Sensitivity analysis using two-dimensional models of the Whiteshell geosphere

  • 41 Pages
  • 0.91 MB
  • English
AECL, Whiteshell Nuclear Research Establishment , Pinawa, Man
Statementby N.W. Scheier, T. Chan and F.W. Stanchell.
SeriesTechnical record (Atomic Energy of Canada Ltd) -- 572
ContributionsChan, Tak-Chee., Stanchell, Frank W., Atomic Energy of Canada Limited.
The Physical Object
Pagination41 p. :
ID Numbers
Open LibraryOL20220582M

• Development of the conceptual model using available site information concerning the geology, hydrogeology and geochemistry of the site, and the associated key processes that affect the geosphere.

This information is used to conceptualise the movement of groundwater through the geosphere and to define the boundaries of conceptual model. The present article is a sequel to an earlier study in this journal (Saltelli et al., ) where two new sensitivity analysis techniques were techniques, the modified Hora and Iman importance measure (HIM *) (Hora and Iman, ; Iman and Hora ; Ishigami and Homma,Ishigami and Homma, 3–5 December ) and the iterated fractional factorial design (IFFD) (Andres Cited by:   GRS has performed a stochastic study on conceptual model uncertainty by selecting randomly a geosphere model out of a set of eight possible models and by combining the selected model with randomly sampled parameter values [4].

Sensitivity study on the transport calculations in the overlying aquifer () At the Mol site the host clay Cited by: 6. linear-programming system provides this elementary sensitivity analysis, since the calculations are easy to perform using the tableau associated with an optimal solution.

There are two variations in the data that invariably are reported: objective function and File Size: 2MB. The inverse problem of a two‐dimensional model for colloid transport in geochemically heterogeneous porous media is systematically investigated in this paper.

Description Sensitivity analysis using two-dimensional models of the Whiteshell geosphere PDF

Sensitivity analysis prior to the parameter identification provided valuable insights into the identifiability of the six model by: Tutorial: 2D Sensitivity Analysis, for 4 Modeling the managerial sensitivity analysis question • As is the case with all managerial models, one needs to transform the managerial situation into the mathematical model.

DIMENSIONAL ANALYSIS AND MODELING I n this chapter, we first review the concepts of dimensions and then review the fundamental principle of dimensional homogeneity, and show how it is applied to equations in order to nondimensionalize them and to identify dimensionless discuss the concept of similarity between a model and a also describe a powerful tool for engi.

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants.

The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling.

In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches. Sensitivity analysis is the study of how uncertainty in model predictions is Sensitivity analysis using two-dimensional models of the Whiteshell geosphere book by uncertainty in model inputs.

A global sensitivity analysis considers the potential effects from the simultaneous variation of model inputs over their finite range of uncertainty. A number of techniques are available to carry out global sensitivity. Igor V. Florinsky received the degree in reprography, Ph.D.

degree in remote sensing and photogrammetry, and degree in cartography from the Moscow State University of Geodesy and. This should be based more on skin type and sensitivity to exposure than on a specific tanning bed model. 'Sensitivity analysis using two-dimensional models of the Whiteshell geosphere'.

Input parameters and model responses in subsurface modeling may have characteristics that require adaptation of existing sensitivity analysis (SA) techniques.

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Screening techniques rely on simplified approaches to identify non‐influential inputs of a computer model, while keeping the number of model evaluations small. A sensitivity analysis and simulation of a Dengue model with, X = (x 1;x 2;y 1;y 2;z)T and X 0 is the initial population vector [10, 11].

Therefore, = 0 B B @ y 2(t) x 1(t)+x 2(t) x 1(t) ˙ x 2(t) x 1(t)+x 2(t) y 1(t) f˚(y 1(t) + y 2(t)) 1 z(t) k 1 C C A and V = 0 @ (+)x 2 t y 2(t) (ˇ+!)z(t) 1 A.

Shape Design Sensitivity Analysis of Nonlinear Structures 5 symEzz z F(;),=∇⋅() 0 T (3) where 1 2 sym AAA=+T represents the symmetric part of a tensor, Fx=∇ 0 is the deformation gradient, and ∇=∂∂ 0 / X is the gradient operator in the initial domain.

Note that Ez(;)i is linear with respect to its argument, while Sz() is generally nonlinear. Shape sensitivity analysis for dimensionally-heterogeneous models P.J.

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Blanco 1, 2 and R.A. F eij´ oo 1, 2 1 LNCC, Laborat´ orio Nacional de Computa¸ c˜ ao Cient ´ ıfica, Petr´ opolis. analysis of systems biology models [3]. First, sensitivity analysis can provide valuable insights about how robust the model outputs are with respect to the changes of model inputs and which model inputs are the key factors that affect the model output.

In addition, sensitivity analysis is. Shape Sensitivity Analysis and Optimization using Isogeometric Boundary Element Methods in Two-dimensional Linear Elasticity Haojie Lian1, Robert N. Simpson2, Ste´phane P.A. Bordas1,3,∗, Pierre Kerfriden1 1School of Engineering, Cardiff University, Queen’s Buildings, The parade, Cardiff, UK 2School of Engineering, Glasgow University, Rankine Building, Glasgow, UK.

model. Keywords: Uncertainty analysis; Sensitivity analysis; Spatial models, Nitrate transport 1. Introduction Models of varying complexity are developed to describe, at a given degree of approximation, systems and processes in different aspects of the real world (e.g.

industrial, environmental, social, or economic). The use of models, in. outputs by Shi et al. () and Bayarri et al. () and for two-dimensional outputs by Higdon et al. In the case of sensitivity analysis, a functional output is usually considered as a vecto-rial output and sensitivity indices relative to each input are computed for each discretized value of the output (De Rocquigny et al., ).

The fidelity of numerical simulations of transport phenomena is often compromised by the difficulty in modeling the inherent experimental uncertainties. In this study, we examine the sensitivity of direct numerical simulations of two-dimensional spatially developing plane mixing layers to uncertainties in the inflow boundary conditions.

In particular, we treat the magnitudes of discrete. Sensitivity Analysis is a type of analysis that shoes how a particular scenario may be affected by multiple variables.

For example, one could model a home mortgage and run a sensitivity on what. Performing sensitivity analysis for influence diagrams using the decision circuit frame-work is particularly convenient, since the partial derivatives with respect to every pa-rameter are readily available [Bhattacharjya and Shachter, ; ].


28 Introduction. 28 Test Case: Regional Simulation Model for Water Conservation Area-2A. Bohling, G.C.,A radial-coordinate groundwater flow model for analysis of well tests in heterogeneous formations, in Proceedings of the Fifth Annual Conference of the IAMG, August, Trondheim, Norway, p.

Coseismic ground displacements detected through remote sensing surveys are often used to invert the coseismic slip distribution on geologically reliable fault planes. We analyze a well-known case study ( L’Aquila earthquake) to investigate how three-dimensional (3D) slip configuration affects coseismic ground surface deformation.

Different coseismic slip surface configurations. From the sensitivity analysis point of view, the domain for the shape sensitivity analysis is discretized in the early stage of geometric definition so that the normal vector and curvature can be calculated exactly.

Also, shape sensitivity expressions include the shape dependences of higher order geometric information as well as exact responses.

I am computing terrain wetness index (TWI) for a study area using various grid sizes (, 1,2 and ). Which tool or tools in Arcgis can I use to assess the sensitivity of. 1 Sensitivity analysis of spatial models using geostatistical simulation Nathalie SAINT-GEOURS 1, Christian LAVERGNE 2, Jean-Stéphane BAILLY 3 & Frédéric GRELOT 4 1 AgroParisTech, UMR TETIS, I3M, France, [email protected] 2 I3M, Université Montpellier III, France, [email protected] 3 AgroParisTech, UMR TETIS, France, [email protected] Then, the sensitivity analysis can be performed on each meta-model to determine the contribution of each critical parameter (given its range of variation) to the variation of the resulting properties.

This analysis is done using a method of variance decomposition based on the computation of Sobol’s indices. They measure the part of the. Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results.

This type of analysis has proven to be highly effective for evaluating the geographic suitability of certain locations for specific purposes, estimating and predicting outcomes. Eg To assess the potential for a landslide one can use a deductive model based on laws in physics or use an inductive model based on recorded data from past landslides Spatial Analysis and Modelling by Tadele Feyssa, Wollega University The development of a model .Model parsimony and the identification of conceptual models can be assessed using identification criteria, as demonstrated by Carrera and Neuman () in identifying flow models and by Samper and Neuman () in inferring covariance functions for groundwater-quality data.

These identification criteria are based on information theory and were.