undertaken following results from sensitivity analysis, where major parameter controls groundwater levels and other predictions of interest were quantified.</p>

Stochastic simulation of the spatio-temporal field of the average daily heat index in Southern Russia

2020 ◽  
Vol 82 ◽  
pp. 149-160
Author(s):  
N Kargapolova
Keyword(s):  
Heat Waves ◽  
Numerical Models ◽  
Heat Index ◽  
Real Field ◽  
Gaussian Field ◽  
The Real ◽  
Southern Russia ◽  
Weather Stations ◽  
Spatio Temporal ◽  
Temporal Field

Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.

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