Practical postcalibration uncertainty analysis: Yucca Mountain, Nevada.
James, Scott C; Doherty, John E; Eddebbarh, Al-Aziz
2009-01-01
The values of parameters in a groundwater flow model govern the precision of predictions of future system behavior. Predictive precision, thus, typically depends on an ability to infer values of system properties from historical measurements through calibration. When such data are scarce, or when their information content with respect to parameters that are most relevant to predictions of interest is weak, predictive uncertainty may be high, even if the model is "calibrated." Recent advances help recognize this condition, quantitatively evaluate predictive uncertainty, and suggest a path toward improved predictive accuracy by identifying sources of predictive uncertainty and by determining what observations will most effectively reduce this uncertainty. We demonstrate linear and nonlinear predictive error/uncertainty analyses as applied to a groundwater flow model of Yucca Mountain, Nevada, the United States' proposed site for disposal of high-level radioactive waste. Linear and nonlinear uncertainty analyses are readily implemented as an adjunct to model calibration with medium to high parameterization density. Linear analysis yields contributions made by each parameter to a prediction's uncertainty and the worth of different observations, both existing and yet-to-be-gathered, toward reducing this uncertainty. Nonlinear analysis provides more accurate characterization of the uncertainty of model predictions while yielding their (approximate) probability distribution functions. This article applies the above methods to a prediction of specific discharge and confirms the uncertainty bounds on specific discharge supplied in the Yucca Mountain Project License Application. PMID:19744249
Practical post-calibration uncertainty analysis: Yucca Mountain, Nevada, USA
NASA Astrophysics Data System (ADS)
James, S. C.; Doherty, J.; Eddebbarh, A.
2009-12-01
The values of parameters in a groundwater flow model govern the precision of predictions of future system behavior. Predictive precision, thus, typically depends on an ability to infer values of system properties from historical measurements through calibration. When such data are scarce, or when their information content with respect to parameters that are most relevant to predictions of interest is weak, predictive uncertainty may be high, even if the model is “calibrated.” Recent advances help recognize this condition, quantitatively evaluate predictive uncertainty, and suggest a path toward improved predictive accuracy by identifying sources of predictive uncertainty and by determining what observations will most effectively reduce this uncertainty. We demonstrate linear and nonlinear predictive error/uncertainty analyses as applied to a groundwater flow model of Yucca Mountain, Nevada, the US’s proposed site for disposal of high-level radioactive waste. Both of these types uncertainty analysis are readily implemented as an adjunct to model calibration with medium to high parameterization density. Linear analysis yields contributions made by each parameter to a prediction’s uncertainty and the worth of different observations, both existing and yet-to-be-gathered, toward reducing this uncertainty. Nonlinear analysis provides more accurate characterization of the uncertainty of model predictions while yielding their (approximate) probability distribution functions. This paper applies the above methods to a prediction of specific discharge and confirms the uncertainty bounds on specific discharge supplied in the Yucca Mountain Project License Application. Furthermore, Monte Carlo simulations confirm that hydrogeologic units thought to be flow barriers have probability distributions skewed toward lower permeabilities.
Thomas, R.E.
1982-03-01
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software.
MOUSE UNCERTAINTY ANALYSIS SYSTEM
The original MOUSE (Modular Oriented Uncertainty System) system was designed to deal with the problem of uncertainties in Environmental engineering calculations, such as a set of engineering cost or risk analysis equations. t was especially intended for use by individuals with li...
Antarctic Photochemistry: Uncertainty Analysis
NASA Technical Reports Server (NTRS)
Stewart, Richard W.; McConnell, Joseph R.
1999-01-01
Understanding the photochemistry of the Antarctic region is important for several reasons. Analysis of ice cores provides historical information on several species such as hydrogen peroxide and sulfur-bearing compounds. The former can potentially provide information on the history of oxidants in the troposphere and the latter may shed light on DMS-climate relationships. Extracting such information requires that we be able to model the photochemistry of the Antarctic troposphere and relate atmospheric concentrations to deposition rates and sequestration in the polar ice. This paper deals with one aspect of the uncertainty inherent in photochemical models of the high latitude troposphere: that arising from imprecision in the kinetic data used in the calculations. Such uncertainties in Antarctic models tend to be larger than those in models of mid to low latitude clean air. One reason is the lower temperatures which result in increased imprecision in kinetic data, assumed to be best characterized at 298K. Another is the inclusion of a DMS oxidation scheme in the present model. Many of the rates in this scheme are less precisely known than are rates in the standard chemistry used in many stratospheric and tropospheric models.
Analysis of Infiltration Uncertainty
R. McCurley
2003-10-27
The primary objectives of this uncertainty analysis are: (1) to develop and justify a set of uncertain parameters along with associated distributions; and (2) to use the developed uncertain parameter distributions and the results from selected analog site calculations done in ''Simulation of Net Infiltration for Modern and Potential Future Climates'' (USGS 2001 [160355]) to obtain the net infiltration weighting factors for the glacial transition climate. These weighting factors are applied to unsaturated zone (UZ) flow fields in Total System Performance Assessment (TSPA), as outlined in the ''Total System Performance Assessment-License Application Methods and Approach'' (BSC 2002 [160146], Section 3.1) as a method for the treatment of uncertainty. This report is a scientific analysis because no new and mathematical physical models are developed herein, and it is based on the use of the models developed in or for ''Simulation of Net Infiltration for Modern and Potential Future Climates'' (USGS 2001 [160355]). Any use of the term model refers to those developed in the infiltration numerical model report. TSPA License Application (LA) has included three distinct climate regimes in the comprehensive repository performance analysis for Yucca Mountain: present-day, monsoon, and glacial transition. Each climate regime was characterized using three infiltration-rate maps, including a lower- and upper-bound and a mean value (equal to the average of the two boundary values). For each of these maps, which were obtained based on analog site climate data, a spatially averaged value was also calculated by the USGS. For a more detailed discussion of these infiltration-rate maps, see ''Simulation of Net Infiltration for Modern and Potential Future Climates'' (USGS 2001 [160355]). For this Scientific Analysis Report, spatially averaged values were calculated for the lower-bound, mean, and upper-bound climate analogs only for the glacial transition climate regime, within the
NASA Astrophysics Data System (ADS)
Thyer, Mark; Renard, Benjamin; Kavetski, Dmitri; Kuczera, George; Franks, Stewart William; Srikanthan, Sri
2009-12-01
The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall-runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer, and evaluate probability models describing input, output, and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) to address two key requirements of uncertainty assessment: (1) reliable quantification of predictive uncertainty and (2) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantile-quantile analysis, suggest that while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly dependent on the selected rain gauge and calibration period. This will obscure potential relationships between CRR parameters and catchment attributes and prevent the development of meaningful regional relationships. Conversely, BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization. However, significant departures from the calibration assumptions remained even in BATEA, e.g., systematic overestimation of predictive uncertainty, especially in validation. This is likely due to the inferred rainfall errors compensating for simplified treatment of model structural error.
Uncertainties in offsite consequence analysis
Young, M.L.; Harper, F.T.; Lui, C.H.
1996-03-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequences from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the U.S. Nuclear Regulatory Commission and the European Commission began co-sponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables using a formal expert judgment elicitation and evaluation process. This paper focuses on the methods used in and results of this on-going joint effort.
Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.
2010-01-01
Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in
Uncertainty Analysis of Model Coupling
NASA Astrophysics Data System (ADS)
Held, H.; Knopf, B.; Schneider von Deimling, T.; Schellnhuber, H.-J.
The Earth System is a highly complex system that is often modelled by coupling sev- eral nonlinear submodules. For predicting the climate with these models, the following uncertainties play an essential role: parameter uncertainty, uncertainty in initial con- ditions or model uncertainty. Here we will address uncertainty in initial conditions as well as model uncertainty. As the process of coupling is an important part of model- ing, the main aspect of this work is the investigation of uncertainties that are due to the coupling process. For this study we use conceptual models that, compared to GCMs, have the advantage that the model itself as well as the output can be treated in a mathematically elabo- rated way. As the time for running the model is much shorter, the investigation is also possible for a longer period, e.g. for paleo runs. In consideration of these facts it is feasible to analyse the whole phase space of the model. The process of coupling is investigated by using different methods of examining low order coupled atmosphere-ocean systems. In the dynamical approach a fully coupled system of the two submodules can be compared to a system where one submodule forces the other. For a particular atmosphere-ocean system, based on the Lorenz model for the atmosphere, there can be shown significant differences in the predictability of a forced system depending whether the subsystems are coupled in a linear or a non- linear way. In [1] it is shown that in the linear case the forcing cannot represent the coupling, but in the nonlinear case, that we investigated in our study, the variability and the statistics of the coupled system can be reproduced by the forcing. Another approach to analyse the coupling is to carry out a bifurcation analysis. Here the bifurcation diagram of a single atmosphere system is compared to that of a cou- pled atmosphere-ocean system. Again it can be seen from the different behaviour of the coupled and the uncoupled system, that the
ENHANCED UNCERTAINTY ANALYSIS FOR SRS COMPOSITE ANALYSIS
Smith, F.; Phifer, M.
2011-06-30
The Composite Analysis (CA) performed for the Savannah River Site (SRS) in 2009 (SRS CA 2009) included a simplified uncertainty analysis. The uncertainty analysis in the CA (Smith et al. 2009b) was limited to considering at most five sources in a separate uncertainty calculation performed for each POA. To perform the uncertainty calculations in a reasonable amount of time, the analysis was limited to using 400 realizations, 2,000 years of simulated transport time, and the time steps used for the uncertainty analysis were increased from what was used in the CA base case analysis. As part of the CA maintenance plan, the Savannah River National Laboratory (SRNL) committed to improving the CA uncertainty/sensitivity analysis. The previous uncertainty analysis was constrained by the standard GoldSim licensing which limits the user to running at most four Monte Carlo uncertainty calculations (also called realizations) simultaneously. Some of the limitations on the number of realizations that could be practically run and the simulation time steps were removed by building a cluster of three HP Proliant windows servers with a total of 36 64-bit processors and by licensing the GoldSim DP-Plus distributed processing software. This allowed running as many as 35 realizations simultaneously (one processor is reserved as a master process that controls running the realizations). These enhancements to SRNL computing capabilities made uncertainty analysis: using 1000 realizations, using the time steps employed in the base case CA calculations, with more sources, and simulating radionuclide transport for 10,000 years feasible. In addition, an importance screening analysis was performed to identify the class of stochastic variables that have the most significant impact on model uncertainty. This analysis ran the uncertainty model separately testing the response to variations in the following five sets of model parameters: (a) K{sub d} values (72 parameters for the 36 CA elements in
Risk Analysis and Uncertainty: Implications for Counselling
ERIC Educational Resources Information Center
Hassenzahl, David
2004-01-01
Over the past two decades, the risk analysis community has made substantial advances in understanding and describing uncertainty. Uncertainty is ubiquitous, complex, both quantitative and qualitative in nature, and often irreducible. Uncertainty thus creates a challenge when using risk analysis to evaluate the rationality of group and individual…
Sensitivity and Uncertainty Analysis Shell
Energy Science and Technology Software Center (ESTSC)
1999-04-20
SUNS (Sensitivity and Uncertainty Analysis Shell) is a 32-bit application that runs under Windows 95/98 and Windows NT. It is designed to aid in statistical analyses for a broad range of applications. The class of problems for which SUNS is suitable is generally defined by two requirements: 1. A computer code is developed or acquired that models some processes for which input is uncertain and the user is interested in statistical analysis of the outputmore » of that code. 2. The statistical analysis of interest can be accomplished using the Monte Carlo analysis. The implementation then requires that the user identify which input to the process model is to be manipulated for statistical analysis. With this information, the changes required to loosely couple SUNS with the process model can be completed. SUNS is then used to generate the required statistical sample and the user-supplied process model analyses the sample. The SUNS post processor displays statistical results from any existing file that contains sampled input and output values.« less
Uncertainty analysis for Ulysses safety evaluation report
NASA Technical Reports Server (NTRS)
Frank, Michael V.
1991-01-01
As part of the effort to review the Ulysses Final Safety Analysis Report and to understand the risk of plutonium release from the Ulysses spacecraft General Purpose Heat Source-Radioisotope Thermal Generator, the Interagency Nuclear Safety Review Panel (INSRP) performed an integrated, quantitative analysis of the uncertainties of the calculated risk of plutonium release from Ulysses. Using state-of-art probabilistic risk assessment technology, the uncertainty analysis accounted for both variability and uncertainty of the key parameters of the risk analysis. The results show that INSRP had high confidence that risk of fatal cancers from potential plutonium release associated with calculated launch and deployment accident scenarios is low.
Uncertainty quantification and error analysis
Higdon, Dave M; Anderson, Mark C; Habib, Salman; Klein, Richard; Berliner, Mark; Covey, Curt; Ghattas, Omar; Graziani, Carlo; Seager, Mark; Sefcik, Joseph; Stark, Philip
2010-01-01
UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.
Uncertainty Analysis of Instrument Calibration and Application
NASA Technical Reports Server (NTRS)
Tripp, John S.; Tcheng, Ping
1999-01-01
Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.
Numerical Uncertainty Quantification for Radiation Analysis Tools
NASA Technical Reports Server (NTRS)
Anderson, Brooke; Blattnig, Steve; Clowdsley, Martha
2007-01-01
Recently a new emphasis has been placed on engineering applications of space radiation analyses and thus a systematic effort of Verification, Validation and Uncertainty Quantification (VV&UQ) of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. There are two sources of uncertainty in geometric discretization addressed in this paper that need to be quantified in order to understand the total uncertainty in estimating space radiation exposures. One source of uncertainty is in ray tracing, as the number of rays increase the associated uncertainty decreases, but the computational expense increases. Thus, a cost benefit analysis optimizing computational time versus uncertainty is needed and is addressed in this paper. The second source of uncertainty results from the interpolation over the dose vs. depth curves that is needed to determine the radiation exposure. The question, then, is what is the number of thicknesses that is needed to get an accurate result. So convergence testing is performed to quantify the uncertainty associated with interpolating over different shield thickness spatial grids.
Uncertainty Analysis for Photovoltaic Degradation Rates (Poster)
Jordan, D.; Kurtz, S.; Hansen, C.
2014-04-01
Dependable and predictable energy production is the key to the long-term success of the PV industry. PV systems show over the lifetime of their exposure a gradual decline that depends on many different factors such as module technology, module type, mounting configuration, climate etc. When degradation rates are determined from continuous data the statistical uncertainty is easily calculated from the regression coefficients. However, total uncertainty that includes measurement uncertainty and instrumentation drift is far more difficult to determine. A Monte Carlo simulation approach was chosen to investigate a comprehensive uncertainty analysis. The most important effect for degradation rates is to avoid instrumentation that changes over time in the field. For instance, a drifting irradiance sensor, which can be achieved through regular calibration, can lead to a substantially erroneous degradation rates. However, the accuracy of the irradiance sensor has negligible impact on degradation rate uncertainty emphasizing that precision (relative accuracy) is more important than absolute accuracy.
Robustness analysis for real parametric uncertainty
NASA Technical Reports Server (NTRS)
Sideris, Athanasios
1989-01-01
Some key results in the literature in the area of robustness analysis for linear feedback systems with structured model uncertainty are reviewed. Some new results are given. Model uncertainty is described as a combination of real uncertain parameters and norm bounded unmodeled dynamics. Here the focus is on the case of parametric uncertainty. An elementary and unified derivation of the celebrated theorem of Kharitonov and the Edge Theorem is presented. Next, an algorithmic approach for robustness analysis in the cases of multilinear and polynomic parametric uncertainty (i.e., the closed loop characteristic polynomial depends multilinearly and polynomially respectively on the parameters) is given. The latter cases are most important from practical considerations. Some novel modifications in this algorithm which result in a procedure of polynomial time behavior in the number of uncertain parameters is outlined. Finally, it is shown how the more general problem of robustness analysis for combined parametric and dynamic (i.e., unmodeled dynamics) uncertainty can be reduced to the case of polynomic parametric uncertainty, and thus be solved by means of the algorithm.
Uncertainty analysis for Ulysses safety evaluation report
Frank, M.V. )
1991-01-01
As part of the effort to review the Ulysses Final Safety Analysis Report and to understand the risk of plutonium release from the Ulysses spacecraft General Purpose Heat Source---Radioisotope Thermal Generator (GPHS-RTG), the Interagency Nuclear Safety Review Panel (INSRP) and the author performed an integrated, quantitative analysis of the uncertainties of the calculated risk of plutonium release from Ulysses. Using state-of-art probabilistic risk assessment technology, the uncertainty analysis accounted for both variability and uncertainty of the key parameters of the risk analysis. The results show that INSRP had high confidence that risk of fatal cancers from potential plutonium release associated with calculated launch and deployment accident scenarios is low.
Spatial uncertainty analysis of population models
Jager, Yetta; King, Anthony Wayne; Schumaker, Nathan; Ashwood, Tom L; Jackson, Barbara L
2004-01-01
This paper describes an approach for conducting spatial uncertainty analysis of spatial population models, and illustrates the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial population models typically simulate birth, death, and migration on an input map that describes habitat. Typically, only a single reference map is available, but we can imagine that a collection of other, slightly different, maps could be drawn to represent a particular species' habitat. As a first approximation, our approach assumes that spatial uncertainty (i.e., the variation among values assigned to a location by such a collection of maps) is constrained by characteristics of the reference map, regardless of how the map was produced. Our approach produces lower levels of uncertainty than alternative methods used in landscape ecology because we condition our alternative landscapes on local properties of the reference map. Simulated spatial uncertainty was higher near the borders of patches. Consequently, average uncertainty was highest for reference maps with equal proportions of suitable and unsuitable habitat, and no spatial autocorrelation. We used two population viability models to evaluate the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial uncertainty produced larger variation among predictions of a spatially explicit model than those of a spatially implicit model. Spatially explicit model predictions of final female population size varied most among landscapes with enough clustered habitat to allow persistence. In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold. We conclude that spatial uncertainty has the greatest effect on persistence when the amount and arrangement of suitable habitat are such that habitat capacity is near the minimum
Uncertainty Analysis of Composite Structures
NASA Technical Reports Server (NTRS)
Noor, Ahmed K.; Starnes, James H., Jr.; Peters, Jeanne M.
2000-01-01
A two-phase approach and a computational procedure are presented for predicting the variability in the nonlinear response of composite structures associated with variations in the geometric and material parameters of the structure. In the first phase, hierarchical sensitivity analysis is used to identify the major parameters, which have the most effect on the response quantities of interest. In the second phase, the major parameters are taken to be fuzzy parameters, and a fuzzy set analysis is used to determine the range of variation of the response, associated with preselected variations in the major parameters. The effectiveness of the procedure is demonstrated by means of a numerical example of a cylindrical panel with four T-shaped stiffeners and a circular cutout.
MOUSE - A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM
Environmental engineering calculations involving uncertainties. either in the model itself or in the data, are far beyond the capabilities of conventional analysis for any but the simplest of models. here exist a number of general-purpose computer simulation languages, using Mont...
Uncertainty Propagation in Hypersonic Vehicle Aerothermoelastic Analysis
NASA Astrophysics Data System (ADS)
Lamorte, Nicolas Etienne
Hypersonic vehicles face a challenging flight environment. The aerothermoelastic analysis of its components requires numerous simplifying approximations. Identifying and quantifying the effect of uncertainties pushes the limits of the existing deterministic models, and is pursued in this work. An uncertainty quantification framework is used to propagate the effects of identified uncertainties on the stability margins and performance of the different systems considered. First, the aeroelastic stability of a typical section representative of a control surface on a hypersonic vehicle is examined. Variability in the uncoupled natural frequencies of the system is modeled to mimic the effect of aerodynamic heating. Next, the stability of an aerodynamically heated panel representing a component of the skin of a generic hypersonic vehicle is considered. Uncertainty in the location of transition from laminar to turbulent flow and the heat flux prediction is quantified using CFD. In both cases significant reductions of the stability margins are observed. A loosely coupled airframe--integrated scramjet engine is considered next. The elongated body and cowl of the engine flow path are subject to harsh aerothermodynamic loading which causes it to deform. Uncertainty associated with deformation prediction is propagated to the engine performance analysis. The cowl deformation is the main contributor to the sensitivity of the propulsion system performance. Finally, a framework for aerothermoelastic stability boundary calculation for hypersonic vehicles using CFD is developed. The usage of CFD enables one to consider different turbulence conditions, laminar or turbulent, and different models of the air mixture, in particular real gas model which accounts for dissociation of molecules at high temperature. The system is found to be sensitive to turbulence modeling as well as the location of the transition from laminar to turbulent flow. Real gas effects play a minor role in the
Extended Forward Sensitivity Analysis for Uncertainty Quantification
Haihua Zhao; Vincent A. Mousseau
2011-09-01
Verification and validation (V&V) are playing more important roles to quantify uncertainties and realize high fidelity simulations in engineering system analyses, such as transients happened in a complex nuclear reactor system. Traditional V&V in the reactor system analysis focused more on the validation part or did not differentiate verification and validation. The traditional approach to uncertainty quantification is based on a 'black box' approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. The 'black box' method mixes numerical errors with all other uncertainties. It is also not efficient to perform sensitivity analysis. Contrary to the 'black box' method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In these types of approaches equations for the propagation of uncertainty are constructed and the sensitivities are directly solved for as variables in the simulation. This paper presents the forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed
Uncertainty Analysis for a Jet Flap Airfoil
NASA Technical Reports Server (NTRS)
Green, Lawrence L.; Cruz, Josue
2006-01-01
An analysis of variance (ANOVA) study was performed to quantify the potential uncertainties of lift and pitching moment coefficient calculations from a computational fluid dynamics code, relative to an experiment, for a jet flap airfoil configuration. Uncertainties due to a number of factors including grid density, angle of attack and jet flap blowing coefficient were examined. The ANOVA software produced a numerical model of the input coefficient data, as functions of the selected factors, to a user-specified order (linear, 2-factor interference, quadratic, or cubic). Residuals between the model and actual data were also produced at each of the input conditions, and uncertainty confidence intervals (in the form of Least Significant Differences or LSD) for experimental, computational, and combined experimental / computational data sets were computed. The LSD bars indicate the smallest resolvable differences in the functional values (lift or pitching moment coefficient) attributable solely to changes in independent variable, given just the input data points from selected data sets. The software also provided a collection of diagnostics which evaluate the suitability of the input data set for use within the ANOVA process, and which examine the behavior of the resultant data, possibly suggesting transformations which should be applied to the data to reduce the LSD. The results illustrate some of the key features of, and results from, the uncertainty analysis studies, including the use of both numerical (continuous) and categorical (discrete) factors, the effects of the number and range of the input data points, and the effects of the number of factors considered simultaneously.
Extended Forward Sensitivity Analysis for Uncertainty Quantification
Haihua Zhao; Vincent A. Mousseau
2008-09-01
This report presents the forward sensitivity analysis method as a means for quantification of uncertainty in system analysis. The traditional approach to uncertainty quantification is based on a “black box” approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. This approach requires large number of simulation runs and therefore has high computational cost. Contrary to the “black box” method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In this approach equations for the propagation of uncertainty are constructed and the sensitivity is solved for as variables in the same simulation. This “glass box” method can generate similar sensitivity information as the above “black box” approach with couples of runs to cover a large uncertainty region. Because only small numbers of runs are required, those runs can be done with a high accuracy in space and time ensuring that the uncertainty of the physical model is being measured and not simply the numerical error caused by the coarse discretization. In the forward sensitivity method, the model is differentiated with respect to each parameter to yield an additional system of the same size as the original one, the result of which is the solution sensitivity. The sensitivity of any output variable can then be directly obtained from these sensitivities by applying the chain rule of differentiation. We extend the forward sensitivity method to include time and spatial steps as special parameters so that the numerical errors can be quantified against other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty analysis. By knowing the relative sensitivity of time and space steps with other
Uncertainty Analysis of Decomposing Polyurethane Foam
NASA Technical Reports Server (NTRS)
Hobbs, Michael L.; Romero, Vicente J.
2000-01-01
Sensitivity/uncertainty analyses are necessary to determine where to allocate resources for improved predictions in support of our nation's nuclear safety mission. Yet, sensitivity/uncertainty analyses are not commonly performed on complex combustion models because the calculations are time consuming, CPU intensive, nontrivial exercises that can lead to deceptive results. To illustrate these ideas, a variety of sensitivity/uncertainty analyses were used to determine the uncertainty associated with thermal decomposition of polyurethane foam exposed to high radiative flux boundary conditions. The polyurethane used in this study is a rigid closed-cell foam used as an encapsulant. Related polyurethane binders such as Estane are used in many energetic materials of interest to the JANNAF community. The complex, finite element foam decomposition model used in this study has 25 input parameters that include chemistry, polymer structure, and thermophysical properties. The response variable was selected as the steady-state decomposition front velocity calculated as the derivative of the decomposition front location versus time. An analytical mean value sensitivity/uncertainty (MV) analysis was used to determine the standard deviation by taking numerical derivatives of the response variable with respect to each of the 25 input parameters. Since the response variable is also a derivative, the standard deviation was essentially determined from a second derivative that was extremely sensitive to numerical noise. To minimize the numerical noise, 50-micrometer element dimensions and approximately 1-msec time steps were required to obtain stable uncertainty results. As an alternative method to determine the uncertainty and sensitivity in the decomposition front velocity, surrogate response surfaces were generated for use with a constrained Latin Hypercube Sampling (LHS) technique. Two surrogate response surfaces were investigated: 1) a linear surrogate response surface (LIN) and 2
LCA data quality: sensitivity and uncertainty analysis.
Guo, M; Murphy, R J
2012-10-01
Life cycle assessment (LCA) data quality issues were investigated by using case studies on products from starch-polyvinyl alcohol based biopolymers and petrochemical alternatives. The time horizon chosen for the characterization models was shown to be an important sensitive parameter for the environmental profiles of all the polymers. In the global warming potential and the toxicity potential categories the comparison between biopolymers and petrochemical counterparts altered as the time horizon extended from 20 years to infinite time. These case studies demonstrated that the use of a single time horizon provide only one perspective on the LCA outcomes which could introduce an inadvertent bias into LCA outcomes especially in toxicity impact categories and thus dynamic LCA characterization models with varying time horizons are recommended as a measure of the robustness for LCAs especially comparative assessments. This study also presents an approach to integrate statistical methods into LCA models for analyzing uncertainty in industrial and computer-simulated datasets. We calibrated probabilities for the LCA outcomes for biopolymer products arising from uncertainty in the inventory and from data variation characteristics this has enabled assigning confidence to the LCIA outcomes in specific impact categories for the biopolymer vs. petrochemical polymer comparisons undertaken. Uncertainty combined with the sensitivity analysis carried out in this study has led to a transparent increase in confidence in the LCA findings. We conclude that LCAs lacking explicit interpretation of the degree of uncertainty and sensitivities are of limited value as robust evidence for decision making or comparative assertions. PMID:22854094
Extended Forward Sensitivity Analysis for Uncertainty Quantification
Haihua Zhao; Vincent A. Mousseau
2013-01-01
This paper presents the extended forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed to run at optimized time and space steps without affecting the confidence of the physical parameter sensitivity results. The time and space steps forward sensitivity analysis method can also replace the traditional time step and grid convergence study with much less computational cost. Two well-defined benchmark problems with manufactured solutions are utilized to demonstrate the method.
Rahman, A.; Tsai, F.T.-C.; White, C.D.; Willson, C.S.
2008-01-01
This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis. ?? 2008 ASCE.
Pretest uncertainty analysis for chemical rocket engine tests
NASA Technical Reports Server (NTRS)
Davidian, Kenneth J.
1987-01-01
A parametric pretest uncertainty analysis has been performed for a chemical rocket engine test at a unique 1000:1 area ratio altitude test facility. Results from the parametric study provide the error limits required in order to maintain a maximum uncertainty of 1 percent on specific impulse. Equations used in the uncertainty analysis are presented.
Uncertainty Analysis and Expert Judgment in Seismic Hazard Analysis
NASA Astrophysics Data System (ADS)
Klügel, Jens-Uwe
2011-01-01
The large uncertainty associated with the prediction of future earthquakes is usually regarded as the main reason for increased hazard estimates which have resulted from some recent large scale probabilistic seismic hazard analysis studies (e.g. the PEGASOS study in Switzerland and the Yucca Mountain study in the USA). It is frequently overlooked that such increased hazard estimates are characteristic for a single specific method of probabilistic seismic hazard analysis (PSHA): the traditional (Cornell-McGuire) PSHA method which has found its highest level of sophistication in the SSHAC probability method. Based on a review of the SSHAC probability model and its application in the PEGASOS project, it is shown that the surprising results of recent PSHA studies can be explained to a large extent by the uncertainty model used in traditional PSHA, which deviates from the state of the art in mathematics and risk analysis. This uncertainty model, the Ang-Tang uncertainty model, mixes concepts of decision theory with probabilistic hazard assessment methods leading to an overestimation of uncertainty in comparison to empirical evidence. Although expert knowledge can be a valuable source of scientific information, its incorporation into the SSHAC probability method does not resolve the issue of inflating uncertainties in PSHA results. Other, more data driven, PSHA approaches in use in some European countries are less vulnerable to this effect. The most valuable alternative to traditional PSHA is the direct probabilistic scenario-based approach, which is closely linked with emerging neo-deterministic methods based on waveform modelling.
Application of uncertainty analysis to cooling tower thermal performance tests
Yost, J.G.; Wheeler, D.E.
1986-01-01
The purpose of this paper is to provide an overview of uncertainty analyses. The following topics are addressed: l. A review and summary of the basic constituents of an uncertainty analysis with definitions and discussion of basic terms; 2. A discussion of the benefits and uses of uncertainty analysis; and 3. Example uncertainty analyses with emphasis on the problems, limitations, and site-specific complications.
Representation of analysis results involving aleatory and epistemic uncertainty.
Johnson, Jay Dean; Helton, Jon Craig; Oberkampf, William Louis; Sallaberry, Cedric J.
2008-08-01
Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.
A TIERED APPROACH TO PERFORMING UNCERTAINTY ANALYSIS IN CONDUCTING EXPOSURE ANALYSIS FOR CHEMICALS
The WHO/IPCS draft Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment provides guidance on recommended strategies for conducting uncertainty analysis as part of human exposure analysis. Specifically, a tiered approach to uncertainty analysis ...
Uncertainty Budget Analysis for Dimensional Inspection Processes (U)
Valdez, Lucas M.
2012-07-26
This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensional inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.
Kettler, Susanne; Kennedy, Marc; McNamara, Cronan; Oberdörfer, Regina; O'Mahony, Cian; Schnabel, Jürgen; Smith, Benjamin; Sprong, Corinne; Faludi, Roland; Tennant, David
2015-08-01
Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties. PMID:25890086
Geoengineering to Avoid Overshoot: An Uncertainty Analysis
NASA Astrophysics Data System (ADS)
Tanaka, K.
2009-04-01
Geoengineering (or climate engineering) using stratospheric sulfur injections (Crutzen, 2006) has been called for research in case of an urgent need for stopping global warming when other mitigation efforts were exhausted. Although there are a number of concerns over this idea (e.g. Robock, 2008), it is still useful to consider geoengineering as a possible method to limit warming caused by overshoot. Overshoot is a feature accompanied by low stabilizations scenarios aiming for a stringent target (Rao et al., 2008) in which total radiative forcing temporarily exceeds the target before reaching there. Scenarios achieving a 50% emission reduction by 2050 produces overshoot. Overshoot could cause sustained warming for decades due to the inertia of the climate system. If stratospheric sulfur injections were to be used as a "last resort" to avoid overshoot, what would be the suitable start-year and injection profile of such an intervention? Wigley (2006) examined climate response to combined mitigation/geoengineering scenarios with the intent to avert overshoot. Wigley's analysis demonstrated a basic potential of such a combined mitigation/geoengineering approach to avoid temperature overshoot - however it considered only simplistic sulfur injection profiles (all started in 2010), just one mitigation scenario, and did not examine the sensitivity of the climate response to any underlying uncertainties. This study builds upon Wigley's premise of the combined mitigation/geoengineering approach and brings associated uncertainty into the analysis. First, this study addresses the question as to how much geoengineering intervention would be needed to avoid overshoot by considering associated uncertainty? Then, would a geoengineering intervention of such a magnitude including uncertainty be permissible in considering all the other side effects? This study begins from the supposition that geoengineering could be employed to cap warming at 2.0°C since preindustrial. A few
The challenges on uncertainty analysis for pebble bed HTGR
Hao, C.; Li, F.; Zhang, H.
2012-07-01
The uncertainty analysis is very popular and important, and many works have been done for Light Water Reactor (LWR), although the experience for the uncertainty analysis in High Temperature Gas cooled Reactor (HTGR) modeling is still in the primary stage. IAEA will launch a Coordination Research Project (CRP) on this topic soon. This paper addresses some challenges for the uncertainty analysis in HTGR modeling, based on the experience of OECD LWR Uncertainty Analysis in Modeling (UAM) activities, and taking into account the peculiarities of pebble bed HTGR designs. The main challenges for HTGR UAM are: the lack of experience, the totally different code packages, the coupling of power distribution, temperature distribution and burnup distribution through the temperature feedback and pebble flow. The most serious challenge is how to deal with the uncertainty in pebble flow, the uncertainty in pebble bed flow modeling, and their contribution to the uncertainty of maximum fuel temperature, which is the most interested parameter for the modular HTGR. (authors)
Uncertainty Analysis with Site Specific Groundwater Models: Experiences and Observations
Brewer, K.
2003-07-15
Groundwater flow and transport predictions are a major component of remedial action evaluations for contaminated groundwater at the Savannah River Site. Because all groundwater modeling results are subject to uncertainty from various causes; quantification of the level of uncertainty in the modeling predictions is beneficial to project decision makers. Complex site-specific models present formidable challenges for implementing an uncertainty analysis.
Uncertainty Analysis of Simulated Hydraulic Fracturing
NASA Astrophysics Data System (ADS)
Chen, M.; Sun, Y.; Fu, P.; Carrigan, C. R.; Lu, Z.
2012-12-01
Artificial hydraulic fracturing is being used widely to stimulate production of oil, natural gas, and geothermal reservoirs with low natural permeability. Optimization of field design and operation is limited by the incomplete characterization of the reservoir, as well as the complexity of hydrological and geomechanical processes that control the fracturing. Thus, there are a variety of uncertainties associated with the pre-existing fracture distribution, rock mechanics, and hydraulic-fracture engineering that require evaluation of their impact on the optimized design. In this study, a multiple-stage scheme was employed to evaluate the uncertainty. We first define the ranges and distributions of 11 input parameters that characterize the natural fracture topology, in situ stress, geomechanical behavior of the rock matrix and joint interfaces, and pumping operation, to cover a wide spectrum of potential conditions expected for a natural reservoir. These parameters were then sampled 1,000 times in an 11-dimensional parameter space constrained by the specified ranges using the Latin-hypercube method. These 1,000 parameter sets were fed into the fracture simulators, and the outputs were used to construct three designed objective functions, i.e. fracture density, opened fracture length and area density. Using PSUADE, three response surfaces (11-dimensional) of the objective functions were developed and global sensitivity was analyzed to identify the most sensitive parameters for the objective functions representing fracture connectivity, which are critical for sweep efficiency of the recovery process. The second-stage high resolution response surfaces were constructed with dimension reduced to the number of the most sensitive parameters. An additional response surface with respect to the objective function of the fractal dimension for fracture distributions was constructed in this stage. Based on these response surfaces, comprehensive uncertainty analyses were conducted
Measurement uncertainty analysis techniques applied to PV performance measurements
Wells, C.
1992-10-01
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Micro-Pulse Lidar Signals: Uncertainty Analysis
NASA Technical Reports Server (NTRS)
Welton, Ellsworth J.; Campbell, James R.; Starr, David OC. (Technical Monitor)
2002-01-01
Micro-pulse lidar (MPL) systems are small, autonomous, eye-safe lidars used for continuous observations of the vertical distribution of cloud and aerosol layers. Since the construction of the first MPL in 1993, procedures have been developed to correct for various instrument effects present in MPL signals. The primary instrument effects include afterpulse, laser-detector cross-talk, and overlap, poor near-range (less than 6 km) focusing. The accurate correction of both afterpulse and overlap effects are required to study both clouds and aerosols. Furthermore, the outgoing energy of the laser pulses and the statistical uncertainty of the MPL detector must also be correctly determined in order to assess the accuracy of MPL observations. The uncertainties associated with the afterpulse, overlap, pulse energy, detector noise, and all remaining quantities affecting measured MPL signals, are determined in this study. The uncertainties are propagated through the entire MPL correction process to give a net uncertainty on the final corrected MPL signal. The results show that in the near range, the overlap uncertainty dominates. At altitudes above the overlap region, the dominant source of uncertainty is caused by uncertainty in the pulse energy. However, if the laser energy is low, then during mid-day, high solar background levels can significantly reduce the signal-to-noise of the detector. In such a case, the statistical uncertainty of the detector count rate becomes dominant at altitudes above the overlap region.
NASA Astrophysics Data System (ADS)
Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng
2016-09-01
This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.
Statistical Uncertainty Analysis Applied to Criticality Calculation
Hartini, Entin; Andiwijayakusuma, Dinan; Susmikanti, Mike; Nursinta, A. W.
2010-06-22
In this paper, we present an uncertainty methodology based on a statistical approach, for assessing uncertainties in criticality prediction using monte carlo method due to uncertainties in the isotopic composition of the fuel. The methodology has been applied to criticality calculations with MCNP5 with additional stochastic input of the isotopic fuel composition. The stochastic input were generated using the latin hypercube sampling method based one the probability density function of each nuclide composition. The automatic passing of the stochastic input to the MCNP and the repeated criticality calculation is made possible by using a python script to link the MCNP and our latin hypercube sampling code.
Uncertainty Analysis of Historical Hurricane Data
NASA Technical Reports Server (NTRS)
Green, Lawrence L.
2007-01-01
An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm
Characterization and evaluation of uncertainty in probabilistic risk analysis
Parry, G.W.; Winter, P.W.
1981-01-01
The sources of uncertainty in probabilistic risk analysis are discussed, using the event and fault-tree methodology as an example. The role of statistics in quantifying these uncertainties is investigated. A class of uncertainties is identified which are, at present, unquantifiable using either classical or Bayesian statistics. it is argued that Bayesian statistics is the more appropriate vehicle for the probabilistic analysis of rare events, and a short review is given with some discussion on the representation of ignorance.
Radiometer Design Analysis Based Upon Measurement Uncertainty
NASA Technical Reports Server (NTRS)
Racette, Paul E.; Lang, Roger H.
2004-01-01
This paper introduces a method for predicting the performance of a radiometer design based on calculating the measurement uncertainty. The variety in radiometer designs and the demand for improved radiometric measurements justify the need for a more general and comprehensive method to assess system performance. Radiometric resolution, or sensitivity, is a figure of merit that has been commonly used to characterize the performance of a radiometer. However when evaluating the performance of a calibration design for a radiometer, the use of radiometric resolution has limited application. These limitations are overcome by considering instead the measurement uncertainty. A method for calculating measurement uncertainty for a generic radiometer design including its calibration algorithm is presented. The result is a generalized technique by which system calibration architectures and design parameters can be studied to optimize instrument performance for given requirements and constraints. Example applications demonstrate the utility of using measurement uncertainty as a figure of merit.
Uncertainty analysis of statistical downscaling methods
NASA Astrophysics Data System (ADS)
Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas
2006-03-01
Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of downscaled and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of downscaled daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation downscaling, in addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and downscaled daily precipitation. The study has been carried out using 40 years of observed and downscaled daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its downscaled results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN.
Uncertainty Analysis of Thermal Comfort Parameters
NASA Astrophysics Data System (ADS)
Ribeiro, A. Silva; Alves e Sousa, J.; Cox, Maurice G.; Forbes, Alistair B.; Matias, L. Cordeiro; Martins, L. Lages
2015-08-01
International Standard ISO 7730:2005 defines thermal comfort as that condition of mind that expresses the degree of satisfaction with the thermal environment. Although this definition is inevitably subjective, the Standard gives formulae for two thermal comfort indices, predicted mean vote ( PMV) and predicted percentage dissatisfied ( PPD). The PMV formula is based on principles of heat balance and experimental data collected in a controlled climate chamber under steady-state conditions. The PPD formula depends only on PMV. Although these formulae are widely recognized and adopted, little has been done to establish measurement uncertainties associated with their use, bearing in mind that the formulae depend on measured values and tabulated values given to limited numerical accuracy. Knowledge of these uncertainties are invaluable when values provided by the formulae are used in making decisions in various health and civil engineering situations. This paper examines these formulae, giving a general mechanism for evaluating the uncertainties associated with values of the quantities on which the formulae depend. Further, consideration is given to the propagation of these uncertainties through the formulae to provide uncertainties associated with the values obtained for the indices. Current international guidance on uncertainty evaluation is utilized.
Spatial Uncertainty Analysis of Ecological Models
Jager, H.I.; Ashwood, T.L.; Jackson, B.L.; King, A.W.
2000-09-02
The authors evaluated the sensitivity of a habitat model and a source-sink population model to spatial uncertainty in landscapes with different statistical properties and for hypothetical species with different habitat requirements. Sequential indicator simulation generated alternative landscapes from a source map. Their results showed that spatial uncertainty was highest for landscapes in which suitable habitat was rare and spatially uncorrelated. Although, they were able to exert some control over the degree of spatial uncertainty by varying the sampling density drawn from the source map, intrinsic spatial properties (i.e., average frequency and degree of spatial autocorrelation) played a dominant role in determining variation among realized maps. To evaluate the ecological significance of landscape variation, they compared the variation in predictions from a simple habitat model to variation among landscapes for three species types. Spatial uncertainty in predictions of the amount of source habitat depended on both the spatial life history characteristics of the species and the statistical attributes of the synthetic landscapes. Species differences were greatest when the landscape contained a high proportion of suitable habitat. The predicted amount of source habitat was greater for edge-dependent (interior) species in landscapes with spatially uncorrelated(correlated) suitable habitat. A source-sink model demonstrated that, although variation among landscapes resulted in relatively little variation in overall population growth rate, this spatial uncertainty was sufficient in some situations, to produce qualitatively different predictions about population viability (i.e., population decline vs. increase).
Uncertainty Analysis in Space Radiation Protection
NASA Technical Reports Server (NTRS)
Cucinotta, Francis A.
2011-01-01
Space radiation is comprised of high energy and charge (HZE) nuclei, protons, and secondary radiation including neutrons. The uncertainties in estimating the health risks from galactic cosmic rays (GCR) are a major limitation to the length of space missions, the evaluation of potential risk mitigation approaches, and application of the As Low As Reasonably Achievable (ALARA) principle. For long duration space missio ns, risks may approach radiation exposure limits, therefore the uncertainties in risk projections become a major safety concern and methodologies used for ground-based works are not deemed to be sufficient. NASA limits astronaut exposures to a 3% risk of exposure induced death (REID) and protects against uncertainties in risks projections using an assessment of 95% confidence intervals in the projection model. We discuss NASA s approach to space radiation uncertainty assessments and applications for the International Space Station (ISS) program and design studies of future missions to Mars and other destinations. Several features of NASA s approach will be discussed. Radiation quality descriptions are based on the properties of radiation tracks rather than LET with probability distribution functions (PDF) for uncertainties derived from radiobiology experiments at particle accelerators. The application of age and gender specific models for individual astronauts is described. Because more than 90% of astronauts are never-smokers, an alternative risk calculation for never-smokers is used and will be compared to estimates for an average U.S. population. Because of the high energies of the GCR limits the benefits of shielding and the limited role expected for pharmaceutical countermeasures, uncertainty reduction continues to be the optimal approach to improve radiation safety for space missions.
Controllable set analysis for planetary landing under model uncertainties
NASA Astrophysics Data System (ADS)
Long, Jiateng; Gao, Ai; Cui, Pingyuan
2015-07-01
Controllable set analysis is a beneficial method in planetary landing mission design by feasible entry state selection in order to achieve landing accuracy and satisfy entry path constraints. In view of the severe impact of model uncertainties on planetary landing safety and accuracy, the purpose of this paper is to investigate the controllable set under uncertainties between on-board model and the real situation. Controllable set analysis under model uncertainties is composed of controllable union set (CUS) analysis and controllable intersection set (CIS) analysis. Definitions of CUS and CIS are demonstrated and computational method of them based on Gauss pseudospectral method is presented. Their applications on entry states distribution analysis under uncertainties and robustness of nominal entry state selection to uncertainties are illustrated by situations with ballistic coefficient, lift-to-drag ratio and atmospheric uncertainty in Mars entry. With analysis of CUS and CIS, the robustness of entry state selection and entry trajectory to model uncertainties can be guaranteed, thus enhancing the safety, reliability and accuracy under model uncertainties during planetary entry and landing.
Uncertainty Modeling for Structural Control Analysis and Synthesis
NASA Technical Reports Server (NTRS)
Campbell, Mark E.; Crawley, Edward F.
1996-01-01
The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.
Measurement uncertainty analysis techniques applied to PV performance measurements
Wells, C
1992-10-01
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment`s final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Analysis of the Uncertainty in Microbubble Characterization.
Harfield, Caroline; Fury, Christopher R; Memoli, Gianluca; Jones, Philip; Ovenden, Nick; Stride, Eleanor
2016-06-01
There is increasing interest in the use of microbubble contrast agents for quantitative imaging applications such as perfusion and blood pressure measurement. The response of a microbubble to ultrasound excitation is, however, extremely sensitive to its size, the properties of its coating and the characteristics of the sound field and surrounding environment. Hence the results of microbubble characterization experiments can be significantly affected by experimental uncertainties, and this can limit their utility in predictive modelling. The aim of this study was to attempt to quantify these uncertainties and their influence upon measured microbubble characteristics. Estimates for the parameters characterizing the microbubble coating were obtained by fitting model data to numerical simulations of microbubble dynamics. The effect of uncertainty in different experimental parameters was gauged by modifying the relevant input values to the fitting process. The results indicate that even the minimum expected uncertainty in, for example, measurements of microbubble radius using conventional optical microscopy, leads to variations in the estimated coating parameters of ∼20%. This should be taken into account in designing microbubble characterization experiments and in the use of data obtained from them. PMID:26993799
New Programming Environments for Uncertainty Analysis
NASA Astrophysics Data System (ADS)
Hill, M. C.; Poeter, E. P.; Banta, E. R.; Christensen, S.; Cooley, R. L.; Ely, D. M.; Babendreier, J.; Leavesley, G.; Tonkin, M.; Julich, R.
2005-12-01
We live in a world of faster computers, better GUI's and visualization technology, increasing international cooperation made possible by new digital infrastructure, new agreements between US federal agencies (such as ISCMEM), new European Union programs (such as Harmoniqua), and greater collaboration between US university scientists through CUAHSI. These changes provide new resources for tackling the difficult job of quantifying how well our models perform. This talk introduces new programming environments that take advantage of these new developments and will change the paradigm of how we develop methods for uncertainty evaluation. For example, the programming environments provided by COSU API, JUPITER API, and Sensitivity/Optimization Toolbox provide enormous opportunities for faster and more meaningful evaluation of uncertainties. Instead of waiting years for ideas and theories to be compared in the complex circumstances of interest to resource managers, these new programming environments will expedite the process. In the new paradigm, unproductive ideas and theories will be revealed more quickly, productive ideas and theories will more quickly be used to address our increasingly difficult water resources problems. As examples, two ideas in JUPITER API applications are presented: uncertainty correction factors that account for system complexities not represented in models, and PPR and OPR statistics used to identify new data needed to reduce prediction uncertainty.
Uncertainty Analysis of Air Radiation for Lunar Return Shock Layers
NASA Technical Reports Server (NTRS)
Kleb, Bil; Johnston, Christopher O.
2008-01-01
By leveraging a new uncertainty markup technique, two risk analysis methods are used to compute the uncertainty of lunar-return shock layer radiation predicted by the High temperature Aerothermodynamic Radiation Algorithm (HARA). The effects of epistemic uncertainty, or uncertainty due to a lack of knowledge, is considered for the following modeling parameters: atomic line oscillator strengths, atomic line Stark broadening widths, atomic photoionization cross sections, negative ion photodetachment cross sections, molecular bands oscillator strengths, and electron impact excitation rates. First, a simplified shock layer problem consisting of two constant-property equilibrium layers is considered. The results of this simplified problem show that the atomic nitrogen oscillator strengths and Stark broadening widths in both the vacuum ultraviolet and infrared spectral regions, along with the negative ion continuum, are the dominant uncertainty contributors. Next, three variable property stagnation-line shock layer cases are analyzed: a typical lunar return case and two Fire II cases. For the near-equilibrium lunar return and Fire 1643-second cases, the resulting uncertainties are very similar to the simplified case. Conversely, the relatively nonequilibrium 1636-second case shows significantly larger influence from electron impact excitation rates of both atoms and molecules. For all cases, the total uncertainty in radiative heat flux to the wall due to epistemic uncertainty in modeling parameters is 30% as opposed to the erroneously-small uncertainty levels (plus or minus 6%) found when treating model parameter uncertainties as aleatory (due to chance) instead of epistemic (due to lack of knowledge).
INTEGRATED UNCERTAINTY ANALYSIS TO SUPPORT EFFECTIVE ENVIRONMENTAL DECISION-MAKING
The expected results of this research are to 1) contribute to our understanding of dominant uncertainties in models typically used across many RIAs, 2) identify integrated uncertainty analysis strategies that the EPA and other regulatory agencies can use to evaluate overall un...
AN IMPROVEMENT TO THE MOUSE COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM
The original MOUSE (Modular Oriented Uncertainty System) system was designed to deal with the problem of uncertainties in Environmental engineering calculations, such as a set of engineering cast or risk analysis equations. It was especially intended for use by individuals with l...
Analysis of Hydrogeologic Conceptual Model and Parameter Uncertainty
Meyer, Philip D.; Nicholson, Thomas J.; Mishra, Srikanta
2003-06-24
A systematic methodology for assessing hydrogeologic conceptual model, parameter, and scenario uncertainties is being developed to support technical reviews of environmental assessments related to decommissioning of nuclear facilities. The first major task being undertaken is to produce a coupled parameter and conceptual model uncertainty assessment methodology. This task is based on previous studies that have primarily dealt individually with these two types of uncertainties. Conceptual model uncertainty analysis is based on the existence of alternative conceptual models that are generated using a set of clearly stated guidelines targeted at the needs of NRC staff. Parameter uncertainty analysis makes use of generic site characterization data as well as site-specific characterization and monitoring data to evaluate parameter uncertainty in each of the alternative conceptual models. Propagation of parameter uncertainty will be carried out through implementation of a general stochastic model of groundwater flow and transport in the saturated and unsaturated zones. Evaluation of prediction uncertainty will make use of Bayesian model averaging and visualization of model results. The goal of this study is to develop a practical tool to quantify uncertainties in the conceptual model and parameters identified in performance assessments.
Automated uncertainty analysis methods in the FRAP computer codes. [PWR
Peck, S O
1980-01-01
A user oriented, automated uncertainty analysis capability has been incorporated in the Fuel Rod Analysis Program (FRAP) computer codes. The FRAP codes have been developed for the analysis of Light Water Reactor fuel rod behavior during steady state (FRAPCON) and transient (FRAP-T) conditions as part of the United States Nuclear Regulatory Commission's Water Reactor Safety Research Program. The objective of uncertainty analysis of these codes is to obtain estimates of the uncertainty in computed outputs of the codes is to obtain estimates of the uncertainty in computed outputs of the codes as a function of known uncertainties in input variables. This paper presents the methods used to generate an uncertainty analysis of a large computer code, discusses the assumptions that are made, and shows techniques for testing them. An uncertainty analysis of FRAP-T calculated fuel rod behavior during a hypothetical loss-of-coolant transient is presented as an example and carried through the discussion to illustrate the various concepts.
NASA Technical Reports Server (NTRS)
Wang, T.; Simon, T. W.
1988-01-01
Development of a recent experimental program to investigate the effects of streamwise curvature on boundary layer transition required making a bendable, heated and instrumented test wall, a rather nonconventional surface. The present paper describes this surface, the design choices made in its development and how uncertainty analysis was used, beginning early in the test program, to make such design choices. Published uncertainty analysis techniques were found to be of great value; but, it became clear that another step, one herein called the pre-test analysis, would aid the program development. Finally, it is shown how the uncertainty analysis was used to determine whether the test surface was qualified for service.
Detailed Uncertainty Analysis of the ZEM-3 Measurement System
NASA Technical Reports Server (NTRS)
Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred
2014-01-01
The measurement of Seebeck coefficient and electrical resistivity are critical to the investigation of all thermoelectric systems. Therefore, it stands that the measurement uncertainty must be well understood to report ZT values which are accurate and trustworthy. A detailed uncertainty analysis of the ZEM-3 measurement system has been performed. The uncertainty analysis calculates error in the electrical resistivity measurement as a result of sample geometry tolerance, probe geometry tolerance, statistical error, and multi-meter uncertainty. The uncertainty on Seebeck coefficient includes probe wire correction factors, statistical error, multi-meter uncertainty, and most importantly the cold-finger effect. The cold-finger effect plagues all potentiometric (four-probe) Seebeck measurement systems, as heat parasitically transfers through thermocouple probes. The effect leads to an asymmetric over-estimation of the Seebeck coefficient. A thermal finite element analysis allows for quantification of the phenomenon, and provides an estimate on the uncertainty of the Seebeck coefficient. The thermoelectric power factor has been found to have an uncertainty of +9-14 at high temperature and 9 near room temperature.
Including uncertainty in hazard analysis through fuzzy measures
Bott, T.F.; Eisenhawer, S.W.
1997-12-01
This paper presents a method for capturing the uncertainty expressed by an Hazard Analysis (HA) expert team when estimating the frequencies and consequences of accident sequences and provides a sound mathematical framework for propagating this uncertainty to the risk estimates for these accident sequences. The uncertainty is readily expressed as distributions that can visually aid the analyst in determining the extent and source of risk uncertainty in HA accident sequences. The results also can be expressed as single statistics of the distribution in a manner analogous to expressing a probabilistic distribution as a point-value statistic such as a mean or median. The study discussed here used data collected during the elicitation portion of an HA on a high-level waste transfer process to demonstrate the techniques for capturing uncertainty. These data came from observations of the uncertainty that HA team members expressed in assigning frequencies and consequences to accident sequences during an actual HA. This uncertainty was captured and manipulated using ideas from possibility theory. The result of this study is a practical method for displaying and assessing the uncertainty in the HA team estimates of the frequency and consequences for accident sequences. This uncertainty provides potentially valuable information about accident sequences that typically is lost in the HA process.
An Approach of Uncertainty Evaluation for Thermal-Hydraulic Analysis
Katsunori Ogura; Hisashi Ninokata
2002-07-01
An approach to evaluate uncertainty systematically for thermal-hydraulic analysis programs is demonstrated. The approach is applied to the Peach Bottom Unit 2 Turbine Trip 2 Benchmark and is validated. (authors)
A Stochastic Collocation Algorithm for Uncertainty Analysis
NASA Technical Reports Server (NTRS)
Mathelin, Lionel; Hussaini, M. Yousuff; Zang, Thomas A. (Technical Monitor)
2003-01-01
This report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters.
Uncertainty Analysis of Knowledge Reductions in Rough Sets
Zhang, Nan
2014-01-01
Uncertainty analysis is a vital issue in intelligent information processing, especially in the age of big data. Rough set theory has attracted much attention to this field since it was proposed. Relative reduction is an important problem of rough set theory. Different relative reductions have been investigated for preserving some specific classification abilities in various applications. This paper examines the uncertainty analysis of five different relative reductions in four aspects, that is, reducts' relationship, boundary region granularity, rules variance, and uncertainty measure according to a constructed decision table. PMID:25258725
Sensitivity analysis for handling uncertainty in an economic evaluation.
Limwattananon, Supon
2014-05-01
To meet updated international standards, this paper revises the previous Thai guidelines for conducting sensitivity analyses as part of the decision analysis model for health technology assessment. It recommends both deterministic and probabilistic sensitivity analyses to handle uncertainty of the model parameters, which are best represented graphically. Two new methodological issues are introduced-a threshold analysis of medicines' unit prices for fulfilling the National Lists of Essential Medicines' requirements and the expected value of information for delaying decision-making in contexts where there are high levels of uncertainty. Further research is recommended where parameter uncertainty is significant and where the cost of conducting the research is not prohibitive. PMID:24964700
A stochastic approach to uncertainty quantification in residual moveout analysis
NASA Astrophysics Data System (ADS)
Johng-Ay, T.; Landa, E.; Dossou-Gbété, S.; Bordes, L.
2015-06-01
Oil and gas exploration and production relies usually on the interpretation of a single seismic image, which is obtained from observed data. However, the statistical nature of seismic data and the various approximations and assumptions are sources of uncertainties which may corrupt the evaluation of parameters. The quantification of these uncertainties is a major issue which supposes to help in decisions that have important social and commercial implications. The residual moveout analysis, which is an important step in seismic data processing is usually performed by a deterministic approach. In this paper we discuss a Bayesian approach to the uncertainty analysis.
Deconvolution of variability and uncertainty in the Cassini safety analysis
NASA Astrophysics Data System (ADS)
Kampas, Frank J.; Loughin, Stephen
1998-01-01
The standard method for propagation of uncertainty in a risk analysis requires rerunning the risk calculation numerous times with model parameters chosen from their uncertainty distributions. This was not practical for the Cassini nuclear safety analysis, due to the computationally intense nature of the risk calculation. A less computationally intense procedure was developed which requires only two calculations for each accident case. The first of these is the standard ``best-estimate'' calculation. In the second calculation, variables and parameters change simultaneously. The mathematical technique of deconvolution is then used to separate out an uncertainty multiplier distribution, which can be used to calculate distribution functions at various levels of confidence.
Uncertainty analysis technique for OMEGA Dante measurementsa)
NASA Astrophysics Data System (ADS)
May, M. J.; Widmann, K.; Sorce, C.; Park, H.-S.; Schneider, M.
2010-10-01
The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.
Uncertainty Analysis Technique for OMEGA Dante Measurements
May, M J; Widmann, K; Sorce, C; Park, H; Schneider, M
2010-05-07
The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.
Uncertainty analysis technique for OMEGA Dante measurements
May, M. J.; Widmann, K.; Sorce, C.; Park, H.-S.; Schneider, M.
2010-10-15
The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.
Environmental engineering calculations involving uncertainties; either in the model itself or in the data, are far beyond the capabilities of conventional analysis for any but the simplest of models. There exist a number of general-purpose computer simulation languages, using Mon...
Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions
NASA Technical Reports Server (NTRS)
Crespo, Luis G.; Munoz, Cesar A.; Narkawicz, Anthony J.; Kenny, Sean P.; Giesy, Daniel P.
2011-01-01
This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. A Bernstein expansion approach is used to size hyper-rectangular subsets while a sum of squares programming approach is used to size quasi-ellipsoidal subsets. These methods are applicable to requirement functions whose functional dependency on the uncertainty is a known polynomial. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the uncertainty model assumed (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.
Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions
NASA Technical Reports Server (NTRS)
Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.
2011-01-01
This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.
The Role of Uncertainty in Aerospace Vehicle Analysis and Design
NASA Technical Reports Server (NTRS)
Kenny, Sean P.; Crespo, Luis G.
2011-01-01
Effective uncertainty quantification (UQ) begins at the earliest phase in the design phase for which there are adequate models and continues tightly integrated to the analysis and design cycles as the refinement of the models and the fidelity of the tools increase. It is essential that uncertainty quantification strategies provide objective information to support the processes of identifying, analyzing and accommodating for the effects of uncertainty. Assessments of uncertainty should never render the results more difficult for engineers and decision makers to comprehend, but instead provide them with critical information to assist with resource utilization decisions and risk mitigation strategies. Success would be measured by the tools to enable engineers and decision makers to effectively balance critical project resources against system requirements while accounting for the impact of uncertainty.
Uncertainty analysis of geothermal energy economics
NASA Astrophysics Data System (ADS)
Sener, Adil Caner
This dissertation research endeavors to explore geothermal energy economics by assessing and quantifying the uncertainties associated with the nature of geothermal energy and energy investments overall. The study introduces a stochastic geothermal cost model and a valuation approach for different geothermal power plant development scenarios. The Monte Carlo simulation technique is employed to obtain probability distributions of geothermal energy development costs and project net present values. In the study a stochastic cost model with incorporated dependence structure is defined and compared with the model where random variables are modeled as independent inputs. One of the goals of the study is to attempt to shed light on the long-standing modeling problem of dependence modeling between random input variables. The dependence between random input variables will be modeled by employing the method of copulas. The study focuses on four main types of geothermal power generation technologies and introduces a stochastic levelized cost model for each technology. Moreover, we also compare the levelized costs of natural gas combined cycle and coal-fired power plants with geothermal power plants. The input data used in the model relies on the cost data recently reported by government agencies and non-profit organizations, such as the Department of Energy, National Laboratories, California Energy Commission and Geothermal Energy Association. The second part of the study introduces the stochastic discounted cash flow valuation model for the geothermal technologies analyzed in the first phase. In this phase of the study, the Integrated Planning Model (IPM) software was used to forecast the revenue streams of geothermal assets under different price and regulation scenarios. These results are then combined to create a stochastic revenue forecast of the power plants. The uncertainties in gas prices and environmental regulations will be modeled and their potential impacts will be
Analysis of automated highway system risks and uncertainties. Volume 5
Sicherman, A.
1994-10-01
This volume describes a risk analysis performed to help identify important Automated Highway System (AHS) deployment uncertainties and quantify their effect on costs and benefits for a range of AHS deployment scenarios. The analysis identified a suite of key factors affecting vehicle and roadway costs, capacities and market penetrations for alternative AHS deployment scenarios. A systematic protocol was utilized for obtaining expert judgments of key factor uncertainties in the form of subjective probability percentile assessments. Based on these assessments, probability distributions on vehicle and roadway costs, capacity and market penetration were developed for the different scenarios. The cost/benefit risk methodology and analysis provide insights by showing how uncertainties in key factors translate into uncertainties in summary cost/benefit indices.
Little, M.P.; Muirhead, C.R.; Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.; Harper, F.T.; Hora, S.C.
1997-12-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA late health effects models.
Haskin, F.E.; Harper, F.T.; Goossens, L.H.J.; Kraan, B.C.P.; Grupa, J.B.
1997-12-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA early health effects models.
Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.; Harrison, J.D.; Harper, F.T.; Hora, S.C.
1998-04-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models.
Proceedings of the CEC/USDOE workshop on uncertainty analysis
Elderkin, C.E. ); Kelly, G.N. )
1990-09-01
In recent years it has become increasingly important to specify the uncertainty inherent in consequence assessments and in the models that trace radionuclides from their source, through the environment, to their impacts on human health. European and US scientists have, been independently developing and applying methods for analyzing uncertainty. It recently became apparent that a scientific exchange on this subject would be beneficial as improvements are sought and as uncertainty methods find broader application. The Commission of the European Communities (CEC) and the Office of Health and Environmental Research of the US Department of Energy (OHER/DOE), through their continuing agreement for cooperation, decided to co-sponsor the CEC/USDOE Workshop on Uncertainty Analysis. CEC's Radiation Protection Research Programme and OHER's Atmospheric Studies in Complex Terrain Program collaborated in planning and organizing the workshop, which was held in Santa Fe, New Mexico, on November 13 through 16, 1989. As the workshop progressed, the perspectives of individual participants, each with their particular background and interests in some segment of consequence assessment and its uncertainties, contributed to a broader view of how uncertainties are introduced and handled. This proceedings contains, first, the editors' introduction to the problem of uncertainty analysis and their general summary and conclusions. These are then followed by the results of the working groups, and the abstracts of individual presentations.
Uncertainty of calculation results in vehicle collision analysis.
Wach, Wojciech; Unarski, Jan
2007-04-11
In the analysis of road accidents two types of calculation result uncertainty can be distinguished: modelling uncertainty and uncertainty in calculation results [R.M. Brach, M. Brach, Vehicle Accident Analysis & Reconstruction Methods, SAE International Publisher, Warrendale, 2005]. The problem becomes very important first of all when minor modifications of input parameters or application of different models of the phenomenon lead to a fundamentally different answer to the question posed by the court. The aim of the paper was to prove the necessity of including the problem of uncertainty in calculations related to vehicle collision mechanics and to justify the application of different error analysis methods recommendable in vehicle collision reconstruction. The data file from crash test No. 7 [H. Burg, M. Lindenmann, Unfallversuche, Verlag Information Ambs, Kippenheim, 1982] was used, the selection restricted to the range typical of average police records of collision place. Collision speeds were calculated using two methods: reconstruction and simulation. The analysis of uncertainty was carried out. Maximum and mean square uncertainty were calculated by means of total differential of relevant forms. Since the reconstruction resulted in very broad error intervals of uniform distribution, additional calculations were performed by the Monte Carlo method using algorithm described in [W. Wach, J. Unarski, Determination of vehicle velocities and collision location by means of Monte Carlo simulation method, Special Publication Accident Reconstruction SP-1999, SAE Paper No. 2006-01-0907, 2006]. PMID:16884874
New challenges on uncertainty propagation assessment of flood risk analysis
NASA Astrophysics Data System (ADS)
Martins, Luciano; Aroca-Jiménez, Estefanía; Bodoque, José M.; Díez-Herrero, Andrés
2016-04-01
Natural hazards, such as floods, cause considerable damage to the human life, material and functional assets every year and around the World. Risk assessment procedures has associated a set of uncertainties, mainly of two types: natural, derived from stochastic character inherent in the flood process dynamics; and epistemic, that are associated with lack of knowledge or the bad procedures employed in the study of these processes. There are abundant scientific and technical literature on uncertainties estimation in each step of flood risk analysis (e.g. rainfall estimates, hydraulic modelling variables); but very few experience on the propagation of the uncertainties along the flood risk assessment. Therefore, epistemic uncertainties are the main goal of this work, in particular,understand the extension of the propagation of uncertainties throughout the process, starting with inundability studies until risk analysis, and how far does vary a proper analysis of the risk of flooding. These methodologies, such as Polynomial Chaos Theory (PCT), Method of Moments or Monte Carlo, are used to evaluate different sources of error, such as data records (precipitation gauges, flow gauges...), hydrologic and hydraulic modelling (inundation estimation), socio-demographic data (damage estimation) to evaluate the uncertainties propagation (UP) considered in design flood risk estimation both, in numerical and cartographic expression. In order to consider the total uncertainty and understand what factors are contributed most to the final uncertainty, we used the method of Polynomial Chaos Theory (PCT). It represents an interesting way to handle to inclusion of uncertainty in the modelling and simulation process. PCT allows for the development of a probabilistic model of the system in a deterministic setting. This is done by using random variables and polynomials to handle the effects of uncertainty. Method application results have a better robustness than traditional analysis
Uncertainty and sensitivity analysis and its applications in OCD measurements
NASA Astrophysics Data System (ADS)
Vagos, Pedro; Hu, Jiangtao; Liu, Zhuan; Rabello, Silvio
2009-03-01
This article describes an Uncertainty & Sensitivity Analysis package, a mathematical tool that can be an effective time-shortcut for optimizing OCD models. By including real system noises in the model, an accurate method for predicting measurements uncertainties is shown. The assessment, in an early stage, of the uncertainties, sensitivities and correlations of the parameters to be measured drives the user in the optimization of the OCD measurement strategy. Real examples are discussed revealing common pitfalls like hidden correlations and simulation results are compared with real measurements. Special emphasis is given to 2 different cases: 1) the optimization of the data set of multi-head metrology tools (NI-OCD, SE-OCD), 2) the optimization of the azimuth measurement angle in SE-OCD. With the uncertainty and sensitivity analysis result, the right data set and measurement mode (NI-OCD, SE-OCD or NI+SE OCD) can be easily selected to achieve the best OCD model performance.
Analysis of uncertainties in turbine metal temperature predictions
NASA Technical Reports Server (NTRS)
Stepka, F. S.
1980-01-01
An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.
Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization
NASA Technical Reports Server (NTRS)
Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred
2014-01-01
In order to provide a complete description of a materials thermoelectric power factor, in addition to the measured nominal value, an uncertainty interval is required. The uncertainty may contain sources of measurement error including systematic bias error and precision error of a statistical nature. The work focuses specifically on the popular ZEM-3 (Ulvac Technologies) measurement system, but the methods apply to any measurement system. The analysis accounts for sources of systematic error including sample preparation tolerance, measurement probe placement, thermocouple cold-finger effect, and measurement parameters; in addition to including uncertainty of a statistical nature. Complete uncertainty analysis of a measurement system allows for more reliable comparison of measurement data between laboratories.
INSPECTION SHOP: PLAN TO PROVIDE UNCERTAINTY ANALYSIS WITH MEASUREMENTS
Nederbragt, W W
2006-12-20
The LLNL inspection shop is chartered to make dimensional measurements of components for critical programmatic experiments. These measurements ensure that components are within tolerance and provide geometric details that can be used to further refine simulations. For these measurements to be useful, they must be significantly more accurate than the tolerances that are being checked. For example, if a part has a specified dimension of 100 millimeters and a tolerance of 1 millimeter, then the precision and/or accuracy of the measurement should be less than 1 millimeter. Using the ''10-to-1 gaugemaker's rule of thumb'', the desired precision of the measurement should be less than 100 micrometers. Currently, the process for associating measurement uncertainty with data is not standardized, nor is the uncertainty based on a thorough uncertainty analysis. The goal of this project is to begin providing measurement uncertainty statements with critical measurements performed in the inspection shop. To accomplish this task, comprehensive knowledge about the underlying sources of uncertainty for measurement instruments need to be understood and quantified. Moreover, measurements of elemental uncertainties for each physical source need to be combined in a meaningful way to obtain an overall measurement uncertainty.
Uncertainty analysis for common Seebeck and electrical resistivity measurement systems.
Mackey, Jon; Dynys, Frederick; Sehirlioglu, Alp
2014-08-01
This work establishes the level of uncertainty for electrical measurements commonly made on thermoelectric samples. The analysis targets measurement systems based on the four probe method. Sources of uncertainty for both electrical resistivity and Seebeck coefficient were identified and evaluated. Included are reasonable estimates on the magnitude of each source, and cumulative propagation of error. Uncertainty for the Seebeck coefficient includes the cold-finger effect which has been quantified with thermal finite element analysis. The cold-finger effect, which is a result of parasitic heat transfer down the thermocouple probes, leads to an asymmetric over-estimation of the Seebeck coefficient. A silicon germanium thermoelectric sample has been characterized to provide an understanding of the total measurement uncertainty. The electrical resistivity was determined to contain uncertainty of ±7.0% across any measurement temperature. The Seebeck coefficient of the system is +1.0%/-13.1% at high temperature and ±1.0% near room temperature. The power factor has a combined uncertainty of +7.3%/-27.0% at high temperature and ±7.5% near room temperature. These ranges are calculated to be typical values for a general four probe Seebeck and resistivity measurement configuration. PMID:25173324
Effect of material uncertainties on dynamic analysis of piezoelectric fans
NASA Astrophysics Data System (ADS)
Srivastava, Swapnil; Yadav, Shubham Kumar; Mukherjee, Sujoy
2015-04-01
A piezofan is a resonant device that uses a piezoceramic material to induce oscillations in a cantilever beam. In this study, lumped-mass modelling is used to analyze a piezoelectric fan. Uncertainties are associated with the piezoelectric structures due to several reasons such as variation during manufacturing process, temperature, presence of adhesive layer between the piezoelectric actuator/sensor and the shim stock etc. Presence of uncertainty in the piezoelectric materials can influence the dynamic behavior of the piezoelectric fan such as natural frequency, tip deflection etc. Moreover, these quantities will also affect the performance parameters of the piezoelectric fan. Uncertainty analysis is performed using classical Monte Carlo Simulation (MCS). It is found that the propagation of uncertainty causes significant deviations from the baseline deterministic predictions, which also affect the achievable performance of the piezofan. The numerical results in this paper provide useful bounds on several performance parameters of the cooling fan and will enhance confidence in the design process.
Analysis and Reduction of Complex Networks Under Uncertainty
Knio, Omar M
2014-04-09
This is a collaborative proposal that aims at developing new methods for the analysis and reduction of complex multiscale networks under uncertainty. The approach is based on combining methods of computational singular perturbation (CSP) and probabilistic uncertainty quantification. In deterministic settings, CSP yields asymptotic approximations of reduced-dimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing uncertainty raises fundamentally new issues, particularly concerning its impact on the topology of slow manifolds, and means to represent and quantify associated variability. To address these challenges, this project uses polynomial chaos (PC) methods to reformulate uncertain network models, and to analyze them using CSP in probabilistic terms. Specific objectives include (1) developing effective algorithms that can be used to illuminate fundamental and unexplored connections among model reduction, multiscale behavior, and uncertainty, and (2) demonstrating the performance of these algorithms through applications to model problems.
Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models
NASA Astrophysics Data System (ADS)
Ardani, S.; Kaihatu, J. M.
2012-12-01
Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques
Parameter uncertainty analysis of a biokinetic model of caesium.
Li, W B; Klein, W; Blanchardon, E; Puncher, M; Leggett, R W; Oeh, U; Breustedt, B; Noßke, D; Lopez, M A
2015-01-01
Parameter uncertainties for the biokinetic model of caesium (Cs) developed by Leggett et al. were inventoried and evaluated. The methods of parameter uncertainty analysis were used to assess the uncertainties of model predictions with the assumptions of model parameter uncertainties and distributions. Furthermore, the importance of individual model parameters was assessed by means of sensitivity analysis. The calculated uncertainties of model predictions were compared with human data of Cs measured in blood and in the whole body. It was found that propagating the derived uncertainties in model parameter values reproduced the range of bioassay data observed in human subjects at different times after intake. The maximum ranges, expressed as uncertainty factors (UFs) (defined as a square root of ratio between 97.5th and 2.5th percentiles) of blood clearance, whole-body retention and urinary excretion of Cs predicted at earlier time after intake were, respectively: 1.5, 1.0 and 2.5 at the first day; 1.8, 1.1 and 2.4 at Day 10 and 1.8, 2.0 and 1.8 at Day 100; for the late times (1000 d) after intake, the UFs were increased to 43, 24 and 31, respectively. The model parameters of transfer rates between kidneys and blood, muscle and blood and the rate of transfer from kidneys to urinary bladder content are most influential to the blood clearance and to the whole-body retention of Cs. For the urinary excretion, the parameters of transfer rates from urinary bladder content to urine and from kidneys to urinary bladder content impact mostly. The implication and effect on the estimated equivalent and effective doses of the larger uncertainty of 43 in whole-body retention in the later time, say, after Day 500 will be explored in a successive work in the framework of EURADOS. PMID:24743755
Uncertainty of quantitative microbiological methods of pharmaceutical analysis.
Gunar, O V; Sakhno, N G
2015-12-30
The total uncertainty of quantitative microbiological methods, used in pharmaceutical analysis, consists of several components. The analysis of the most important sources of the quantitative microbiological methods variability demonstrated no effect of culture media and plate-count techniques in the estimation of microbial count while the highly significant effect of other factors (type of microorganism, pharmaceutical product and individual reading and interpreting errors) was established. The most appropriate method of statistical analysis of such data was ANOVA which enabled not only the effect of individual factors to be estimated but also their interactions. Considering all the elements of uncertainty and combining them mathematically the combined relative uncertainty of the test results was estimated both for method of quantitative examination of non-sterile pharmaceuticals and microbial count technique without any product. These data did not exceed 35%, appropriated for a traditional plate count methods. PMID:26456251
Uncertainty Analysis for RELAP5-3D
Aaron J. Pawel; Dr. George L. Mesina
2011-08-01
In its current state, RELAP5-3D is a 'best-estimate' code; it is one of our most reliable programs for modeling what occurs within reactor systems in transients from given initial conditions. This code, however, remains an estimator. A statistical analysis has been performed that begins to lay the foundation for a full uncertainty analysis. By varying the inputs over assumed probability density functions, the output parameters were shown to vary. Using such statistical tools as means, variances, and tolerance intervals, a picture of how uncertain the results are based on the uncertainty of the inputs has been obtained.
NASA Astrophysics Data System (ADS)
Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.
2014-12-01
Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping
Assessment of Uncertainties Related to Seismic Hazard Using Fuzzy Analysis
NASA Astrophysics Data System (ADS)
Jorjiashvili, N.; Yokoi, T.; Javakhishvili, Z.
2013-05-01
Seismic hazard analysis in last few decades has been become very important issue. Recently, new technologies and available data have been improved that helped many scientists to understand where and why earthquakes happen, physics of earthquakes, etc. They have begun to understand the role of uncertainty in Seismic hazard analysis. However, there is still significant problem how to handle existing uncertainty. The same lack of information causes difficulties to quantify uncertainty accurately. Usually attenuation curves are obtained in statistical way: regression analysis. Statistical and probabilistic analysis show overlapped results for the site coefficients. This overlapping takes place not only at the border between two neighboring classes, but also among more than three classes. Although the analysis starts from classifying sites using the geological terms, these site coefficients are not classified at all. In the present study, this problem is solved using Fuzzy set theory. Using membership functions the ambiguities at the border between neighboring classes can be avoided. Fuzzy set theory is performed for southern California by conventional way. In this study standard deviations that show variations between each site class obtained by Fuzzy set theory and classical way are compared. Results on this analysis show that when we have insufficient data for hazard assessment site classification based on Fuzzy set theory shows values of standard deviations less than obtained by classical way which is direct proof of less uncertainty.
Planning for robust reserve networks using uncertainty analysis
Moilanen, A.; Runge, M.C.; Elith, J.; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.
2006-01-01
Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.
Harper, F.T.; Young, M.L.; Miller, L.A.; Hora, S.C.; Lui, C.H.; Goossens, L.H.J.; Cooke, R.M.; Paesler-Sauer, J.; Helton, J.C.
1995-01-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the second of a three-volume document describing the project and contains two appendices describing the rationales for the dispersion and deposition data along with short biographies of the 16 experts who participated in the project.
Harper, F.T.; Young, M.L.; Miller, L.A.; Hora, S.C.; Lui, C.H.; Goossens, L.H.J.; Cooke, R.M.; Paesler-Sauer, J.; Helton, J.C.
1995-01-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The ultimate objective of the joint effort was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. Experts developed their distributions independently. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. To validate the distributions generated for the dispersion code input variables, samples from the distributions and propagated through the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the first of a three-volume document describing the project.
Visual Scanning Hartmann Optical Tester (VSHOT) Uncertainty Analysis (Milestone Report)
Gray, A.; Lewandowski, A.; Wendelin, T.
2010-10-01
In 1997, an uncertainty analysis was conducted of the Video Scanning Hartmann Optical Tester (VSHOT). In 2010, we have completed a new analysis, based primarily on the geometric optics of the system, and it shows sensitivities to various design and operational parameters. We discuss sources of error with measuring devices, instrument calibrations, and operator measurements for a parabolic trough mirror panel test. These help to guide the operator in proper setup, and help end-users to understand the data they are provided. We include both the systematic (bias) and random (precision) errors for VSHOT testing and their contributions to the uncertainty. The contributing factors we considered in this study are: target tilt; target face to laser output distance; instrument vertical offset; laser output angle; distance between the tool and the test piece; camera calibration; and laser scanner. These contributing factors were applied to the calculated slope error, focal length, and test article tilt that are generated by the VSHOT data processing. Results show the estimated 2-sigma uncertainty in slope error for a parabolic trough line scan test to be +/-0.2 milliradians; uncertainty in the focal length is +/- 0.1 mm, and the uncertainty in test article tilt is +/- 0.04 milliradians.
Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.; Harrison, J.D.; Harper, F.T.; Hora, S.C.
1998-04-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on internal dosimetry, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Haskin, F.E.; Harper, F.T.; Goossens, L.H.J.; Kraan, B.C.P.
1997-12-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA early health effects models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on early health effects, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M.; Boardman, J.; Jones, J.A.; Harper, F.T.; Young, M.L.; Hora, S.C.
1997-12-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA deposited material and external dose models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on deposited material and external doses, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Practitioner Representations of Environmental Uncertainty: An Application of Discriminant Analysis.
ERIC Educational Resources Information Center
Acharya, Lalit
Multiple discriminant analysis was used to analyze the structure of a perceived environmental uncertainty variable employed previously in research on public relations roles. Data came from a subset (N=229) of a national sample of public relations practitioners belonging to the Public Relations Society of America, who completed a set of scaled…
UNCERTAINTY ANALYSIS OF RUNOFF ESTIMATES FROM A RUNOFF CONTOUR MAP
The US EPA in cooperation with the USGS conducted an analysis to quantify the uncertainty associated with interpolatinq runoff to specific sites using a runoff contour map. e interpolated runoff to 93 gaged watersheds from a runoff contour map using 1) hand interpolation to the w...
MOUSE: A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM OPERATIONAL MANUAL (DISKETTE)
Environmental engineering calculations involving uncertainties; either in the model itself or in the data, are far beyond the capabilities of conventional analysis for any but the simplest of models. There exist a number of general-purpose computer simulation languages, using Mon...
An educational model for ensemble streamflow simulation and uncertainty analysis
NASA Astrophysics Data System (ADS)
AghaKouchak, A.; Nakhjiri, N.; Habib, E.
2013-02-01
This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation) are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI) and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.
The effects of uncertainty on the analysis of atmospheric deposition
Bloyd, C.N. ); Small, M.J.; Henrion, M.; Rubin, E.S. )
1988-01-01
Research efforts on the problem of acid ran are directed at improving current scientific understanding in critical areas, including sources of precursor emissions, the transport and transformation of pollutants in the atmosphere, the deposition of acidic species, and the chemical and biological effects of acid deposition on aquatic systems, materials, forests, crops and human health. The general goal of these research efforts is to characterize the current situation and to develop analytical models which can be used to predict the response of various systems to changes in critical parameters. This paper describes a framework which enables one to characterize uncertainty at each major stage of the modeling process. Following a general presentation of the modeling framework, a description is given of the methods chosen to characterize uncertainty for each major step. Analysis is then performed to illustrate the effects of uncertainty on future lake acidification in the Adirondacks Park area of upstate New York.
Uncertainty analysis on photogrammetry-derived national shoreline
NASA Astrophysics Data System (ADS)
Yao, Fang
Photogrammetric shoreline mapping remains the primary method for mapping the national shoreline used by the National Geodetic Survey (NGS) in the National Oceanic and Atmospheric Administration (NOAA). To date, NGS has not conducted a statistical analysis on the photograrnmetry-derived shoreline uncertainty. The aim of this thesis is to develop and test a rigorous total propagated uncertainty (TPU) model for shoreline compiled from both tide-coordinated and non-tide-coordinated aerial imagery using photogrammetric methods. Survey imagery collected over a study site in northeast Maine was used to test the TPU model. The TPU model developed in this thesis can easily be extended to other areas and may facilitate estimation of uncertainty in inundation models and marsh migration models.
A convolution integral approach for performance assessments with uncertainty analysis
Dawoud, E.; Miller, L.F.
1999-09-01
Performance assessments that include uncertainty analyses and risk assessments are typically not obtained for time-dependent releases of radioactive contaminants to the geosphere when a series of sequentially coupled transport models is required for determining results. This is due, in part, to the geophysical complexity of the site, and to the numerical complexity of the fate and transport models. The lack of a practical tool for linking the transport models in a fashion that facilitates uncertainty analysis is another reason for not performing uncertainty analyses in these studies. The multiconvolution integral (MCI) approach presented herein greatly facilitates the practicality of incorporating uncertainty analyses into performance assessments. In this research an MCI approach is developed, and the decoupling of fate and transport processes into an independent system is described. A conceptual model, extracted from the Inactive Tanks project at the Oak Ridge National Laboratory (ORNL), is used to demonstrate the approach. Numerical models are used for transport of {sup 90}Sr from a disposal facility, WC-1 at ORNL, through the vadose and saturated zones to a downgradient point at Fifth Creek, and an analytical surface water model is used to transport the contaminants to a downstream potential receptor point at White Oak Creek. The probability density functions of the final concentrations obtained by the MCI approach are in excellent agreement with those obtained by a Monte Carlo approach that propagated uncertainties through all submodels for each random sample.
Uncertainty analysis of a SFR core with sodium plenum
Canuti, E.; Ivanov, E.; Tiberi, V.; Pignet, S.
2012-07-01
The new concepts of Sodium-cooled Fast Reactors have to reach the Generation IV safety objectives. In this regard the Sodium Void Effect has to be minimized for the future projects of large-size SFR as well as the uncertainties on it. The Inst. of Radiological Protection and Nuclear Safety (IRSN) as technological support of French public authorities is in charge of safety assessment of operating and under construction reactors, as well as future projects. In order to state about the safety of new SFR designs the IRSN must be able to evaluate core parameters and their uncertainties. In this frame a sensitivity and uncertainty study has been performed to evaluate the impact of nuclear data uncertainty on sodium void effect, for the benchmark model of large SFR BN-800. The benchmark parameters (effective multiplication factor and sodium void effect) have been evaluated using two codes, the deterministic code ERANOS and the Monte Carlo code SCALE, while the S/U analysis has been performed only with SCALE. The results of the these studies point out the most relevant cross section uncertainties that affect the SVE and how efforts should be done in increasing the existing nuclear data accuracies. (authors)
Probabilistic uncertainty analysis of laser/material thermal interactions
NASA Astrophysics Data System (ADS)
Pelaccio, Dennis George
Performance of a system during heat-flux (laser-type) irradiation is of increasing importance to a variety of defense and commercial applications. For laser irradiation of spacecraft components, such as a laser power or propulsion system receiver, predicting with accuracy the moment (time) and type of failure of it is difficult. These difficulties arise from the inherent nonlinear nature of the problem, because surface reradiation heat transport mechanisms come into play as the system is heated. Additionally, there are uncertainties associated with the irradiation source intensity, interaction cross-section and view angle; the property state of the material(s) that are being heated; and the effective emissivity/absorptivity and surface radiation view factor(s). The physical properties of the materials on a spacecraft may also change greatly over time due to exposure to the space environment. To better understand the uncertainties associated with these issues, a study was performed at the University of New Mexico's Institute for Space and Nuclear Power Studies, under U. S. Air Force Phillips Laboratory sponsorship, to develop and apply uncertainty computer model for generic laser heating problems that incorporate probabilistic design (Monte Carlo sampling based) assessment methods. This work discusses in detail: the background associated with the laser irradiation/material thermal interaction process; past work in related technical areas; the research objectives of the study; the technical approach employed; as well as the development and application of the generic one- and two-dimensional laser/material heating uncertainty interaction analysis models. This study successfully demonstrated an efficient uncertainty assessment methodology to assess simple laser irradiation/material thermal heating process problems. Key parameter uncertainties were characterized and ranked for numerous example problem applications, and the influence of various Monte Carlo sampling
Uncertainty Analysis of the Grazing Flow Impedance Tube
NASA Technical Reports Server (NTRS)
Brown, Martha C.; Jones, Michael G.; Watson, Willie R.
2012-01-01
This paper outlines a methodology to identify the measurement uncertainty of NASA Langley s Grazing Flow Impedance Tube (GFIT) over its operating range, and to identify the parameters that most significantly contribute to the acoustic impedance prediction. Two acoustic liners are used for this study. The first is a single-layer, perforate-over-honeycomb liner that is nonlinear with respect to sound pressure level. The second consists of a wire-mesh facesheet and a honeycomb core, and is linear with respect to sound pressure level. These liners allow for evaluation of the effects of measurement uncertainty on impedances educed with linear and nonlinear liners. In general, the measurement uncertainty is observed to be larger for the nonlinear liners, with the largest uncertainty occurring near anti-resonance. A sensitivity analysis of the aerodynamic parameters (Mach number, static temperature, and static pressure) used in the impedance eduction process is also conducted using a Monte-Carlo approach. This sensitivity analysis demonstrates that the impedance eduction process is virtually insensitive to each of these parameters.
Uncertainty analysis for regional-scale reserve selection.
Moilanen, Atte; Wintle, Brendan A; Elith, Jane; Burgman, Mark
2006-12-01
Methods for reserve selection and conservation planning often ignore uncertainty. For example, presence-absence observations and predictions of habitat models are used as inputs but commonly assumed to be without error. We applied information-gap decision theory to develop uncertainty analysis methods for reserve selection. Our proposed method seeks a solution that is robust in achieving a given conservation target, despite uncertainty in the data. We maximized robustness in reserve selection through a novel method, "distribution discounting," in which the site- and species-specific measure of conservation value (related to species-specific occupancy probabilities) was penalized by an error measure (in our study, related to accuracy of statistical prediction). Because distribution discounting can be implemented as a modification of input files, it is a computationally efficient solution for implementing uncertainty analysis into reserve selection. Thus, the method is particularly useful for high-dimensional decision problems characteristic of regional conservation assessment. We implemented distribution discounting in the zonation reserve-selection algorithm that produces a hierarchy of conservation priorities throughout the landscape. We applied it to reserve selection for seven priority fauna in a landscape in New South Wales, Australia. The distribution discounting method can be easily adapted for use with different kinds of data (e.g., probability of occurrence or abundance) and different landscape descriptions (grid or patch based) and incorporated into other reserve-selection algorithms and software. PMID:17181804
Treatment of uncertainties in the IPCC: a philosophical analysis
NASA Astrophysics Data System (ADS)
Jebeile, J.; Drouet, I.
2014-12-01
The IPCC produces scientific reports out of findings on climate and climate change. Because the findings are uncertain in many respects, the production of reports requires aggregating assessments of uncertainties of different kinds. This difficult task is currently regulated by the Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. The note recommends that two metrics—i.e. confidence and likelihood— be used for communicating the degree of certainty in findings. Confidence is expressed qualitatively "based on the type, amount, quality, and consistency of evidence […] and the degree of agreement", while likelihood is expressed probabilistically "based on statistical analysis of observations or model results, or expert judgment". Therefore, depending on the evidence evaluated, authors have the choice to present either an assigned level of confidence or a quantified measure of likelihood. But aggregating assessments of uncertainties of these two different kinds express distinct and conflicting methodologies. So the question arises whether the treatment of uncertainties in the IPCC is rationally justified. In order to answer the question, it is worth comparing the IPCC procedures with the formal normative theories of epistemic rationality which have been developed by philosophers. These theories—which include contributions to the philosophy of probability and to bayesian probabilistic confirmation theory—are relevant for our purpose because they are commonly used to assess the rationality of common collective jugement formation based on uncertain knowledge. In this paper we make the comparison and pursue the following objectives: i/we determine whether the IPCC confidence and likelihood can be compared with the notions of uncertainty targeted by or underlying the formal normative theories of epistemic rationality; ii/we investigate whether the formal normative theories of epistemic rationality justify
UNCERTAINTY ANALYSIS FOR THE TECHA RIVER DOSIMETRY SYSTEM
Napier, Bruce A.; Degteva, M. O.; Shagina, N. B.; Anspaugh, L. R.
2013-04-01
Uncertainties in the doses estimated for the members of the Techa River Cohort (TRC) are being estimated with a two-dimensional Monte Carlo approach. In order to provide more accurate and precise estimates of individual dose (and thus more precise estimates of radiation risk) for the members of the TRC, a new dosimetric calculation system, the Techa River Dosimetry System-2009 (TRDS-2009) has been prepared. The deterministic version of the improved dosimetry system TRDS-2009D was basically completed in April 2009. Recent developments in evaluation of dose-response models in light of uncertain dose have highlighted the importance of different types of uncertainties in the development of individual dose estimates. These include uncertain parameters that may be either shared (common to some or all individuals) or unshared (a unique value for each person whose dose is to be estimated) within the dosimetric cohort. The nature of the type of uncertainty may be aleatory (random variability of true values due to stochastic processes) or epistemic (due to lack of complete knowledge about a unique quantity). Finally, there is a need to identify whether the structure of the errors is either related to measurement (the estimate differs from the true value by an error that is stochastically independent of the true value; frequently called classical uncertainty) or related to grouping (the true value varies from the estimate by an error that is random and is independent of the estimate; frequently called Berkson uncertainty). An approach has been developed that identifies the nature of the various input parameters and calculational methods incorporated in the Techa River Dosimetry System (based on the TRDS-2009D implementation), and a stochastic calculation model has been prepared to estimate the uncertainties in the dose estimates. This article reviews the concepts of uncertainty analysis, the equations, and input parameters, and then identifies the authors’ interpretations
Compositional Analysis of Lignocellulosic Feedstocks. 2. Method Uncertainties
2010-01-01
The most common procedures for characterizing the chemical components of lignocellulosic feedstocks use a two-stage sulfuric acid hydrolysis to fractionate biomass for gravimetric and instrumental analyses. The uncertainty (i.e., dispersion of values from repeated measurement) in the primary data is of general interest to those with technical or financial interests in biomass conversion technology. The composition of a homogenized corn stover feedstock (154 replicate samples in 13 batches, by 7 analysts in 2 laboratories) was measured along with a National Institute of Standards and Technology (NIST) reference sugar cane bagasse, as a control, using this laboratory's suite of laboratory analytical procedures (LAPs). The uncertainty was evaluated by the statistical analysis of these data and is reported as the standard deviation of each component measurement. Censored and uncensored versions of these data sets are reported, as evidence was found for intermittent instrumental and equipment problems. The censored data are believed to represent the “best case” results of these analyses, whereas the uncensored data show how small method changes can strongly affect the uncertainties of these empirical methods. Relative standard deviations (RSD) of 1−3% are reported for glucan, xylan, lignin, extractives, and total component closure with the other minor components showing 4−10% RSD. The standard deviations seen with the corn stover and NIST bagasse materials were similar, which suggests that the uncertainties reported here are due more to the analytical method used than to the specific feedstock type being analyzed. PMID:20669952
INTEGRATION OF SYSTEM COMPONENTS AND UNCERTAINTY ANALYSIS - HANFORD EXAMPLES
WOOD MI
2009-07-09
{sm_bullet} Deterministic 'One Off' analyses as basis for evaluating sensitivity and uncertainty relative to reference case {sm_bullet} Spatial coverage identical to reference case {sm_bullet} Two types of analysis assumptions - Minimax parameter values around reference case conditions - 'What If' cases that change reference case condition and associated parameter values {sm_bullet} No conclusions about likelihood of estimated result other than' qualitative expectation that actual outcome should tend toward reference case estimate
Uncertainty analysis of penicillin V production using Monte Carlo simulation.
Biwer, Arno; Griffith, Steve; Cooney, Charles
2005-04-20
Uncertainty and variability affect economic and environmental performance in the production of biotechnology and pharmaceutical products. However, commercial process simulation software typically provides analysis that assumes deterministic rather than stochastic process parameters and thus is not capable of dealing with the complexities created by variance that arise in the decision-making process. Using the production of penicillin V as a case study, this article shows how uncertainty can be quantified and evaluated. The first step is construction of a process model, as well as analysis of its cost structure and environmental impact. The second step is identification of uncertain variables and determination of their probability distributions based on available process and literature data. Finally, Monte Carlo simulations are run to see how these uncertainties propagate through the model and affect key economic and environmental outcomes. Thus, the overall variation of these objective functions are quantified, the technical, supply chain, and market parameters that contribute most to the existing variance are identified and the differences between economic and ecological evaluation are analyzed. In our case study analysis, we show that final penicillin and biomass concentrations in the fermenter have the highest contribution to variance for both unit production cost and environmental impact. The penicillin selling price dominates return on investment variance as well as the variance for other revenue-dependent parameters. PMID:15742389
Uncertainty analysis in WWTP model applications: a critical discussion using an example from design.
Sin, Gürkan; Gernaey, Krist V; Neumann, Marc B; van Loosdrecht, Mark C M; Gujer, Willi
2009-06-01
This study focuses on uncertainty analysis of WWTP models and analyzes the issue of framing and how it affects the interpretation of uncertainty analysis results. As a case study, the prediction of uncertainty involved in model-based design of a wastewater treatment plant is studied. The Monte Carlo procedure is used for uncertainty estimation, for which the input uncertainty is quantified through expert elicitation and the sampling is performed using the Latin hypercube method. Three scenarios from engineering practice are selected to examine the issue of framing: (1) uncertainty due to stoichiometric, biokinetic and influent parameters; (2) uncertainty due to hydraulic behaviour of the plant and mass transfer parameters; (3) uncertainty due to the combination of (1) and (2). The results demonstrate that depending on the way the uncertainty analysis is framed, the estimated uncertainty of design performance criteria differs significantly. The implication for the practical applications of uncertainty analysis in the wastewater industry is profound: (i) as the uncertainty analysis results are specific to the framing used, the results must be interpreted within the context of that framing; and (ii) the framing must be crafted according to the particular purpose of uncertainty analysis/model application. Finally, it needs to be emphasised that uncertainty analysis is no doubt a powerful tool for model-based design among others, however clear guidelines for good uncertainty analysis in wastewater engineering practice are needed. PMID:19447462
Geoengineering to Avoid Overshoot: An Analysis of Uncertainty
NASA Astrophysics Data System (ADS)
Tanaka, Katsumasa; Cho, Cheolhung; Krey, Volker; Patt, Anthony; Rafaj, Peter; Rao-Skirbekk, Shilpa; Wagner, Fabian
2010-05-01
., 2009) is employed to calculate climate responses including associated uncertainty and to estimate geoengineering profiles to cap the warming at 2°C since preindustrial. The inversion setup for the model ACC2 is used to estimate the uncertain parameters (e.g. climate sensitivity) against associated historical observations (e.g. global-mean surface air temperature). Our preliminary results show that under climate and scenario uncertainties, a geoengineering intervention to avoid an overshoot would be with medium intensity in the latter half of this century (≈ 1 Mt. Pinatubo eruption every 4 years in terms of stratospheric sulfur injections). The start year of geoengineering intervention does not significantly influence the long-term geoengineering profile. However, a geoengineering intervention of the medium intensity could bring about substantial environmental side effects such as the destruction of stratospheric ozone. Our results point to the necessity to pursue persistently mainstream mitigation efforts. 2) Pollution Abatement and Geoengineering The second study examines the potential of geoengineering combined with air clean policy. A drastic air pollution abatement might result in an abrupt warming because it would suddenly remove the tropospheric aerosols which partly offset the background global warming (e.g. Andreae et al, 2005, Raddatz and Tanaka, 2010). This study investigates the magnitude of unrealized warming under a range of policy assumptions and associated uncertainties. Then the profile of geoengineering is estimated to suppress the warming that would be accompanied by clean air policy. This study is the first attempt to explore uncertainty in the warming caused by clean air policy - Kloster et al. (2009), which assess regional changes in climate and hydrological cycle, has not however included associated uncertainties in the analysis. A variety of policy assumptions will be devised to represent various degrees of air pollution abatement. These
Final Report. Analysis and Reduction of Complex Networks Under Uncertainty
Marzouk, Youssef M.; Coles, T.; Spantini, A.; Tosatto, L.
2013-09-30
The project was a collaborative effort among MIT, Sandia National Laboratories (local PI Dr. Habib Najm), the University of Southern California (local PI Prof. Roger Ghanem), and The Johns Hopkins University (local PI Prof. Omar Knio, now at Duke University). Our focus was the analysis and reduction of large-scale dynamical systems emerging from networks of interacting components. Such networks underlie myriad natural and engineered systems. Examples important to DOE include chemical models of energy conversion processes, and elements of national infrastructure—e.g., electric power grids. Time scales in chemical systems span orders of magnitude, while infrastructure networks feature both local and long-distance connectivity, with associated clusters of time scales. These systems also blend continuous and discrete behavior; examples include saturation phenomena in surface chemistry and catalysis, and switching in electrical networks. Reducing size and stiffness is essential to tractable and predictive simulation of these systems. Computational singular perturbation (CSP) has been effectively used to identify and decouple dynamics at disparate time scales in chemical systems, allowing reduction of model complexity and stiffness. In realistic settings, however, model reduction must contend with uncertainties, which are often greatest in large-scale systems most in need of reduction. Uncertainty is not limited to parameters; one must also address structural uncertainties—e.g., whether a link is present in a network—and the impact of random perturbations, e.g., fluctuating loads or sources. Research under this project developed new methods for the analysis and reduction of complex multiscale networks under uncertainty, by combining computational singular perturbation (CSP) with probabilistic uncertainty quantification. CSP yields asymptotic approximations of reduceddimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing
UCNA Systematic Uncertainties: Developments in Analysis and Method
NASA Astrophysics Data System (ADS)
Zeck, Bryan
2012-10-01
The UCNA experiment is an effort to measure the beta-decay asymmetry parameter A of the correlation between the electron momentum and the neutron spin, using bottled polarized ultracold neutrons in a homogenous 1 T magnetic field. Continued improvements in both analysis and method are helping to push the measurement uncertainty to the limits of the current statistical sensitivity (less than 0.4%). The implementation of thinner decay trap windows will be discussed, as will the use of a tagged beta particle calibration source to measure angle-dependent scattering effects and energy loss. Additionally, improvements in position reconstruction and polarization measurements using a new shutter system will be introduced. A full accounting of the current systematic uncertainties will be given.
Bayesian analysis of input uncertainty in hydrological modeling: 2. Application
NASA Astrophysics Data System (ADS)
Kavetski, Dmitri; Kuczera, George; Franks, Stewart W.
2006-03-01
The Bayesian total error analysis (BATEA) methodology directly addresses both input and output errors in hydrological modeling, requiring the modeler to make explicit, rather than implicit, assumptions about the likely extent of data uncertainty. This study considers a BATEA assessment of two North American catchments: (1) French Broad River and (2) Potomac basins. It assesses the performance of the conceptual Variable Infiltration Capacity (VIC) model with and without accounting for input (precipitation) uncertainty. The results show the considerable effects of precipitation errors on the predicted hydrographs (especially the prediction limits) and on the calibrated parameters. In addition, the performance of BATEA in the presence of severe model errors is analyzed. While BATEA allows a very direct treatment of input uncertainty and yields some limited insight into model errors, it requires the specification of valid error models, which are currently poorly understood and require further work. Moreover, it leads to computationally challenging highly dimensional problems. For some types of models, including the VIC implemented using robust numerical methods, the computational cost of BATEA can be reduced using Newton-type methods.
Global land cover mapping: a review and uncertainty analysis
Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu
2014-01-01
Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.
The Uncertainty in the Local Seismic Response Analysis
Pasculli, A.; Pugliese, A.; Romeo, R. W.; Sano, T.
2008-07-08
In the present paper is shown the influence on the local seismic response analysis exerted by considering dispersion and uncertainty in the seismic input as well as in the dynamic properties of soils. In a first attempt a 1D numerical model is developed accounting for both the aleatory nature of the input motion and the stochastic variability of the dynamic properties of soils. The seismic input is introduced in a non-conventional way through a power spectral density, for which an elastic response spectrum, derived--for instance--by a conventional seismic hazard analysis, is required with an appropriate level of reliability. The uncertainty in the geotechnical properties of soils are instead investigated through a well known simulation technique (Monte Carlo method) for the construction of statistical ensembles. The result of a conventional local seismic response analysis given by a deterministic elastic response spectrum is replaced, in our approach, by a set of statistical elastic response spectra, each one characterized by an appropriate level of probability to be reached or exceeded. The analyses have been carried out for a well documented real case-study. Lastly, we anticipate a 2D numerical analysis to investigate also the spatial variability of soil's properties.
Pazó, Jose A.; Granada, Enrique; Saavedra, Ángeles; Eguía, Pablo; Collazo, Joaquín
2010-01-01
The objective of this study was to develop a methodology for the determination of the maximum sampling error and confidence intervals of thermal properties obtained from thermogravimetric analysis (TG), including moisture, volatile matter, fixed carbon and ash content. The sampling procedure of the TG analysis was of particular interest and was conducted with care. The results of the present study were compared to those of a prompt analysis, and a correlation between the mean values and maximum sampling errors of the methods were not observed. In general, low and acceptable levels of uncertainty and error were obtained, demonstrating that the properties evaluated by TG analysis were representative of the overall fuel composition. The accurate determination of the thermal properties of biomass with precise confidence intervals is of particular interest in energetic biomass applications. PMID:20717532
MOUSE (Modular Oriented Uncertainty SystEm) deals with the problem of uncertainties in models that consist of one or more algebraic equations. It was especially designed for use by those with little or no knowledge of computer languages or programming. It is compact (and thus can...
Results of a 24-inch Hybrid Motor Performance Uncertainty Analysis
NASA Technical Reports Server (NTRS)
Sims, Joseph D.; Coleman, Hugh W.
1998-01-01
The subscale (11 and 24-inch) hybrid motors at the Marshall Space Flight Center (MSFC) have been used as versatile and cost effective testbeds for developing new technology. Comparisons between motor configuration, ignition systems, feed systems, fuel formulations, and nozzle materials have been carried out without detailed consideration as to haw "good" the motor performance data were. For the 250,000 lb/thrust motor developed by the Hybrid Propulsion Demonstration Program consortium, this shortcoming is particularly risky because motor performance will likely be used as put of a set of downselect criteria to choose between competing ignition and feed systems under development. This analysis directly addresses that shortcoming by applying uncertainty analysis techniques to the experimental determination of the characteristic velocity, theoretical characteristic velocity, and characteristic velocity efficiency for a 24-inch motor firing. With the adoption of fuel-lined headends, flow restriction, and aft mixing chambers, state of the an 24-inch hybrid motors have become very efficient However, impossibly high combustion efficiencies (some computed as high as 108%) have been measured in some tests with 11-inch motors. This analysis has given new insight into explaining how these efficiencies were measured to be so high, and into which experimental measurements contribute the most to the overall uncertainty.
Productivity of Northern Eurasian forests: Analysis of uncertainties
NASA Astrophysics Data System (ADS)
Shvidenko, Anatoly; Schepaschenko, Dmitry; McCallum, Ian
2010-05-01
Indicators of biological productivity of forests (live and dead biomass, net primary production, net and gross growth) are crucial for both assessment of the impacts of terrestrial ecosystems on major biogeochemical cycles and practice of sustainable forest management. However, different information and the diversity of methods used in the assessments of forests productivity cause substantial variation in reported estimates. The paper contains a systems analysis of the existing methods, their uncertainties, and a description of available information. With respect to Northern Eurasian forests, the major reasons for uncertainties could be categorized as following: (1) significant biases that are inherent in a number of important sources of available information (e.g., forest inventory data, results of measurements of some indicators in situ); (2) inadequacy and oversimplification of models of different types (empirical aggregations, process-based models); (3) lack of data for some regions; and (4) upscaling procedure of 'point' observations. Based on as comprehensive as possible adherence to the principles of systems analysis, we made an attempt to provide a reanalysis of indicators of forests productivity of Russia aiming at obtaining the results for which uncertainties could be estimated in a reliable and transparent way. Within a landscape-ecosystem approach it has required (1) development of an expert system for refinement of initial data including elimination of recognized biases; (2) delineation of ecological regions based on gradients of major indicators of productivity; (3) transition to multidimensional models (e.g., for calculation of spatially distributed biomass expansion factors); (4) use of process-based elements in empirical models; and (5) development of some approaches which presumably do not have recognized biases. However, taking into account the fuzzy character of the problem, the above approach (as well as any other individually used method) is
Additional challenges for uncertainty analysis in river engineering
NASA Astrophysics Data System (ADS)
Berends, Koen; Warmink, Jord; Hulscher, Suzanne
2016-04-01
the proposed intervention. The implicit assumption underlying such analysis is that both models are commensurable. We hypothesize that they are commensurable only to a certain extent. In an idealised study we have demonstrated that prediction performance loss should be expected with increasingly large engineering works. When accounting for parametric uncertainty of floodplain roughness in model identification, we see uncertainty bounds for predicted effects of interventions increase with increasing intervention scale. Calibration of these types of models therefore seems to have a shelf-life, beyond which calibration does not longer improves prediction. Therefore a qualification scheme for model use is required that can be linked to model validity. In this study, we characterize model use along three dimensions: extrapolation (using the model with different external drivers), extension (using the model for different output or indicators) and modification (using modified models). Such use of models is expected to have implications for the applicability of surrogating modelling for efficient uncertainty analysis as well, which is recommended for future research. Warmink, J. J.; Straatsma, M. W.; Huthoff, F.; Booij, M. J. & Hulscher, S. J. M. H. 2013. Uncertainty of design water levels due to combined bed form and vegetation roughness in the Dutch river Waal. Journal of Flood Risk Management 6, 302-318 . DOI: 10.1111/jfr3.12014
Foundational methods for model verification and uncertainty analysis (Invited)
NASA Astrophysics Data System (ADS)
Jakeman, A. J.; Croke, B. F.; Guillaume, J. H.; Jakeman, J. D.; Shin, M.
2013-12-01
Before embarking on formal methods of uncertainty analysis that may entail unnecessarily restrictive assumptions and sophisticated treatment, prudence dictates exploring one's data, model candidates and applicable objective functions with a mixture of methods as a first step. It seems that there are several foundational methods that warrant more attention in practice and that there is scope for the development of new ones. Ensuing results from a selection of foundational methods may well inform the choice of formal methods and assumptions, or suffice in themselves as an effective appreciation of uncertainty. Through the case of four lumped rainfall-runoff models of varying complexity from several watersheds we illustrate that there are valuable methods, many of them already in open source software, others we have recently developed, which can be invoked to yield valuable insights into model veracity and uncertainty. We show results of using methods of global sensitivity analysis that help: determine whether insensitive parameters impact on predictions and therefore cannot be fixed; and identify which combinations of objective function, dataset and model structure allow insensitive parameters to be estimated. We apply response surface and polynomial chaos methods to yield knowledge of the models' response surfaces and parameter interactions, thereby informing model redesign. A new approach to model structure discrimination is presented based on Pareto methods and cross-validation. It reveals which model structures are acceptable in the sense that they are non-dominated by other structures across calibration and validation periods and across catchments according to specified performance criteria. Finally we present and demonstrate a falsification approach that shows the value of examining scenarios of model structures and parameters to identify any change that might have a specified effect on a prediction.
Statistical analysis of the uncertainty related to flood hazard appraisal
NASA Astrophysics Data System (ADS)
Notaro, Vincenza; Freni, Gabriele
2015-12-01
The estimation of flood hazard frequency statistics for an urban catchment is of great interest in practice. It provides the evaluation of potential flood risk and related damage and supports decision making for flood risk management. Flood risk is usually defined as function of the probability, that a system deficiency can cause flooding (hazard), and the expected damage, due to the flooding magnitude (damage), taking into account both the exposure and the vulnerability of the goods at risk. The expected flood damage can be evaluated by an a priori estimation of potential damage caused by flooding or by interpolating real damage data. With regard to flood hazard appraisal several procedures propose to identify some hazard indicator (HI) such as flood depth or the combination of flood depth and velocity and to assess the flood hazard corresponding to the analyzed area comparing the HI variables with user-defined threshold values or curves (penalty curves or matrixes). However, flooding data are usually unavailable or piecemeal allowing for carrying out a reliable flood hazard analysis, therefore hazard analysis is often performed by means of mathematical simulations aimed at evaluating water levels and flow velocities over catchment surface. As results a great part of the uncertainties intrinsic to flood risk appraisal can be related to the hazard evaluation due to the uncertainty inherent to modeling results and to the subjectivity of the user defined hazard thresholds applied to link flood depth to a hazard level. In the present work, a statistical methodology was proposed for evaluating and reducing the uncertainties connected with hazard level estimation. The methodology has been applied to a real urban watershed as case study.
Reducing spatial uncertainty in climatic maps through geostatistical analysis
NASA Astrophysics Data System (ADS)
Pesquer, Lluís; Ninyerola, Miquel; Pons, Xavier
2014-05-01
Climatic maps from meteorological stations and geographical co-variables can be obtained through correlative models (Ninyerola et al., 2000)*. Nevertheless, the spatial uncertainty of the resulting maps could be reduced. The present work is a new stage over those approaches aiming to study how to obtain better results while characterizing spatial uncertainty. The study area is Catalonia (32000 km2), a region with highly variable relief (0 to 3143 m). We have used 217 stations (321 to 1244 mm) to model the annual precipitation in two steps: 1/ multiple regression using geographical variables (elevation, distance to the coast, latitude, etc) and 2/ refinement of the results by adding the spatial interpolation of the regression residuals with inverse distance weighting (IDW), regularized splines with tension (SPT) or ordinary kriging (OK). Spatial uncertainty analysis is based on an independent subsample (test set), randomly selected in previous works. The main contribution of this work is the analysis of this test set as well as the search for an optimal process of division (split) of the stations in two sets, one used to perform the multiple regression and residuals interpolation (fit set), and another used to compute the quality (test set); optimal division should reduce spatial uncertainty and improve the overall quality. Two methods have been evaluated against classical methods: (random selection RS and leave-one-out cross-validation LOOCV): selection by Euclidian 2D-distance, and selection by anisotropic 2D-distance combined with a 3D-contribution (suitable weighted) from the most representative independent variable. Both methods define a minimum threshold distance, obtained by variogram analysis, between samples. Main preliminary results for LOOCV, RS (average from 10 executions), Euclidian criterion (EU), and for anisotropic criterion (with 1.1 value, UTMY coordinate has a bit more weight than UTMX) combined with 3D criteria (A3D) (1000 factor for elevation
Geoengineering to Avoid Overshoot: An Analysis of Uncertainty
NASA Astrophysics Data System (ADS)
Tanaka, Katsumasa; Cho, Cheolhung; Krey, Volker; Patt, Anthony; Rafaj, Peter; Rao-Skirbekk, Shilpa; Wagner, Fabian
2010-05-01
., 2009) is employed to calculate climate responses including associated uncertainty and to estimate geoengineering profiles to cap the warming at 2°C since preindustrial. The inversion setup for the model ACC2 is used to estimate the uncertain parameters (e.g. climate sensitivity) against associated historical observations (e.g. global-mean surface air temperature). Our preliminary results show that under climate and scenario uncertainties, a geoengineering intervention to avoid an overshoot would be with medium intensity in the latter half of this century (≈ 1 Mt. Pinatubo eruption every 4 years in terms of stratospheric sulfur injections). The start year of geoengineering intervention does not significantly influence the long-term geoengineering profile. However, a geoengineering intervention of the medium intensity could bring about substantial environmental side effects such as the destruction of stratospheric ozone. Our results point to the necessity to pursue persistently mainstream mitigation efforts. 2) Pollution Abatement and Geoengineering The second study examines the potential of geoengineering combined with air clean policy. A drastic air pollution abatement might result in an abrupt warming because it would suddenly remove the tropospheric aerosols which partly offset the background global warming (e.g. Andreae et al, 2005, Raddatz and Tanaka, 2010). This study investigates the magnitude of unrealized warming under a range of policy assumptions and associated uncertainties. Then the profile of geoengineering is estimated to suppress the warming that would be accompanied by clean air policy. This study is the first attempt to explore uncertainty in the warming caused by clean air policy - Kloster et al. (2009), which assess regional changes in climate and hydrological cycle, has not however included associated uncertainties in the analysis. A variety of policy assumptions will be devised to represent various degrees of air pollution abatement. These
Dynamic wake prediction and visualization with uncertainty analysis
NASA Technical Reports Server (NTRS)
Holforty, Wendy L. (Inventor); Powell, J. David (Inventor)
2005-01-01
A dynamic wake avoidance system utilizes aircraft and atmospheric parameters readily available in flight to model and predict airborne wake vortices in real time. A novel combination of algorithms allows for a relatively simple yet robust wake model to be constructed based on information extracted from a broadcast. The system predicts the location and movement of the wake based on the nominal wake model and correspondingly performs an uncertainty analysis on the wake model to determine a wake hazard zone (no fly zone), which comprises a plurality of wake planes, each moving independently from another. The system selectively adjusts dimensions of each wake plane to minimize spatial and temporal uncertainty, thereby ensuring that the actual wake is within the wake hazard zone. The predicted wake hazard zone is communicated in real time directly to a user via a realistic visual representation. In an example, the wake hazard zone is visualized on a 3-D flight deck display to enable a pilot to visualize or see a neighboring aircraft as well as its wake. The system substantially enhances the pilot's situational awareness and allows for a further safe decrease in spacing, which could alleviate airport and airspace congestion.
Selection of Representative Models for Decision Analysis Under Uncertainty
NASA Astrophysics Data System (ADS)
Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.
2016-03-01
The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.
NASA Technical Reports Server (NTRS)
Sankararaman, Shankar
2016-01-01
This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.
Brown, J.; Goossens, L.H.J.; Kraan, B.C.P.
1997-06-01
This volume is the first of a two-volume document that summarizes a joint project conducted by the US Nuclear Regulatory Commission and the European Commission to assess uncertainties in the MACCS and COSYMA probabilistic accident consequence codes. These codes were developed primarily for estimating the risks presented by nuclear reactors based on postulated frequencies and magnitudes of potential accidents. This document reports on an ongoing project to assess uncertainty in the MACCS and COSYMA calculations for the offsite consequences of radionuclide releases by hypothetical nuclear power plant accidents. A panel of sixteen experts was formed to compile credible and traceable uncertainty distributions for food chain variables that affect calculations of offsite consequences. The expert judgment elicitation procedure and its outcomes are described in these volumes. Other panels were formed to consider uncertainty in other aspects of the codes. Their results are described in companion reports. Volume 1 contains background information and a complete description of the joint consequence uncertainty study. Volume 2 contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures for both panels, (3) the rationales and results for the panels on soil and plant transfer and animal transfer, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Handling of Uncertainty in Analysis of Chest Pain
Hudson, D. L.; Cohen, M. E.
1990-01-01
Over the last 15 years, a large research effort has been devoted to the development of rule-based expert systems for medical decision making. While many of these systems provide competent medical advice in their domains of expertise, few are employed in actual clinical use for a variety of reasons. One of the challenges confronting these systems is the task of dealing with uncertain information. Although much attention has been devoted to this problem, no completely satisfactory solution has been found. A strategy for coping with uncertainty based on a modified production rule format as well as an automated technique for extracting relative degrees of importance of contributing factors is discussed as applied to an existing medical expert system for the analysis of chest pain.
Uncertainty analysis of planar laser-induced fluorescence measurements
NASA Astrophysics Data System (ADS)
Tavoularis, Stavros; Vanderwel, Christina
2014-11-01
We present a thorough analysis of the uncertainty of the planar laser-induced fluorescence (PLIF) method. We consider the measurement of concentration maps in cross-sections parallel to and normal to the axis of a slender plume containing Rhodamine 6G as a passive scalar tracer and transported by a turbulent shear flow. In particular, we identify two previously unexplored sources of error contributed by non-uniformity of the concentration across the laser sheet and by secondary fluorescence. We propose new methods to evaluate and correct for these sources of error and demonstrate that the corrected concentration measurements accurately determined the injected dye mass flow rate of the plume in the far field. Supported by NSERC.
Impact of Model Uncertainties on Quantitative Analysis of FUV Auroral Images: Peak Production Height
NASA Technical Reports Server (NTRS)
Germany, G. A.; Lummerzheim, D.; Parks, G. K.; Brittnacher, M. J.; Spann, James F., Jr.; Richards, Phil G.
1999-01-01
We demonstrate that small uncertainties in the modeled height of peak production for FUV emissions can lead to significant uncertainties in the analysis of these sai-ne emissions. In particular, an uncertainty of only 3 km in the peak production height can lead to a 50% uncertainty in the mean auroral energy deduced from the images. This altitude uncertainty is comparable to differences in different auroral deposition models currently used for UVI analysis. Consequently, great care must be taken in quantitative photometric analysis and interpretation of FUV auroral images.
[Parameter uncertainty analysis for urban rainfall runoff modelling].
Huang, Jin-Liang; Lin, Jie; Du, Peng-Fei
2012-07-01
An urban watershed in Xiamen was selected to perform the parameter uncertainty analysis for urban stormwater runoff modeling in terms of identification and sensitivity analysis based on storm water management model (SWMM) using Monte-Carlo sampling and regionalized sensitivity analysis (RSA) algorithm. Results show that Dstore-Imperv, Dstore-Perv and Curve Number (CN) are the identifiable parameters with larger K-S values in hydrological and hydraulic module, and the rank of K-S values in hydrological and hydraulic module is Dstore-Imperv > CN > Dstore-Perv > N-Perv > conductivity > Con-Mann > N-Imperv. With regards to water quality module, the parameters in exponent washoff model including Coefficient and Exponent and the Max. Buildup parameter of saturation buildup model in three land cover types are the identifiable parameters with the larger K-S values. In comparison, the K-S value of rate constant in three landuse/cover types is smaller than that of Max. Buildup, Coefficient and Exponent. PMID:23002595
Improving uncertainty analysis in European Union risk assessment of chemicals.
Verdonck, Frederik A M; Souren, Astrid; van Asselt, Marjolein B A; Van Sprang, Patrick A; Vanrolleghem, Peter A
2007-07-01
Handling uncertainty in curren European Union (EU) risk assessment of new and existing substances is problematic for several reasons. The known or quantifiable sources of uncertainty are mainly considered. Uncertainty is insufficiently, explicitly communicated to risk managers and decision makers but hidden and concealed in risk quotient numbers that appear to be certain and, therefore, create a false sense of certainty and protectiveness. The new EU chemical policy legislation, REACH, is an opportunity to learn from interdisciplinary thinking in order to evolve to smart risk assessment: an assessment in which awareness and openness to uncertainty is used to produce better characterizations and evaluations of risks. In a smart risk assessment context, quantifying uncertainty is not an aim but just a productive means to refine the assessment or to find alternative solutions for the problem at stake. Guidance and examples are given on how to differentiate, assess, and use uncertainty. PMID:17695106
Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh
NASA Astrophysics Data System (ADS)
Mortuza, M. R.; Demissie, Y.; Li, H. Y.
2014-12-01
Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.
Uncertainty Analysis of the Three Pagodas Fault-Source Geometry
NASA Astrophysics Data System (ADS)
Haller, K. M.
2015-12-01
Probabilistic seismic-hazard assessment generally relies on an earthquake catalog (to estimate future seismicity from the locations and rates of past earthquakes) and faults sources (to estimate future seismicity from the known paleoseismic history of surface rupture). The paleoseismic history of potentially active faults in Southeast Asia is addressed at few locations and spans only a few complete recurrence intervals; many faults remain unstudied. Even where the timing of a surface-rupturing earthquakes is known, the extent of rupture may not be well constrained. Therefore, subjective judgment of experts is often used to define the three-dimensional size of future ruptures; limited paleoseismic data can lead to large uncertainties in ground-motion hazard from fault sources due to the preferred models that underlie these judgments. The 300-km-long, strike-slip Three Pagodas fault in western Thailand is possibly one of the most active faults in the country. The fault parallels the plate boundary and may be characterized by a slip rate high enough to result in measurable ground-motion at periods of interest for building design. The known paleoseismic history is limited and likely does not include the largest possible earthquake on the fault. This lack of knowledge begs the question what sizes of earthquakes are expected? Preferred rupture models constrain possible magnitude-frequency distributions, and alternative rupture models can result in different ground-motion hazard near the fault. This analysis includes alternative rupture models for the Three Pagodas fault, a first-level check against gross modeling assumptions to assure the source model is a reasonable reflection of observed data, and resulting ground-motion hazard for each alternative. Inadequate paleoseismic data is an important source of uncertainty that could be compensated for by considering alternative rupture models for poorly known seismic sources.
WHEELER, TIMOTHY A.; WYSS, GREGORY D.; HARPER, FREDERICK T.
2000-11-01
Uncertainty distributions for specific parameters of the Cassini General Purpose Heat Source Radioisotope Thermoelectric Generator (GPHS-RTG) Final Safety Analysis Report consequence risk analysis were revised and updated. The revisions and updates were done for all consequence parameters for which relevant information exists from the joint project on Probabilistic Accident Consequence Uncertainty Analysis by the United States Nuclear Regulatory Commission and the Commission of European Communities.
Boak, D.M.; Painton, L.
1995-12-08
Probabilistic forecasting techniques have been used in many risk assessment and performance assessment applications on radioactive waste disposal projects such as Yucca Mountain and the Waste Isolation Pilot Plant (WIPP). Probabilistic techniques such as Monte Carlo and Latin Hypercube sampling methods are routinely used to treat uncertainties in physical parameters important in simulating radionuclide transport in a coupled geohydrologic system and assessing the ability of that system to comply with regulatory release limits. However, the use of probabilistic techniques in the treatment of uncertainties in the cost and duration of programmatic alternatives on risk and performance assessment projects is less common. Where significant uncertainties exist and where programmatic decisions must be made despite existing uncertainties, probabilistic techniques may yield important insights into decision options, especially when used in a decision analysis framework and when properly balanced with deterministic analyses. For relatively simple evaluations, these types of probabilistic evaluations can be made using personal computer-based software.
Cardoni, Jeffrey N.; Kalinich, Donald A.
2014-02-01
Sandia National Laboratories (SNL) plans to conduct uncertainty analyses (UA) on the Fukushima Daiichi unit (1F1) plant with the MELCOR code. The model to be used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). However, that study only examined a handful of various model inputs and boundary conditions, and the predictions yielded only fair agreement with plant data and current release estimates. The goal of this uncertainty study is to perform a focused evaluation of uncertainty in core melt progression behavior and its effect on key figures-of-merit (e.g., hydrogen production, vessel lower head failure, etc.). In preparation for the SNL Fukushima UA work, a scoping study has been completed to identify important core melt progression parameters for the uncertainty analysis. The study also lays out a preliminary UA methodology.
Incorporating parametric uncertainty into population viability analysis models
McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.
2011-01-01
Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.
Experimental investigations for uncertainty quantification in brake squeal analysis
NASA Astrophysics Data System (ADS)
Renault, A.; Massa, F.; Lallemand, B.; Tison, T.
2016-04-01
The aim of this paper is to improve the correlation between the experimental and the numerical prediction of unstable frequencies for automotive brake systems considering uncertainty. First, an experimental quantification of uncertainty and a discussion analysing the contributions of uncertainty to a numerical squeal simulation are proposed. Frequency and transient simulations are performed considering nominal values of model parameters, determined experimentally. The obtained results are compared with those derived from experimental tests to highlight the limitation of deterministic simulations. The effects of the different kinds of uncertainty detected in working conditions of brake system, the pad boundary condition, the brake system material properties and the pad surface topography are discussed by defining different unstable mode classes. Finally, a correlation between experimental and numerical results considering uncertainty is successfully proposed for an industrial brake system. Results from the different comparisons reveal also a major influence of the pad topography and consequently the contact distribution.
Uncertainty analysis of fission fraction for reactor antineutrino experiments
NASA Astrophysics Data System (ADS)
Ma, X. B.; Lu, F.; Wang, L. Z.; Chen, Y. X.; Zhong, W. L.; An, F. P.
2016-06-01
Reactor simulation is an important source of uncertainties for a reactor neutrino experiment. Therefore, how to evaluate the antineutrino flux uncertainty results from reactor simulation is an important question. In this study, a method of the antineutrino flux uncertainty result from reactor simulation was proposed by considering the correlation coefficient. In order to use this method in the Daya Bay antineutrino experiment, the open source code DRAGON was improved and used for obtaining the fission fraction and correlation coefficient. The average fission fraction between DRAGON and SCIENCE code was compared and the difference was less than 5% for all the four isotopes. The uncertainty of fission fraction was evaluated by comparing simulation atomic density of four main isotopes with Takahama-3 experiment measurement. After that, the uncertainty of the antineutrino flux results from reactor simulation was evaluated as 0.6% per core for Daya Bay antineutrino experiment.
Brown, J.; Goossens, L.H.J.; Kraan, B.C.P.
1997-06-01
This volume is the second of a two-volume document that summarizes a joint project by the US Nuclear Regulatory and the Commission of European Communities to assess uncertainties in the MACCS and COSYMA probabilistic accident consequence codes. These codes were developed primarily for estimating the risks presented by nuclear reactors based on postulated frequencies and magnitudes of potential accidents. This two-volume report, which examines mechanisms and uncertainties of transfer through the food chain, is the first in a series of five such reports. A panel of sixteen experts was formed to compile credible and traceable uncertainty distributions for food chain transfer that affect calculations of offsite radiological consequences. Seven of the experts reported on transfer into the food chain through soil and plants, nine reported on transfer via food products from animals, and two reported on both. The expert judgment elicitation procedure and its outcomes are described in these volumes. This volume contains seven appendices. Appendix A presents a brief discussion of the MAACS and COSYMA model codes. Appendix B is the structure document and elicitation questionnaire for the expert panel on soils and plants. Appendix C presents the rationales and responses of each of the members of the soils and plants expert panel. Appendix D is the structure document and elicitation questionnaire for the expert panel on animal transfer. The rationales and responses of each of the experts on animal transfer are given in Appendix E. Brief biographies of the food chain expert panel members are provided in Appendix F. Aggregated results of expert responses are presented in graph format in Appendix G.
ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING
Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.; Ratzlaff, Pete; Siemiginowska, Aneta E-mail: vkashyap@cfa.harvard.edu E-mail: rpete@head.cfa.harvard.edu
2011-04-20
While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.
A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules
NASA Astrophysics Data System (ADS)
Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.
2012-08-01
Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.
Sensitivity and Uncertainty Analysis to Burn-up Estimates on ADS Using ACAB Code
Cabellos, O; Sanz, J; Rodriguez, A; Gonzalez, E; Embid, M; Alvarez, F; Reyes, S
2005-02-11
Within the scope of the Accelerator Driven System (ADS) concept for nuclear waste management applications, the burnup uncertainty estimates due to uncertainty in the activation cross sections (XSs) are important regarding both the safety and the efficiency of the waste burning process. We have applied both sensitivity analysis and Monte Carlo methodology to actinides burnup calculations in a lead-bismuth cooled subcritical ADS. The sensitivity analysis is used to identify the reaction XSs and the dominant chains that contribute most significantly to the uncertainty. The Monte Carlo methodology gives the burnup uncertainty estimates due to the synergetic/global effect of the complete set of XS uncertainties. These uncertainty estimates are valuable to assess the need of any experimental or systematic reevaluation of some uncertainty XSs for ADS.
Sensitivity and Uncertainty Analysis to Burnup Estimates on ADS using the ACAB Code
Cabellos, O.; Sanz, J.; Rodriguez, A.; Gonzalez, E.; Embid, M.; Alvarez, F.; Reyes, S.
2005-05-24
Within the scope of the Accelerator Driven System (ADS) concept for nuclear waste management applications, the burnup uncertainty estimates due to uncertainty in the activation cross sections (XSs) are important regarding both the safety and the efficiency of the waste burning process. We have applied both sensitivity analysis and Monte Carlo methodology to actinides burnup calculations in a lead-bismuth cooled subcritical ADS. The sensitivity analysis is used to identify the reaction XSs and the dominant chains that contribute most significantly to the uncertainty. The Monte Carlo methodology gives the burnup uncertainty estimates due to the synergetic/global effect of the complete set of XS uncertainties. These uncertainty estimates are valuable to assess the need of any experimental or systematic re-evaluation of some uncertainty XSs for ADS.
Measurement uncertainty sources analysis for parasitic time grating sensors
NASA Astrophysics Data System (ADS)
Yang, Hongtao; Zhou, Jiao; Fan, Bin; Fei, Yetai; Peng, Donglin; Wu, Tianfeng
2016-01-01
The signal quality of traveling wave and the measurement accuracy of parasitic time grating can be improved by optimiz ing its structure. This optimization process can be guided through building the electrical traveling wave equation with respect to the structure and the traveling wave signal generation principle. Based on Ansoft Maxwell simulation, the important electromagnetic parameters and the main uncertainty sources were analyzed and determined respectively. In the simulation parameters such as the excitation signal frequency, the gap width, the relative area of the probe, the coils number, the excitation signal amplitude and the core length were set to different values. It can be seen from the simulation results that excitation signal frequency, the gap width, the relative area between the probe and the rotor are the major factors to influence the angular measuring accuracy of parasitic time grating sensor. Meanwhile, the coils number, the excitation signal amplitude and the core length are the secondary factors. The analysis result can be utilized to optimize the structure of parasitic time grating and correct measurement error.
Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas
Bedford, Tim; Daneshkhah, Alireza
2015-01-01
Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets. PMID:26332240
CASMO5/TSUNAMI-3D spent nuclear fuel reactivity uncertainty analysis
Ferrer, R.; Rhodes, J.; Smith, K.
2012-07-01
The CASMO5 lattice physics code is used in conjunction with the TSUNAMI-3D sequence in ORNL's SCALE 6 code system to estimate the uncertainties in hot-to-cold reactivity changes due to cross-section uncertainty for PWR assemblies at various burnup points. The goal of the analysis is to establish the multiplication factor uncertainty similarity between various fuel assemblies at different conditions in a quantifiable manner and to obtain a bound on the hot-to-cold reactivity uncertainty over the various assembly types and burnup attributed to fundamental cross-section data uncertainty. (authors)
Uncertainty Analysis of NASA Glenn's 8- by 6-Foot Supersonic Wind Tunnel
NASA Technical Reports Server (NTRS)
Stephens, Julia E.; Hubbard, Erin P.; Walter, Joel A.; McElroy, Tyler
2016-01-01
An analysis was performed to determine the measurement uncertainty of the Mach Number of the 8- by 6-foot Supersonic Wind Tunnel at the NASA Glenn Research Center. This paper details the analysis process used, including methods for handling limited data and complicated data correlations. Due to the complexity of the equations used, a Monte Carlo Method was utilized for this uncertainty analysis. A summary of the findings are presented as pertains to understanding what the uncertainties are, how they impact various research tests in the facility, and methods of reducing the uncertainties in the future.
ANALYSIS OF MEASUREMENT UNCERTAINTIES IN THE NULLING TEST FOR AIR LEAKAGE FROM RESIDENTIAL DUCTS.
ANDREWS,J.W.
2001-04-01
An analysis of measurement uncertainties in a recently proposed method of measuring air leakage in residential duct systems has been carried out. The uncertainties in supply and return leakage rates are expressed in terms of the value of the envelope leakage flow coefficient and the uncertainties in measured pressures and air flow rates. Results of the analysis are compared with data published by two research groups.
Analysis and reduction of chemical models under uncertainty.
Oxberry, Geoff; Debusschere, Bert J.; Najm, Habib N.
2008-08-01
While models of combustion processes have been successful in developing engines with improved fuel economy, more costly simulations are required to accurately model pollution chemistry. These simulations will also involve significant parametric uncertainties. Computational singular perturbation (CSP) and polynomial chaos-uncertainty quantification (PC-UQ) can be used to mitigate the additional computational cost of modeling combustion with uncertain parameters. PC-UQ was used to interrogate and analyze the Davis-Skodje model, where the deterministic parameter in the model was replaced with an uncertain parameter. In addition, PC-UQ was combined with CSP to explore how model reduction could be combined with uncertainty quantification to understand how reduced models are affected by parametric uncertainty.
INCORPORATING UNCERTAINTY ANALYSIS INTO INTEGRATED AIR QUALITY PLANNING
The proposed research will develop methods by which air quality planners could formally consider the uncertainty of models that inform control strategy development. We will identify key photochemical model inputs, epidemiological parameters, and other assumptions that most in...
Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models
The task of quantifying the uncertainty in both parameter estimates and model predictions has become more important with the increased use of dynamic computational toxicology models by the EPA. Dynamic toxicological models include physiologically-based pharmacokinetic (PBPK) mode...
Problem Solving Environment for Uncertainty Analysis and Design Exploration
Energy Science and Technology Software Center (ESTSC)
2009-09-02
PSUDE is an software system that is used to study the relationships between the inputs and outputs of general simulation models for the purpose of performaing uncertainty and sensitivity analyses on simulation models.
Problem Solving Environment for Uncertainty Analysis and Design Exploration
Energy Science and Technology Software Center (ESTSC)
2011-10-26
PSUADE is an software system that is used to study the releationships between the inputs and outputs of gerneral simulation models for the purpose of performing uncertainty and sensitivity analyses on simulation models.
Problem Solving Environment for Uncertainty Analysis and Design Exploration
Energy Science and Technology Software Center (ESTSC)
2008-05-31
PSUADE is an software system that is used to study the relationships between the inputs and outputs of general simulation models for the purpose of performing uncertainty and sensitivity analyses on simulation models.
Probabilistic structural analysis to quantify uncertainties associated with turbopump blades
NASA Technical Reports Server (NTRS)
Nagpal, Vinod K.; Rubinstein, Robert; Chamis, Christos C.
1988-01-01
A probabilistic study of turbopump blades has been in progress at NASA Lewis Research Center for over the last two years. The objectives of this study are to evaluate the effects of uncertainties in geometry and material properties on the structural response of the turbopump blades to evaluate the tolerance limits on the design. A methodology based on probabilistic approach was developed to quantify the effects of the random uncertainties. The results indicate that only the variations in geometry have significant effects.
Model parameter uncertainty analysis for an annual field-scale P loss model
NASA Astrophysics Data System (ADS)
Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie
2016-08-01
Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model
Uncertainty Analysis of the Single-Vector Force Balance Calibration System
NASA Technical Reports Server (NTRS)
Parker, Peter A.; Liu, Tianshu
2002-01-01
This paper presents an uncertainty analysis of the Single-Vector Force Balance Calibration System (SVS). This study is focused on the uncertainty involved in setting the independent variables during the calibration experiment. By knowing the uncertainty in the calibration system, the fundamental limits of the calibration accuracy of a particular balance can be determined. A brief description of the SVS mechanical system is provided. A mathematical model is developed to describe the mechanical system elements. A sensitivity analysis of these parameters is carried out through numerical simulations to assess the sensitivity of the total uncertainty to the elemental error sources. These sensitivity coefficients provide valuable information regarding the relative significance of the elemental sources of error. An example calculation of the total uncertainty for a specific balance is provided. Results from this uncertainty analysis are specific to the Single-Vector System, but the approach is broad in nature and therefore applicable to other measurement and calibration systems.
A POTENTIAL APPLICATION OF UNCERTAINTY ANALYSIS TO DOE-STD-3009-94 ACCIDENT ANALYSIS
Palmrose, D E; Yang, J M
2007-05-10
The objective of this paper is to assess proposed transuranic waste accident analysis guidance and recent software improvements in a Windows-OS version of MACCS2 that allows the inputting of parameter uncertainty. With this guidance and code capability, there is the potential to perform a quantitative uncertainty assessment of unmitigated accident releases with respect to the 25 rem Evaluation Guideline (EG) of DOE-STD-3009-94 CN3 (STD-3009). Historically, the classification of safety systems in a U.S. Department of Energy (DOE) nuclear facility's safety basis has involved how subject matter experts qualitatively view uncertainty in the STD-3009 Appendix A accident analysis methodology. Specifically, whether consequence uncertainty could be larger than previously evaluated so the site-specific accident consequences may challenge the EG. This paper assesses whether a potential uncertainty capability for MACCS2 could provide a stronger technical basis as to when the consequences from a design basis accident (DBA) truly challenges the 25 rem EG.
LCOE Uncertainty Analysis for Hydropower using Monte Carlo Simulations
Chalise, Dol Raj; O'Connor, Patrick W; DeNeale, Scott T; Uria Martinez, Rocio; Kao, Shih-Chieh
2015-01-01
Levelized Cost of Energy (LCOE) is an important metric to evaluate the cost and performance of electricity production generation alternatives, and combined with other measures, can be used to assess the economics of future hydropower development. Multiple assumptions on input parameters are required to calculate the LCOE, which each contain some level of uncertainty, in turn affecting the accuracy of LCOE results. This paper explores these uncertainties, their sources, and ultimately the level of variability they introduce at the screening level of project evaluation for non-powered dams (NPDs) across the U.S. Owing to site-specific differences in site design, the LCOE for hydropower varies significantly from project to project unlike technologies with more standardized configurations such as wind and gas. Therefore, to assess the impact of LCOE input uncertainty on the economics of U.S. hydropower resources, these uncertainties must be modeled across the population of potential opportunities. To demonstrate the impact of uncertainty, resource data from a recent nationwide non-powered dam (NPD) resource assessment (Hadjerioua et al., 2012) and screening-level predictive cost equations (O Connor et al., 2015) are used to quantify and evaluate uncertainties in project capital and operations & maintenance costs, and generation potential at broad scale. LCOE dependence on financial assumptions is also evaluated on a sensitivity basis to explore ownership/investment implications on project economics for the U.S. hydropower fleet. The results indicate that the LCOE for U.S. NPDs varies substantially. The LCOE estimates for the potential NPD projects of capacity greater than 1 MW range from 40 to 182 $/MWh, with average of 106 $/MWh. 4,000 MW could be developed through projects with individual LCOE values below 100 $/MWh. The results also indicate that typically 90 % of LCOE uncertainty can be attributed to uncertainties in capital costs and energy production; however
Uncertainty analysis of the AEDC 7V chamber
NASA Astrophysics Data System (ADS)
Crider, Dustin; Lowry, Heard; Nicholson, Randy; Mead, Kimberly
2005-05-01
For over 30 years, the Space Systems Test Facility and space chambers at the Arnold Engineering Development Center (AEDC) have been used to perform space sensor characterization, calibration, and mission simulation testing of space-based, interceptor, and airborne sensors. In partnership with the Missile Defense Agency (MDA), capability upgrades are continuously pursued to keep pace with evolving sensor technologies. Upgrades to sensor test facilities require rigorous facility characterization and calibration activities that are part of AEDC's annual activities to comply with Major Range Test Facility Base processes to ensure quality metrology and test data. This paper discusses the ongoing effort to characterize and quantify Aerospace Chamber 7V measurement uncertainties. The 7V Chamber is a state-of-the-art cryogenic/vacuum facility providing calibration and high-fidelity mission simulation for infrared seekers and sensors against a low-infrared background. One of its key features is the high fidelity of the radiometric calibration process. Calibration of the radiometric sources used is traceable to the National Institute of Standards and Technology and provides relative uncertainties on the order of two to three percent, based on measurement data acquired during many test periods. Three types of sources of measurement error and top-level uncertainties have been analyzed; these include radiometric calibration, target position, and spectral output. The approach used and presented is to quantify uncertainties of each component in the optical system and then build uncertainty diagrams and easily updated databases to detail the uncertainty for each optical system. The formalism, equations, and corresponding analyses are provided to help describe how the specific quantities are derived and currently used. This paper presents the uncertainty methodology used and current results.
Sensitivity and uncertainty analysis applied to the JHR reactivity prediction
Leray, O.; Vaglio-Gaudard, C.; Hudelot, J. P.; Santamarina, A.; Noguere, G.; Di-Salvo, J.
2012-07-01
The on-going AMMON program in EOLE reactor at CEA Cadarache (France) provides experimental results to qualify the HORUS-3D/N neutronics calculation scheme used for the design and safety studies of the new Material Testing Jules Horowitz Reactor (JHR). This paper presents the determination of technological and nuclear data uncertainties on the core reactivity and the propagation of the latter from the AMMON experiment to JHR. The technological uncertainty propagation was performed with a direct perturbation methodology using the 3D French stochastic code TRIPOLI4 and a statistical methodology using the 2D French deterministic code APOLLO2-MOC which leads to a value of 289 pcm (1{sigma}). The Nuclear Data uncertainty propagation relies on a sensitivity study on the main isotopes and the use of a retroactive marginalization method applied to the JEFF 3.1.1 {sup 27}Al evaluation in order to obtain a realistic multi-group covariance matrix associated with the considered evaluation. This nuclear data uncertainty propagation leads to a K{sub eff} uncertainty of 624 pcm for the JHR core and 684 pcm for the AMMON reference configuration core. Finally, transposition and reduction of the prior uncertainty were made using the Representativity method which demonstrates the similarity of the AMMON experiment with JHR (the representativity factor is 0.95). The final impact of JEFF 3.1.1 nuclear data on the Begin Of Life (BOL) JHR reactivity calculated by the HORUS-3D/N V4.0 is a bias of +216 pcm with an associated posterior uncertainty of 304 pcm (1{sigma}). (authors)
Uncertainty Analysis of RELAP5-3D
Alexandra E Gertman; Dr. George L Mesina
2012-07-01
As world-wide energy consumption continues to increase, so does the demand for the use of alternative energy sources, such as Nuclear Energy. Nuclear Power Plants currently supply over 370 gigawatts of electricity, and more than 60 new nuclear reactors have been commissioned by 15 different countries. The primary concern for Nuclear Power Plant operation and lisencing has been safety. The safety of the operation of Nuclear Power Plants is no simple matter- it involves the training of operators, design of the reactor, as well as equipment and design upgrades throughout the lifetime of the reactor, etc. To safely design, operate, and understand nuclear power plants, industry and government alike have relied upon the use of best-estimate simulation codes, which allow for an accurate model of any given plant to be created with well-defined margins of safety. The most widely used of these best-estimate simulation codes in the Nuclear Power industry is RELAP5-3D. Our project focused on improving the modeling capabilities of RELAP5-3D by developing uncertainty estimates for its calculations. This work involved analyzing high, medium, and low ranked phenomena from an INL PIRT on a small break Loss-Of-Coolant Accident as wall as an analysis of a large break Loss-Of- Coolant Accident. Statistical analyses were performed using correlation coefficients. To perform the studies, computer programs were written that modify a template RELAP5 input deck to produce one deck for each combination of key input parameters. Python scripting enabled the running of the generated input files with RELAP5-3D on INL’s massively parallel cluster system. Data from the studies was collected and analyzed with SAS. A summary of the results of our studies are presented.
NASA Technical Reports Server (NTRS)
Harder, R. L.
1974-01-01
The NASTRAN Thermal Analyzer has been intended to do variance analysis and plot the thermal boundary elements. The objective of the variance analysis addition is to assess the sensitivity of temperature variances resulting from uncertainties inherent in input parameters for heat conduction analysis. The plotting capability provides the ability to check the geometry (location, size and orientation) of the boundary elements of a model in relation to the conduction elements. Variance analysis is the study of uncertainties of the computed results as a function of uncertainties of the input data. To study this problem using NASTRAN, a solution is made for both the expected values of all inputs, plus another solution for each uncertain variable. A variance analysis module subtracts the results to form derivatives, and then can determine the expected deviations of output quantities.
Principles and applications of measurement and uncertainty analysis in research and calibration
Wells, C.V.
1992-11-01
Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that ``The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.`` Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true? What kind of information should we include in a statement of uncertainty accompanying a calibrated value? How and where do we get the information to include in an uncertainty statement? How should we interpret and use measurement uncertainty information? This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.
Principles and applications of measurement and uncertainty analysis in research and calibration
Wells, C.V.
1992-11-01
Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.'' Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true What kind of information should we include in a statement of uncertainty accompanying a calibrated value How and where do we get the information to include in an uncertainty statement How should we interpret and use measurement uncertainty information This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.
Uncertainty and Sensitivity Analysis in Performance Assessment for the Waste Isolation Pilot Plant
Helton, J.C.
1998-12-17
The Waste Isolation Pilot Plant (WIPP) is under development by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) waste. This development has been supported by a sequence of performance assessments (PAs) carried out by Sandla National Laboratories (SNL) to assess what is known about the WIPP and to provide .tidance for future DOE research and development activities. Uncertainty and sensitivity analysis procedures based on Latin hypercube sampling and regression techniques play an important role in these PAs by providing an assessment of the uncertainty in important analysis outcomes and identi~ing the sources of thk uncertainty. Performance assessments for the WIPP are conceptually and computational] y interesting due to regulatory requirements to assess and display the effects of both stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, where stochastic uncertainty arises from the possible disruptions that could occur over the 10,000 yr regulatory period associated with the WIPP and subjective uncertainty arises from an inability to unambi-aously characterize the many models and associated parameters required in a PA for the WIPP. The interplay between uncertainty analysis, sensitivity analysis, stochastic uncertainty and subjective uncertainty are discussed and illustrated in the context of a recent PA carried out by SNL to support an application by the DOE to the U.S. Environmental Protection Agency for the certification of the WIPP for the disposal of TRU waste.
Harper, F.T.; Young, M.L.; Miller, L.A.
1995-01-01
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the third of a three-volume document describing the project and contains descriptions of the probability assessment principles; the expert identification and selection process; the weighting methods used; the inverse modeling methods; case structures; and summaries of the consequence codes.
Bixler, Nathan E.; Osborn, Douglas M.; Sallaberry, Cedric Jean-Marie; Eckert-Gallup, Aubrey Celia; Mattie, Patrick D.; Ghosh, S. Tina
2014-02-01
This paper describes the convergence of MELCOR Accident Consequence Code System, Version 2 (MACCS2) probabilistic results of offsite consequences for the uncertainty analysis of the State-of-the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout scenario at the Peach Bottom Atomic Power Station. The consequence metrics evaluated are individual latent-cancer fatality (LCF) risk and individual early fatality risk. Consequence results are presented as conditional risk (i.e., assuming the accident occurs, risk per event) to individuals of the public as a result of the accident. In order to verify convergence for this uncertainty analysis, as recommended by the Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards, a ‘high’ source term from the original population of Monte Carlo runs has been selected to be used for: (1) a study of the distribution of consequence results stemming solely from epistemic uncertainty in the MACCS2 parameters (i.e., separating the effect from the source term uncertainty), and (2) a comparison between Simple Random Sampling (SRS) and Latin Hypercube Sampling (LHS) in order to validate the original results obtained with LHS. Three replicates (each using a different random seed) of size 1,000 each using LHS and another set of three replicates of size 1,000 using SRS are analyzed. The results show that the LCF risk results are well converged with either LHS or SRS sampling. The early fatality risk results are less well converged at radial distances beyond 2 miles, and this is expected due to the sparse data (predominance of “zero” results).
Fuzzy-algebra uncertainty analysis for abnormal-environment safety assessment
Cooper, J.A.
1994-01-01
Many safety (risk) analyses depend on uncertain inputs and on mathematical models chosen from various alternatives, but give fixed results (implying no uncertainty). Conventional uncertainty analyses help, but are also based on assumptions and models, the accuracy of which may be difficult to assure. Some of the models and assumptions that on cursory examination seem reasonable can be misleading. As a result, quantitative assessments, even those accompanied by uncertainty measures, can give unwarranted impressions of accuracy. Since analysis results can be a major contributor to a safety-measure decision process, risk management depends on relating uncertainty to only the information available. The uncertainties due to abnormal environments are even more challenging than those in normal-environment safety assessments, and therefore require an even more cautious approach. A fuzzy algebra analysis is proposed in this report that has the potential to appropriately reflect the information available and portray uncertainties well, especially for abnormal environments.
We use Bayesian uncertainty analysis to explore how to estimate pollutant exposures from biomarker concentrations. The growing number of national databases with exposure data makes such an analysis possible. They contain datasets of pharmacokinetic biomarkers for many polluta...
Kennedy, Marc C; van der Voet, Hilko; Roelofs, Victoria J; Roelofs, Willem; Glass, C Richard; de Boer, Waldo J; Kruisselbrink, Johannes W; Hart, Andy D M
2015-05-01
Risk assessments for human exposures to plant protection products (PPPs) have traditionally focussed on single routes of exposure and single compounds. Extensions to estimate aggregate (multi-source) and cumulative (multi-compound) exposure from PPPs present many new challenges and additional uncertainties that should be addressed as part of risk analysis and decision-making. A general approach is outlined for identifying and classifying the relevant uncertainties and variabilities. The implementation of uncertainty analysis within the MCRA software, developed as part of the EU-funded ACROPOLIS project to address some of these uncertainties, is demonstrated. An example is presented for dietary and non-dietary exposures to the triazole class of compounds. This demonstrates the chaining of models, linking variability and uncertainty generated from an external model for bystander exposure with variability and uncertainty in MCRA dietary exposure assessments. A new method is also presented for combining pesticide usage survey information with limited residue monitoring data, to address non-detect uncertainty. The results show that incorporating usage information reduces uncertainty in parameters of the residue distribution but that in this case quantifying uncertainty is not a priority, at least for UK grown crops. A general discussion of alternative approaches to treat uncertainty, either quantitatively or qualitatively, is included. PMID:25688423
Design and Uncertainty Analysis for a PVTt Gas Flow Standard
Wright, John D.; Johnson, Aaron N.; Moldover, Michael R.
2003-01-01
A new pressure, volume, temperature, and, time (PVTt) primary gas flow standard at the National Institute of Standards and Technology has an expanded uncertainty (k = 2) of between 0.02 % and 0.05 %. The standard spans the flow range of 1 L/min to 2000 L/min using two collection tanks and two diverter valve systems. The standard measures flow by collecting gas in a tank of known volume during a measured time interval. We describe the significant and novel features of the standard and analyze its uncertainty. The gas collection tanks have a small diameter and are immersed in a uniform, stable, thermostatted water bath. The collected gas achieves thermal equilibrium rapidly and the uncertainty of the average gas temperature is only 7 mK (22 × 10−6 T). A novel operating method leads to essentially zero mass change in and very low uncertainty contributions from the inventory volume. Gravimetric and volume expansion techniques were used to determine the tank and the inventory volumes. Gravimetric determinations of collection tank volume made with nitrogen and argon agree with a standard deviation of 16 × 10−6 VT. The largest source of uncertainty in the flow measurement is drift of the pressure sensor over time, which contributes relative standard uncertainty of 60 × 10−6 to the determinations of the volumes of the collection tanks and to the flow measurements. Throughout the range 3 L/min to 110 L/min, flows were measured independently using the 34 L and the 677 L collection systems, and the two systems agreed within a relative difference of 150 × 10−6. Double diversions were used to evaluate the 677 L system over a range of 300 L/min to 1600 L/min, and the relative differences between single and double diversions were less than 75 × 10−6. PMID:27413592
Probabilistic structural analysis to quantify uncertainties associated with turbopump blades
NASA Technical Reports Server (NTRS)
Nagpal, Vinod K.; Rubinstein, Robert; Chamis, Christos C.
1987-01-01
A probabilistic study of turbopump blades has been in progress at NASA Lewis Research Center for over the last two years. The objectives of this study are to evaluate the effects of uncertainties in geometry and material properties on the structural response of the turbopump blades to evaluate the tolerance limits on the design. A methodology based on probabilistic approach has been developed to quantify the effects of the random uncertainties. The results of this study indicate that only the variations in geometry have significant effects.
Population Uncertainty in Model Ecosystem: Analysis by Stochastic Differential Equation
NASA Astrophysics Data System (ADS)
Morita, Satoru; Tainaka, Kei-ichi; Nagata, Hiroyasu; Yoshimura, Jin
2008-09-01
Perturbation experiments are carried out by the numerical simulations of a contact process and its mean-field version. Here, the mortality rate increases or decreases suddenly. It is known that fluctuation enhancement (FE) occurs after perturbation, where FE indicates population uncertainty. In the present paper, we develop a new theory of stochastic differential equation. The agreement between the theory and the mean-field simulation is almost perfect. This theory enables us to find a much stronger FE than that reported previously. We discuss the population uncertainty in the recovering process of endangered species.
Thorough approach to measurement uncertainty analysis applied to immersed heat exchanger testing
Farrington, R B; Wells, C V
1986-04-01
This paper discusses the value of an uncertainty analysis, discusses how to determine measurement uncertainty, and then details the sources of error in instrument calibration, data acquisition, and data reduction for a particular experiment. Methods are discussed to determine both the systematic (or bias) error in an experiment as well as to determine the random (or precision) error in the experiment. The detailed analysis is applied to two sets of conditions in measuring the effectiveness of an immersed coil heat exchanger. It shows the value of such analysis as well as an approach to reduce overall measurement uncertainty and to improve the experiment. This paper outlines how to perform an uncertainty analysis and then provides a detailed example of how to apply the methods discussed in the paper. The authors hope this paper will encourage researchers and others to become more concerned with their measurement processes and to report measurement uncertainty with all of their test results.
Gregory, Kent J; Pattison, John E; Bibbo, Giovanni
2015-03-01
The minimal dose covering 90 % of the prostate volume--D 90--is arguably the most important dosimetric parameter in low-dose-rate prostate seed brachytherapy. In this study an analysis of the measurement uncertainties in D 90 from low-dose-rate prostate seed brachytherapy was conducted for two common treatment procedures with two different post-implant dosimetry methods. The analysis was undertaken in order to determine the magnitude of D 90 uncertainty, how the magnitude of the uncertainty varied when D 90 was calculated using different dosimetry methods, and which factors were the major contributors to the uncertainty. The analysis considered the prostate as being homogeneous and tissue equivalent and made use of published data, as well as original data collected specifically for this analysis, and was performed according to the Guide to the expression of uncertainty in measurement (GUM). It was found that when prostate imaging and seed implantation were conducted in two separate sessions using only CT images for post-implant analysis, the expanded uncertainty in D 90 values were about 25 % at the 95 % confidence interval. When prostate imaging and seed implantation were conducted during a single session using CT and ultrasound images for post-implant analysis, the expanded uncertainty in D 90 values were about 33 %. Methods for reducing these uncertainty levels are discussed. It was found that variations in contouring the target tissue made the largest contribution to D 90 uncertainty, while the uncertainty in seed source strength made only a small contribution. It is important that clinicians appreciate the overall magnitude of D 90 uncertainty and understand the factors that affect it so that clinical decisions are soundly based, and resources are appropriately allocated. PMID:25555753
A probability density function method for acoustic field uncertainty analysis
NASA Astrophysics Data System (ADS)
James, Kevin R.; Dowling, David R.
2005-11-01
Acoustic field predictions, whether analytical or computational, rely on knowledge of the environmental, boundary, and initial conditions. When knowledge of these conditions is uncertain, acoustic field predictions will also be uncertain, even if the techniques for field prediction are perfect. Quantifying acoustic field uncertainty is important for applications that require accurate field amplitude and phase predictions, like matched-field techniques for sonar, nondestructive evaluation, bio-medical ultrasound, and atmospheric remote sensing. Drawing on prior turbulence research, this paper describes how an evolution equation for the probability density function (PDF) of the predicted acoustic field can be derived and used to quantify predicted-acoustic-field uncertainties arising from uncertain environmental, boundary, or initial conditions. Example calculations are presented in one and two spatial dimensions for the one-point PDF for the real and imaginary parts of a harmonic field, and show that predicted field uncertainty increases with increasing range and frequency. In particular, at 500 Hz in an ideal 100 m deep underwater sound channel with a 1 m root-mean-square depth uncertainty, the PDF results presented here indicate that at a range of 5 km, all phases and a 10 dB range of amplitudes will have non-negligible probability. Evolution equations for the two-point PDF are also derived.
Uncertainty analysis for a field-scale P loss model
Technology Transfer Automated Retrieval System (TEKTRAN)
Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predic...
Uncertainty Analysis in Large Area Aboveground Biomass Mapping
NASA Astrophysics Data System (ADS)
Baccini, A.; Carvalho, L.; Dubayah, R.; Goetz, S. J.; Friedl, M. A.
2011-12-01
Satellite and aircraft-based remote sensing observations are being more frequently used to generate spatially explicit estimates of aboveground carbon stock of forest ecosystems. Because deforestation and forest degradation account for circa 10% of anthropogenic carbon emissions to the atmosphere, policy mechanisms are increasingly recognized as a low-cost mitigation option to reduce carbon emission. They are, however, contingent upon the capacity to accurately measures carbon stored in the forests. Here we examine the sources of uncertainty and error propagation in generating maps of aboveground biomass. We focus on characterizing uncertainties associated with maps at the pixel and spatially aggregated national scales. We pursue three strategies to describe the error and uncertainty properties of aboveground biomass maps, including: (1) model-based assessment using confidence intervals derived from linear regression methods; (2) data-mining algorithms such as regression trees and ensembles of these; (3) empirical assessments using independently collected data sets.. The latter effort explores error propagation using field data acquired within satellite-based lidar (GLAS) acquisitions versus alternative in situ methods that rely upon field measurements that have not been systematically collected for this purpose (e.g. from forest inventory data sets). A key goal of our effort is to provide multi-level characterizations that provide both pixel and biome-level estimates of uncertainties at different scales.
NASA Astrophysics Data System (ADS)
Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang
2014-08-01
Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.
Survey of sampling-based methods for uncertainty and sensitivity analysis.
Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD.; Storlie, Curt B. (Colorado State University, Fort Collins, CO)
2006-06-01
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.
Model parameter uncertainty analysis for an annual field-scale phosphorus loss model
Technology Transfer Automated Retrieval System (TEKTRAN)
Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with ...
Parameter uncertainty analysis for the annual phosphorus loss estimator (APLE) model
Technology Transfer Automated Retrieval System (TEKTRAN)
Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with ...
Linguistic uncertainty in qualitative risk analysis and how to minimize it.
Carey, Janet M; Burgman, Mark A
2008-04-01
Most risk assessments assume uncertainty may be decomposed into variability and incertitude. Language is often overlooked as a source of uncertainty, but linguistic uncertainty may be pervasive in workshops, committees, and other face-to-face language-based settings where it can result in misunderstanding and arbitrary disagreement. Here we present examples of linguistic uncertainty drawn from qualitative risk analysis undertaken in stakeholder workshops and describe how the uncertainties were treated. We used a process of iterative re-assessment of likelihoods and consequences, interspersed with facilitated discussion, to assist in the reduction of language-based uncertainty. The effects of this process were evident as changes in the level of agreement among groups of assessors in the ranking of hazards. PMID:18469210
Use of paired simple and complex models to reduce predictive bias and quantify uncertainty
NASA Astrophysics Data System (ADS)
Doherty, John; Christensen, Steen
2011-12-01
Modern environmental management and decision-making is based on the use of increasingly complex numerical models. Such models have the advantage of allowing representation of complex processes and heterogeneous system property distributions inasmuch as these are understood at any particular study site. The latter are often represented stochastically, this reflecting knowledge of the character of system heterogeneity at the same time as it reflects a lack of knowledge of its spatial details. Unfortunately, however, complex models are often difficult to calibrate because of their long run times and sometimes questionable numerical stability. Analysis of predictive uncertainty is also a difficult undertaking when using models such as these. Such analysis must reflect a lack of knowledge of spatial hydraulic property details. At the same time, it must be subject to constraints on the spatial variability of these details born of the necessity for model outputs to replicate observations of historical system behavior. In contrast, the rapid run times and general numerical reliability of simple models often promulgates good calibration and ready implementation of sophisticated methods of calibration-constrained uncertainty analysis. Unfortunately, however, many system and process details on which uncertainty may depend are, by design, omitted from simple models. This can lead to underestimation of the uncertainty associated with many predictions of management interest. The present paper proposes a methodology that attempts to overcome the problems associated with complex models on the one hand and simple models on the other hand, while allowing access to the benefits each of them offers. It provides a theoretical analysis of the simplification process from a subspace point of view, this yielding insights into the costs of model simplification, and into how some of these costs may be reduced. It then describes a methodology for paired model usage through which predictive
Measurement uncertainties in regression analysis with scarcity of data
NASA Astrophysics Data System (ADS)
Sousa, J. A.; Ribeiro, A. S.; Cox, M. G.; Harris, P. M.; Sousa, J. F. V.
2010-07-01
The evaluation of measurement uncertainty, in certain fields of science, faces the problem of scarcity of data. This is certainly the case in the testing of geological soils in civil engineering, where tests can take several days or weeks and where the same sample is not available for further testing, being destroyed during the experiment. In this particular study attention will be paid to triaxial compression tests used to typify particular soils. The purpose of the testing is to determine two parameters that characterize the soil, namely, cohesion and friction angle. These parameters are defined in terms of the intercept and slope of a straight line fitted to a small number of points (usually three) derived from experimental data. The use of ordinary least squares to obtain uncertainties associated with estimates of the two parameters would be unreliable if there were only three points (and no replicates) and hence only one degrees of freedom.
Uncertainty analysis of densities and isotopics: Handling correlations
Favorite, J. A.; Armstrong, J. C.; Burr, T.
2013-07-01
This paper discusses two cases of correlated parameters in uncertainty analyses: (1) the case of measured mass, density, and volume or spatial dimension correlations; and (2) the case of measured material isotopics, where increasing one atom fraction must cause the others to decrease. In the first case, an equation is derived that has a term due to uncertain density, a term due to uncertain dimensions, and a term due to the correlation between density and dimensions. In a numerical test problem, this equation gives the same result as the standard equation that treats mass and dimensions independently. In the case of isotopics, an equation is derived relating the uncertainty due to uncertain isotopic fractions to the sensitivities to isotopic densities, which are easier to calculate. The equation is verified in a test problem. (authors)
The combined analysis of uncertainty and patient heterogeneity in medical decision models.
Groot Koerkamp, Bas; Stijnen, Theo; Weinstein, Milton C; Hunink, M G Myriam
2011-01-01
The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity. PMID:20974904
Song, Junho; Nguyen, Tam H.; Paulino, Glaucio H.
2008-02-15
Probabilistic fracture analysis is performed for predicting uncertain fracture responses of Functionally Graded Material (FGM) structures. The uncertainties in material properties including Young's modulus and fracture toughness are considered. The limit state function for a crack initiation event is defined in terms of the J-integral for FGMs. The First-Order-Reliability-Method (FORM) is used in conjunction with a finite element code that computes the J-integral with high accuracy. A two-step probabilistic analysis procedure is proposed to investigate the effects of the uncertainties in the spatial distribution of Young's modulus on the probability of crack initiation in FGMs. First, we investigate the effects of the uncertainties in the shape of the spatial distribution by considering the slope and the location of the inflection point of a spatial distribution profile as random quantities. Second, we investigate the effects of the spatial fluctuations of Young's modulus by making use of a discretized random field. The companion paper (Part II) implements this method into a finite element fracture analysis code and presents numerical examples.
Zhang, Xuesong; Zhao, Kaiguang
2012-06-01
Bayesian Neural Networks (BNNs) have been shown as useful tools to analyze modeling uncertainty of Neural Networks (NNs). This research focuses on the comparison of two BNNs. The first BNNs (BNN-I) use statistical methods to describe the characteristics of different uncertainty sources (input, parameter, and model structure) and integrate these uncertainties into a Markov Chain Monte Carlo (MCMC) framework to estimate total uncertainty. The second BNNs (BNN-II) lump all uncertainties into a single error term (i.e. the residual between model prediction and measurement). In this study, we propose a simple BNN-II, which use Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) to calibrate Neural Networks with different structures (number of hidden units) and combine the predictions from different NNs to derive predictions and uncertainty analysis. We tested these two BNNs in two watersheds for daily and monthly hydrologic simulation. The BMA based BNNs developed in this study outperforms BNN-I in the two watersheds in terms of both accurate prediction and uncertainty estimation. These results show that, given incomplete understanding of the characteristics associated with each uncertainty source, the simple lumped error approach may yield better prediction and uncertainty estimation.
Incorporating Fuzzy Systems Modeling and Possibility Theory in Hydrogeological Uncertainty Analysis
NASA Astrophysics Data System (ADS)
Faybishenko, B.
2008-12-01
Hydrogeological predictions are subject to numerous uncertainties, including the development of conceptual, mathematical, and numerical models, as well as determination of their parameters. Stochastic simulations of hydrogeological systems and the associated uncertainty analysis are usually based on the assumption that the data characterizing spatial and temporal variations of hydrogeological processes are random, and the output uncertainty is quantified using a probability distribution. However, hydrogeological systems are often characterized by imprecise, vague, inconsistent, incomplete or subjective information. One of the modern approaches to modeling and uncertainty quantification of such systems is based on using a combination of statistical and fuzzy-logic uncertainty analyses. The aims of this presentation are to: (1) present evidence of fuzziness in developing conceptual hydrogeological models, and (2) give examples of the integration of the statistical and fuzzy-logic analyses in modeling and assessing both aleatoric uncertainties (e.g., caused by vagueness in assessing the subsurface system heterogeneities of fractured-porous media) and epistemic uncertainties (e.g., caused by the selection of different simulation models) involved in hydrogeological modeling. The author will discuss several case studies illustrating the application of fuzzy modeling for assessing the water balance and water travel time in unsaturated-saturated media. These examples will include the evaluation of associated uncertainties using the main concepts of possibility theory, a comparison between the uncertainty evaluation using probabilistic and possibility theories, and a transformation of the probabilities into possibilities distributions (and vice versa) for modeling hydrogeological processes.
NASA Astrophysics Data System (ADS)
Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas
2014-03-01
GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.
UNCERTAINTY ANALYSIS OF TCE USING THE DOSE EXPOSURE ESTIMATING MODEL (DEEM) IN ACSL
The ACSL-based Dose Exposure Estimating Model(DEEM) under development by EPA is used to perform art uncertainty analysis of a physiologically based pharmacokinetic (PSPK) model of trichloroethylene (TCE). This model involves several circulating metabolites such as trichloroacet...
PROBABILISTIC SENSITIVITY AND UNCERTAINTY ANALYSIS WORKSHOP SUMMARY REPORT
Seitz, R
2008-06-25
Stochastic or probabilistic modeling approaches are being applied more frequently in the United States and globally to quantify uncertainty and enhance understanding of model response in performance assessments for disposal of radioactive waste. This increased use has resulted in global interest in sharing results of research and applied studies that have been completed to date. This technical report reflects the results of a workshop that was held to share results of research and applied work related to performance assessments conducted at United States Department of Energy sites. Key findings of this research and applied work are discussed and recommendations for future activities are provided.
Video Scanning Hartmann Optical Tester (VSHOT) Uncertainty Analysis: Preprint
Lewandowski, A.; Gray, A.
2010-10-01
This purely analytical work is based primarily on the geometric optics of the system and shows sensitivities to various design and operational parameters. We discuss sources of error with measuring devices, instrument calibrations, and operator measurements for a parabolic trough test. In this paper, we include both the random (precision) and systematic (bias) errors for VSHOT testing and their contributions to the uncertainty. The contributing factors that we considered in this study are target tilt, target face to laser output distance, instrument vertical offset, scanner tilt, distance between the tool and the test piece, camera calibration, and scanner/calibration.
CALIBRATION AND UNCERTAINTY ANALYSIS OF SWAT MODEL IN A JAPANESE RIVER CATCHMENT
NASA Astrophysics Data System (ADS)
Luo, Pingping; Takara, Kaoru; He, Bin; Cao, Wenqiang; Yamashiki, Yosuke; Nover, Daniel
Calibration and uncertainty analysis is necessary to perform the best estimation and uncertainty identification of hydrological models. This paper uses the Soil and Water Assessment Tool-Calibration and Uncertainly Procedures (SWAT-CUP) model to analyze the uncertainty of SWAT model in a Japanese river catchment. The GLUE and SUFI-2 techniques used in this analysis show quite good results with high value of R2 as 0.98 and 0.95 for monthly simulation. Daily simulation results during calibration and validation are also good with R2 as 0.86 and 0.80. For uncertainty results, the 95% prediction uncertainty (95PPU) brackets very well with the observation. The p-factors of uncertainty analysis for the calibration and validation periods are 92% and 94%. The calibration result by using GLUE shows better than that by using SUFI-2. However, the processing time of the GLUE approach is longer than SUFI-2 approach when they were run in the SWAT-CUP. The uncertainty analysis indicates that the parameters of effective hydraulic conductivity in main channel alluvium (CH_K2) and base-flow alpha factor for bank storage (ALPHA_BNK) play important roles for calibration and validation of SWAT model.
Uncertainty analysis of diffuse-gray radiation enclosure problems: A hypersensitive case study
NASA Technical Reports Server (NTRS)
Taylor, Robert P.; Luck, Rogelio; Hodge, B. K.; Steele, W. Glenn
1993-01-01
An uncertainty analysis of diffuse-gray enclosure problems is presented. The genesis was a diffuse-gray enclosure problem which proved to be hypersensitive to the specification of view factors. This genesis is discussed in some detail. The uncertainty analysis is presented for the general diffuse-gray enclosure problem and applied to the hypersensitive case study. It was found that the hypersensitivity could be greatly reduced by enforcing both closure and reciprocity for the view factors. The effects of uncertainties in the surface emissivities and temperatures are also investigated.
Sensitvity and Uncertainty Analysis for a Minor-actinide Transmuter with JENDL-4.0
NASA Astrophysics Data System (ADS)
Iwamoto, H.; Nishihara, K.; Sugawara, T.; Tsujimoto, K.
2014-04-01
A sensitivity and uncertainty analysis was performed for the minor-actinide transmuter proposed by the Japan Atomic Energy Agency with JENDL-4.0. Analysis with sensitivity coefficients and the JENDL-4.0 covariance data showed that the covariances of the capture cross sections and fission-related parameters of MAs and Pu isotopes have considerable impact on the uncertainties of reactor physics parameters, and covariances of the inelastic scattering cross section of lead-bismuth eutectic (LBE) materials significantly affect the uncertainty of coolant-void reactivity.
PRACTICAL SENSITIVITY AND UNCERTAINTY ANALYSIS TECHNIQUES APPLIED TO AGRICULTURAL SYSTEMS MODELS
Technology Transfer Automated Retrieval System (TEKTRAN)
We present a practical evaluation framework for analysis of two complex, process-based agricultural system models, WEPP and RZWQM. The evaluation framework combines sensitivity analysis and the uncertainty analysis techniques of first order error analysis (FOA) and Monte Carlo simulation with Latin ...
Analysis and Reduction of Complex Networks Under Uncertainty.
Ghanem, Roger G
2014-07-31
This effort was a collaboration with Youssef Marzouk of MIT, Omar Knio of Duke University (at the time at Johns Hopkins University) and Habib Najm of Sandia National Laboratories. The objective of this effort was to develop the mathematical and algorithmic capacity to analyze complex networks under uncertainty. Of interest were chemical reaction networks and smart grid networks. The statements of work for USC focused on the development of stochastic reduced models for uncertain networks. The USC team was led by Professor Roger Ghanem and consisted of one graduate student and a postdoc. The contributions completed by the USC team consisted of 1) methodology and algorithms to address the eigenvalue problem, a problem of significance in the stability of networks under stochastic perturbations, 2) methodology and algorithms to characterize probability measures on graph structures with random flows. This is an important problem in characterizing random demand (encountered in smart grid) and random degradation (encountered in infrastructure systems), as well as modeling errors in Markov Chains (with ubiquitous relevance !). 3) methodology and algorithms for treating inequalities in uncertain systems. This is an important problem in the context of models for material failure and network flows under uncertainty where conditions of failure or flow are described in the form of inequalities between the state variables.
NASA Technical Reports Server (NTRS)
Groves, Curtis E.; LLie, Marcel; Shallhorn, Paul A.
2012-01-01
There are inherent uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field and there is no standard method for evaluating uncertainty in the CFD community. This paper describes an approach to -validate the . uncertainty in using CFD. The method will use the state of the art uncertainty analysis applying different turbulence niodels and draw conclusions on which models provide the least uncertainty and which models most accurately predict the flow of a backward facing step.
NASA Astrophysics Data System (ADS)
Ramírez, Andrea; de Keizer, Corry; Van der Sluijs, Jeroen P.; Olivier, Jos; Brandes, Laurens
This paper presents an assessment of the value added of a Monte Carlo analysis of the uncertainties in the Netherlands inventory of greenhouse gases over a Tier 1 analysis. It also examines which parameters contributed the most to the total emission uncertainty and identified areas of high priority for the further improvement of the accuracy and quality of the inventory. The Monte Carlo analysis resulted in an uncertainty range in total GHG emissions of 4.1% in 2004 and 5.4% in 1990 (with LUCF) and 5.3% (in 1990) and 3.9% (in 2004) for GHG emissions without LUCF. Uncertainty in the trend was estimated at 4.5%. The values are in the same order of magnitude as those estimated in the Tier 1. The results show that accounting for correlation among parameters is important, and for the Netherlands inventory it has a larger impact on the uncertainty in the trend than on the uncertainty in the total GHG emissions. The main contributors to overall uncertainty are found to be related to N 2O emissions from agricultural soils, the N 2O implied emission factors of Nitric Acid Production, CH 4 from managed solid waste disposal on land, and the implied emission factor of CH 4 from manure management from cattle.
Uncertainty estimation in organic elemental analysis using information from proficiency tests.
Companyó, R; Rubio, R; Sahuquillo, A; Boqué, R; Maroto, A; Riu, J
2008-12-01
We evaluate the uncertainty in organic elemental analysis of C, H, N, and S. We use data from six proficiency tests (PTs), in which some 35 Spanish laboratories participated. The uncertainty of the technique is estimated from the relative within-laboratory and between-laboratory variances for pure substances and samples with complex matrices (soil, powdered milk, oil, ash, and petroleum coke). We also calculate the relative standard uncertainties for individual laboratories when analysing pure substances using historical data from the participation of each laboratory in different editions of PTs. The uncertainty values obtained for the individual laboratories are comparable with the uncertainty of the technique and correlate with the combined z-scores. The evolution over time of those laboratories participating in common editions of PTs is also evaluated. PMID:18853149
Monte carlo uncertainty analysis for photothermal radiometry measurements using a curve fit process
NASA Astrophysics Data System (ADS)
Horne, Kyle; Fleming, Austin; Timmins, Ben; Ban, Heng
2015-12-01
Photothermal radiometry (PTR) has become a popular method to measure thermal properties of layered materials. Much research has been done to determine the capabilities of PTR, but with little uncertainty analysis. This study reports a Monte Carlo uncertainty analysis to quantify uncertainty of film diffusivity and effusivity measurements, presents a sensitivity study for each input parameter, compares linear and logarithmic spacing of data points on frequency scans, and investigates the validity of a one-dimensional heat transfer assumption. Logarithmic spacing of frequencies when taking data is found to be unequivocally superior to linear spacing, while the use of a higher-dimensional heat transfer model is only needed for certain measurement configurations. The sensitivity analysis supports the frequency spacing conclusion, as well as explains trends seen in the uncertainty data.
Model calibration and uncertainty analysis in signaling networks.
Heinemann, Tim; Raue, Andreas
2016-06-01
For a long time the biggest challenges in modeling cellular signal transduction networks has been the inference of crucial pathway components and the qualitative description of their interactions. As a result of the emergence of powerful high-throughput experiments, it is now possible to measure data of high temporal and spatial resolution and to analyze signaling dynamics quantitatively. In addition, this increase of high-quality data is the basis for a better understanding of model limitations and their influence on the predictive power of models. We review established approaches in signal transduction network modeling with a focus on ordinary differential equation models as well as related developments in model calibration. As central aspects of the calibration process we discuss possibilities of model adaptation based on data-driven parameter optimization and the concomitant objective of reducing model uncertainties. PMID:27085224
Modelling System Processes to Support Uncertainty Analysis and Robustness Evaluation
NASA Technical Reports Server (NTRS)
Blackwell, Charles; Cuzzi, Jeffrey (Technical Monitor)
1996-01-01
In the use of advanced systems control techniques in the development of a dynamic system, results from effective mathematical modelling is required. Historically, in some cases the use of a model which only reflects the "expected" or "nominal" important -information about the system's internal processes has resulted in acceptable system performance, but it should be recognized that for those cases success was due to a combination of the remarkable inherent potential of feedback control for robustness and fortuitously wide margins between system performance requirements and system performance capability. In the cases of a CELSS development, no such fortuitous combinations should be expected, and it should be expected that the uncertainty in the information on the system's processes will have to be taken into account in order to generate a performance robust design. In this paper, we develop one perspective of the issue of providing robustness as mathematical modelling impacts it, and present some examples of model formats which serve the needed purpose.
Uncertainty analysis for probabilistic pipe fracture evaluations in LBB applications
Rahman, S.; Ghadiali, N.; Wilkowski, G.
1997-04-01
During the NRC`s Short Cracks in Piping and Piping Welds Program at Battelle, a probabilistic methodology was developed to conduct fracture evaluations of circumferentially cracked pipes for application to leak-rate detection. Later, in the IPIRG-2 program, several parameters that may affect leak-before-break and other pipe flaw evaluations were identified. This paper presents new results from several uncertainty analyses to evaluate the effects of normal operating stresses, normal plus safe-shutdown earthquake stresses, off-centered cracks, restraint of pressure-induced bending, and dynamic and cyclic loading rates on the conditional failure probability of pipes. systems in BWR and PWR. For each parameter, the sensitivity to conditional probability of failure and hence, its importance on probabilistic leak-before-break evaluations were determined.
Strydom, Gerhard; Bostelmann, Friederike; Ivanov, Kostadin
2014-10-01
The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of HTGR design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. One way to address the uncertainties in the HTGR analysis tools is to assess the sensitivity of critical parameters (such as the calculated maximum fuel temperature during loss of coolant accidents) to a few important input uncertainties. The input parameters were identified by engineering judgement in the past but are today typically based on a Phenomena Identification Ranking Table (PIRT) process. The input parameters can also be derived from sensitivity studies and are then varied in the analysis to find a spread in the parameter of importance. However, there is often no easy way to compensate for these uncertainties. In engineering system design, a common approach for addressing performance uncertainties is to add compensating margins to the system, but with passive properties credited it is not so clear how to apply it in the case of modular HTGR heat removal path. Other more sophisticated uncertainty modelling approaches, including Monte Carlo analysis, have also been proposed and applied. Ideally one wishes to apply a more fundamental approach to determine the predictive capability and accuracies of coupled neutronics/thermal-hydraulics and depletion simulations used for reactor design and safety assessment. Today there is a broader acceptance of the use of uncertainty analysis even in safety studies, and it has been accepted by regulators in some cases to replace the traditional conservative analysis. Therefore some safety analysis calculations may use a mixture of these approaches for different parameters depending upon the particular requirements of the analysis problem involved. Sensitivity analysis can for example be used to provide information as part of an uncertainty analysis to determine best estimate plus uncertainty results to the
NASA Astrophysics Data System (ADS)
Etemadi, H.; Samadi, S.; Sharifikia, M.
2014-06-01
Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely "Shadegan" in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987-2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.
Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model
Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.; Thorne, Paul D.; Wurstner, Signe K.; Rogers, Phillip M.
2001-11-09
Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the predictions being made with a site-wide groundwater flow and transport model at the U.S. Department of Energy Hanford Site in southeastern Washington State. In FY 2000, the focus of the initiative was on the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of an uncertainty estimation methodology in future assessments and analyses using the site-wide model. This report focuses on the development and implementation of an uncertainty analysis framework.
Cacuci, Dan G.; Ionescu-Bujor, Mihaela
2004-07-15
Part II of this review paper highlights the salient features of the most popular statistical methods currently used for local and global sensitivity and uncertainty analysis of both large-scale computational models and indirect experimental measurements. These statistical procedures represent sampling-based methods (random sampling, stratified importance sampling, and Latin Hypercube sampling), first- and second-order reliability algorithms (FORM and SORM, respectively), variance-based methods (correlation ratio-based methods, the Fourier Amplitude Sensitivity Test, and the Sobol Method), and screening design methods (classical one-at-a-time experiments, global one-at-a-time design methods, systematic fractional replicate designs, and sequential bifurcation designs). It is emphasized that all statistical uncertainty and sensitivity analysis procedures first commence with the 'uncertainty analysis' stage and only subsequently proceed to the 'sensitivity analysis' stage; this path is the exact reverse of the conceptual path underlying the methods of deterministic sensitivity and uncertainty analysis where the sensitivities are determined prior to using them for uncertainty analysis. By comparison to deterministic methods, statistical methods for uncertainty and sensitivity analysis are relatively easier to develop and use but cannot yield exact values of the local sensitivities. Furthermore, current statistical methods have two major inherent drawbacks as follows: 1. Since many thousands of simulations are needed to obtain reliable results, statistical methods are at best expensive (for small systems) or, at worst, impracticable (e.g., for large time-dependent systems).2. Since the response sensitivities and parameter uncertainties are inherently and inseparably amalgamated in the results produced by these methods, improvements in parameter uncertainties cannot be directly propagated to improve response uncertainties; rather, the entire set of simulations and
Sensitivity and Uncertainty Analysis in Chemical Mechanisms for Air Quality Modeling
NASA Astrophysics Data System (ADS)
Gao, Dongfen
1995-01-01
Ambient ozone in urban and regional air pollution is a serious environmental problem. Air quality models can be used to predict ozone concentrations and explore control strategies. One important component of such air quality models is a chemical mechanism. Sensitivity and uncertainty analysis play an important role in the evaluation of the performance of air quality models. The uncertainties associated with the RADM2 chemical mechanism in predicted concentrations of O_3, HCHO, H _2rm O_2, PAN, and HNO _3 were estimated. Monte Carlo simulations with Latin Hypercube Sampling were used to estimate the overall uncertainties in concentrations of species of interest, due to uncertainties in chemical parameters. The parameters that were treated as random variables were identified through first-order sensitivity and uncertainty analyses. Recent estimates of uncertainties in rate parameters and product yields were used. The results showed the relative uncertainties in ozone predictions are +/-23-50% (1sigma relative to the mean) in urban cases, and less than +/-20% in rural cases. Uncertainties in HNO_3 concentrations are the smallest, followed by HCHO, O_3 and PAN. Predicted H_2rm O_2 concentrations have the highest uncertainties. Uncertainties in the differences of peak ozone concentrations between base and control cases were also studied. The results show that the uncertainties in the fractional reductions in ozone concentrations were 9-12% with NO_{rm x} control at an ROG/NO_{rm x} ratio of 24:1 and 11-33% with ROG control at an ROG/NO _{rm x} ratio of 6:1. Linear regression analysis of the Monte Carlo results showed that uncertainties in rate parameters for the formation of HNO_3, for the reaction of HCHO + hv to 2HO _2 + CO, for PAN chemistry and for the photolysis of NO_2 are most influential to ozone concentrations and differences of ozone. The parameters that are important to ozone concentrations also tend to be relatively influential to other key species
Uncertainty analysis of the modelling chain from GCM to flood inundation
NASA Astrophysics Data System (ADS)
Wetterhall, F.; He, Y.; Freer, J. F.; Cloke, H.; Pappenberger, F.; Wilson, M.; McGregor, G.
2010-05-01
This study aims to set up novel techniques for tracking uncertainties through a modelling framework of extreme floods under a climate change. More specifically, thestudy will attempts to (1) assess future flood inundation impacts and extent as well as its hazards and (2) quantifying the cascading uncertainties in a modelling framework. The modeling framework consists of statistically and dynamically downscaled meteorological input from an ensemble of GCMs and RCMs. The climate input is further driving a set of rainfall-runoff models, in this case LISFLOOD-RR and HBV. The hydrological models provide modelled discharges which are fed through two flood inundation models, LISFLOOD-FP and HECRAS. Uncertainties in climate impact modelling are many, for example input errors in observations, impact model parameter and structural uncertainties, parameterisation and resolution errors in climate models and the underlying future scenarios. The uncertainties are cascaded through the modelling chain and it is important to rigorously estimate this uncertainty at all levels. The main aim of this project is to incorporate all these uncertainties at the very end of the chain in a flood risk map. The main research questions of this study are (1) how sensitive is the cascade setup to the downscaled meteorological input from the GCMs, particularly with respect to extreme events; (2) how is the climate change signal affected by the downscaling technique; (3) how can we quantify the sources and magnitude of uncertainties when simulating flood inundation within the context of climate change; (4) how do we deal with multi-scale multi-source uncertainties whilst taking into account the limitations of our observed measurements; (5) how do we develop strategies that improve the efficiency of sampling such a cascaded modeling structure to characterise the uncertainties and most importantly; (6) how do we convey the information to stake holders and policy makers? The analysis is done in three
Three Dimensional Vapor Intrusion Modeling: Model Validation and Uncertainty Analysis
NASA Astrophysics Data System (ADS)
Akbariyeh, S.; Patterson, B.; Rakoczy, A.; Li, Y.
2013-12-01
Volatile organic chemicals (VOCs), such as chlorinated solvents and petroleum hydrocarbons, are prevalent groundwater contaminants due to their improper disposal and accidental spillage. In addition to contaminating groundwater, VOCs may partition into the overlying vadose zone and enter buildings through gaps and cracks in foundation slabs or basement walls, a process termed vapor intrusion. Vapor intrusion of VOCs has been recognized as a detrimental source for human exposures to potential carcinogenic or toxic compounds. The simulation of vapor intrusion from a subsurface source has been the focus of many studies to better understand the process and guide field investigation. While multiple analytical and numerical models were developed to simulate the vapor intrusion process, detailed validation of these models against well controlled experiments is still lacking, due to the complexity and uncertainties associated with site characterization and soil gas flux and indoor air concentration measurement. In this work, we present an effort to validate a three-dimensional vapor intrusion model based on a well-controlled experimental quantification of the vapor intrusion pathways into a slab-on-ground building under varying environmental conditions. Finally, a probabilistic approach based on Monte Carlo simulations is implemented to determine the probability distribution of indoor air concentration based on the most uncertain input parameters.
Uncertainty Analysis of Historic Water Resources Availability in Africa
NASA Astrophysics Data System (ADS)
McNally, A.; Arsenault, K. R.; Narapusetty, B.; Peters-Lidard, C. D.
2014-12-01
Seeing how current agrometerological conditions measure up to historic events helps analysts and decision-makers judge the potential impact that anomalous rainfall and temperatures will have on the availability and accessibility of food and water resources. We present results from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), which is used to produce multi-model and rainfall ensembles of the water balance over semi-arid Africa from 1982-2014. The ensemble approach allows us to assess confidence in our estimates, which is critical given that food and water insecure regions in Africa are data-poor are characterized by complex interactions and feedbacks that cause deterministic hydrologic modeling approaches to fall short. We then use the ensemble of water balance estimates to calculate drought severity (derived from modeled soil moisture), and the Water Requirement Satisfaction Index (a function of atmospheric water demand). We compare these indices to the GIMMS 30-year vegetation data product from AVHRR, and the ESA ECV 30 year microwave soil moisture. These historical time series (with confidence bounds) allow us to improve our quantitative understanding of drought thresholds, to explore sources of parameter and model uncertainty, and to better contextualize current operational drought monitoring efforts in Africa.
NASA Astrophysics Data System (ADS)
Wolfsberg, A.; Kang, Q.; Li, C.; Ruskauff, G.; Bhark, E.; Freeman, E.; Prothro, L.; Drellack, S.
2007-12-01
The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result of the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The
Sig Drellack, Lance Prothro
2007-12-01
The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result of the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The
Gilbert, Jennifer A; Meyers, Lauren Ancel; Galvani, Alison P; Townsend, Jeffrey P
2014-03-01
Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions. However, typical sensitivity analyses of deterministic dynamic infectious disease models focus on model architecture and the relative importance of parameters but neglect parameter uncertainty when reporting model predictions. Consequently, model results that identify point estimates of intervention levels necessary to terminate transmission yield limited insight into the probability of success. We apply probabilistic uncertainty analysis to a dynamic model of influenza transmission and assess global uncertainty in outcome. We illustrate that when parameter uncertainty is not incorporated into outcome estimates, levels of vaccination and treatment predicted to prevent an influenza epidemic will only have an approximately 50% chance of terminating transmission and that sensitivity analysis alone is not sufficient to obtain this information. We demonstrate that accounting for parameter uncertainty yields probabilities of epidemiological outcomes based on the degree to which data support the range of model predictions. Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health. PMID:24593920
Uncertainty analysis of multi-rate kinetics of uranium desorption from sediments
Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.; Zhang, Guannan
2014-01-01
A multi-rate expression for uranyl [U(VI)] surface complexation reactions has been proposed to describe diffusion-limited U(VI) sorption/desorption in heterogeneous subsurface sediments. An important assumption in the rate expression is that its rate constants follow a certain type probability distribution. In this paper, a Bayes-based, Differential Evolution Markov Chain method was used to assess the distribution assumption and to analyze parameter and model structure uncertainties. U(VI) desorption from a contaminated sediment at the US Hanford 300 Area, Washington was used as an example for detail analysis. The results indicated that: 1) the rate constants in the multi-rate expression contain uneven uncertainties with slower rate constants having relative larger uncertainties; 2) the lognormal distribution is an effective assumption for the rate constants in the multi-rate model to simualte U(VI) desorption; 3) however, long-term prediction and its uncertainty may be significantly biased by the lognormal assumption for the smaller rate constants; and 4) both parameter and model structure uncertainties can affect the extrapolation of the multi-rate model with a larger uncertainty from the model structure. The results provide important insights into the factors contributing to the uncertainties of the multi-rate expression commonly used to describe the diffusion or mixing-limited sorption/desorption of both organic and inorganic contaminants in subsurface sediments.
Unscented transform-based uncertainty analysis of rotating coil transducers for field mapping
NASA Astrophysics Data System (ADS)
Arpaia, P.; De Matteis, E.; Schiano Lo Moriello, R.
2016-03-01
The uncertainty of a rotating coil transducer for magnetic field mapping is analyzed. Unscented transform and statistical design of experiments are combined to determine magnetic field expectation, standard uncertainty, and separate contributions of the uncertainty sources. For nonlinear measurement models, the unscented transform-based approach is more error-proof than the linearization underlying the "Guide to the expression of Uncertainty in Measurements" (GUMs), owing to the absence of model approximations and derivatives computation. When GUM assumptions are not met, the deterministic sampling strategy strongly reduces computational burden with respect to Monte Carlo-based methods proposed by the Supplement 1 of the GUM. Furthermore, the design of experiments and the associated statistical analysis allow the uncertainty sources domain to be explored efficiently, as well as their significance and single contributions to be assessed for an effective setup configuration. A straightforward experimental case study highlights that a one-order-of-magnitude reduction in the relative uncertainty of the coil area produces a decrease in uncertainty of the field mapping transducer by a factor of 25 with respect to the worst condition. Moreover, about 700 trials and the related processing achieve results corresponding to 5 × 106 brute-force Monte Carlo simulations.
Unscented transform-based uncertainty analysis of rotating coil transducers for field mapping.
Arpaia, P; De Matteis, E; Schiano Lo Moriello, R
2016-03-01
The uncertainty of a rotating coil transducer for magnetic field mapping is analyzed. Unscented transform and statistical design of experiments are combined to determine magnetic field expectation, standard uncertainty, and separate contributions of the uncertainty sources. For nonlinear measurement models, the unscented transform-based approach is more error-proof than the linearization underlying the "Guide to the expression of Uncertainty in Measurements" (GUMs), owing to the absence of model approximations and derivatives computation. When GUM assumptions are not met, the deterministic sampling strategy strongly reduces computational burden with respect to Monte Carlo-based methods proposed by the Supplement 1 of the GUM. Furthermore, the design of experiments and the associated statistical analysis allow the uncertainty sources domain to be explored efficiently, as well as their significance and single contributions to be assessed for an effective setup configuration. A straightforward experimental case study highlights that a one-order-of-magnitude reduction in the relative uncertainty of the coil area produces a decrease in uncertainty of the field mapping transducer by a factor of 25 with respect to the worst condition. Moreover, about 700 trials and the related processing achieve results corresponding to 5 × 10(6) brute-force Monte Carlo simulations. PMID:27036810
Uncertainty Analysis Using BMA for Hydrologic Projections under Future Climate Change
NASA Astrophysics Data System (ADS)
Beigi, E.; Tsai, F. T. C.
2014-12-01
This study conducts uncertainty analysis on future region-scale hydrologic projections under the uncertain climate change projections of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report. The choice of the global climate models (GCMs), the greenhouse gas concentration trajectories and the GCM initial conditions are considered to be three major sources of uncertainty in the projected precipitation and temperature. This study uses the 133 sets of downscaled precipitation and temperature of the 1/8 degree BCCA projections for uncertainty analysis, which are derived from 22 CMIP5 GCMs, four emissions paths (RCP2.6, RCP4.5, RCP6.0, and RCP8.5), and different number of GCM initial conditions. The downscaled precipitation and temperature are used in the hydrologic model HELP3 to derive high-resolution spatiotemporal distributions of surface runoff, evapotranspiration and groundwater recharge from 2010 to 2099. The hierarchical Bayesian model averaging (HBMA) method is adopted to segregate and prioritize the three sources of climate projection uncertainty, obtain the ensemble mean of hydrologic projections, and quantify the hydrologic projection uncertainty. Posterior model probabilities in the BMA are calculated based on a performance criterion and a convergence criterion. The performance criterion is the GCM performance in reproducing the historical climate. The convergence criterion is the closeness of GCM simulation to the ensemble mean of future projections. Different likelihood functions are used to investigate their impacts on the posterior model probabilities. The methodology is applied to the study of hydrologic projections and uncertainty for the area of the Southern Hills aquifer system, southwestern Mississippi and southeastern Louisiana. The study area is divided into more than 2.6 million subdivisions by intersecting various datasets through the ArcGIS. The analysis is computationally intensive. Parallel computation is used to
Pawel, David; Leggett, Richard Wayne; Eckerman, Keith F; Nelson, Christopher
2007-01-01
Federal Guidance Report No. 13 (FGR 13) provides risk coefficients for estimation of the risk of cancer due to low-level exposure to each of more than 800 radionuclides. Uncertainties in risk coefficients were quantified in FGR 13 for 33 cases (exposure to each of 11 radionuclides by each of three exposure pathways) on the basis of sensitivity analyses in which various combinations of plausible biokinetic, dosimetric, and radiation risk models were used to generate alternative risk coefficients. The present report updates the uncertainty analysis in FGR 13 for the cases of inhalation and ingestion of radionuclides and expands the analysis to all radionuclides addressed in that report. The analysis indicates that most risk coefficients for inhalation or ingestion of radionuclides are determined within a factor of 5 or less by current information. That is, application of alternate plausible biokinetic and dosimetric models and radiation risk models (based on the linear, no-threshold hypothesis with an adjustment for the dose and dose rate effectiveness factor) is unlikely to change these coefficients by more than a factor of 5. In this analysis the assessed uncertainty in the radiation risk model was found to be the main determinant of the uncertainty category for most risk coefficients, but conclusions concerning the relative contributions of risk and dose models to the total uncertainty in a risk coefficient may depend strongly on the method of assessing uncertainties in the risk model.
NASA Astrophysics Data System (ADS)
He, Honglin; Liu, Min; Xiao, Xiangming; Ren, Xiaoli; Zhang, Li; Sun, Xiaomin; Yang, Yuanhe; Li, Yingnian; Zhao, Liang; Shi, Peili; Du, Mingyuan; Ma, Yaoming; Ma, Mingguo; Zhang, Yu; Yu, Guirui
2014-03-01
Gross primary production (GPP) is an important parameter for carbon cycle and climate change research. Previous estimations of GPP on the Tibetan Plateau were usually reported without quantitative uncertainty analyses. This study sought to quantify the uncertainty and its partitioning in GPP estimation across Tibetan alpine grasslands during 2003-2008 with the modified Vegetation Photosynthesis Model (VPM). Monte Carlo analysis was used to provide a quantitative assessment of the uncertainty in model simulations, and Sobol' variance decomposition method was applied to determine the relative contribution of each source of uncertainty to the total uncertainty. The results showed that the modified VPM successfully reproduced the seasonal dynamics and magnitude of GPP of 10 flux tower sites on the plateau (R2 = 0.77 - 0.95, p < 0.001). The 6 year mean GPP in Tibetan alpine grasslands was estimated at 223.3 Tg C yr-1 (312.3 g C m-2 yr-1). The mean annual GPP increased from western to eastern plateau, with the increase of annual temperature and precipitation and the decrease of elevation, while the decrease of GPP from southern to northern plateau was primarily driven by air temperature. Furthermore, the mean relative uncertainty of the annual GPP was 18.30%, with larger uncertainty occurring in regions with lower GPP. Photosynthetic active radiation, enhanced vegetation index, and the maximum light use efficiency (LUE) are the primary sources of uncertainty in GPP estimation, contributing 36.84%, 26.86%, and 21.99%, respectively. This emphasizes the importance of uncertainty in driving variables as well as that of maximum LUE in LUE model simulation.
Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation
NASA Astrophysics Data System (ADS)
Ren, Xiaoli; He, Honglin; Moore, David J. P.; Zhang, Li; Liu, Min; Li, Fan; Yu, Guirui; Wang, Huimin
2013-12-01
Estimating the exchanges of carbon and water between vegetation and the atmosphere requires process-based ecosystem models; however, uncertainty in model predictions is inevitable due to the uncertainties in model structure, model parameters, and driving variables. This paper proposes a methodological framework for analyzing prediction uncertainty of ecosystem models caused by parameters and applies it in Qianyanzhou subtropical coniferous plantation using the Simplified Photosynthesis and Evapotranspiration model. We selected 20 key parameters from 42 parameters of the model using one-at-a-time sensitivity analysis method and estimated their posterior distributions using Markov Chain Monte Carlo technique. Prediction uncertainty was quantified through Monte Carlo method and partitioned using Sobol' method by decomposing the total variance of model predictions into different components. The uncertainty in predicted net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (RE), evapotranspiration (ET), and transpiration (T), defined as the coefficient of variation, was 61.0%, 20.6%, 12.7%, 14.2%, and 19.9%, respectively. Modeled carbon and water fluxes were highly sensitive to two parameters, maximum net CO2 assimilation rate (Amax) and specific leaf weight (SLWC). They contributed more than two thirds of the uncertainty in predicted NEE, GPP, ET, and T and almost one third of the uncertainty in predicted RE, which should be focused on in further efforts to reduce uncertainty. The results indicated a direction for future model development and data collection. Although there were still limitations in the framework illustrated here, it did provide a paradigm for systematic quantification of ecosystem model prediction uncertainty.
Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model
NASA Astrophysics Data System (ADS)
Wang, Shitao; Iskandarani, Mohamed; Srinivasan, Ashwanth; Thacker, W. Carlisle; Winokur, Justin; Knio, Omar M.
2016-05-01
Polynomial Chaos expansions are used to analyze uncertainties in an integral oil-gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet-size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos-based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.
Development and Uncertainty Analysis of an Automatic Testing System for Diffusion Pump Performance
NASA Astrophysics Data System (ADS)
Zhang, S. W.; Liang, W. S.; Zhang, Z. J.
A newly developed automatic testing system used in laboratory for diffusion pump performance measurement is introduced in this paper. By using two optical fiber sensors to indicate the oil level in glass-buret and a needle valve driven by a stepper motor to regulate the pressure in the test dome, the system can automatically test the ultimate pressure and pumping speed of a diffusion pump in accordance with ISO 1608. The uncertainty analysis theory is applied to analyze pumping speed measurement results. Based on the test principle and system structure, it is studied how much influence each component and test step contributes to the final uncertainty. According to differential method, the mathematical model for systematic uncertainty transfer function is established. Finally, by case study, combined uncertainties of manual operation and automatic operation are compared with each other (6.11% and 5.87% respectively). The reasonableness and practicality of this newly developed automatic testing system is proved.
Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis
Fish, Robert; Austin, Zoe; Christley, Robert; Haygarth, Philip M.; Heathwaite, Louise A.; Latham, Sophia; Medd, William; Mort, Maggie; Oliver, David M.; Pickup, Roger; Wastling, Jonathan M.; Wynne, Brian
2011-01-01
Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy. PMID:21624922
J.C. Helton
2000-04-19
This presentation investigates the incorporation of uncertainty and variability of drip shield and waste package degradation in analyses with the Waste Package Degradation (WAPDEG) program (CRWMS M&O 1998). This plan was developed in accordance with Development Plan TDP-EBS-MD-000020 (CRWMS M&O 1999a). Topics considered include (1) the nature of uncertainty and variability (Section 6.1), (2) incorporation of variability and uncertainty into analyses involving individual patches, waste packages, groups of waste packages, and the entire repository (Section 6.2), (3) computational strategies (Section 6.3), (4) incorporation of multiple waste package layers (i.e., drip shield, Alloy 22, and stainless steel) into an analysis (Section 6.4), (5) uncertainty in the characterization of variability (Section 6.5), and (6) Gaussian variance partitioning (Section 6.6). The presentation ends with a brief concluding discussion (Section 7).
IAEA CRP on HTGR Uncertainty Analysis: Benchmark Definition and Test Cases
Gerhard Strydom; Frederik Reitsma; Hans Gougar; Bismark Tyobeka; Kostadin Ivanov
2012-11-01
Uncertainty and sensitivity studies are essential elements of the reactor simulation code verification and validation process. Although several international uncertainty quantification activities have been launched in recent years in the LWR, BWR and VVER domains (e.g. the OECD/NEA BEMUSE program [1], from which the current OECD/NEA LWR Uncertainty Analysis in Modelling (UAM) benchmark [2] effort was derived), the systematic propagation of uncertainties in cross-section, manufacturing and model parameters for High Temperature Reactor (HTGR) designs has not been attempted yet. This paper summarises the scope, objectives and exercise definitions of the IAEA Coordinated Research Project (CRP) on HTGR UAM [3]. Note that no results will be included here, as the HTGR UAM benchmark was only launched formally in April 2012, and the specification is currently still under development.
Uncertainty analysis for 3D geological modeling using the Kriging variance
NASA Astrophysics Data System (ADS)
Choi, Yosoon; Choi, Younjung; Park, Sebeom; Um, Jeong-Gi
2014-05-01
The credible estimation of geological properties is critical in many geosciences fields including the geotechnical engineering, environmental engineering, mining engineering and petroleum engineering. Many interpolation techniques have been developed to estimate the geological properties from limited sampling data such as borehole logs. The Kriging is an interpolation technique that gives the best linear unbiased prediction of the intermediate values. It also provides the Kriging variance which quantifies the uncertainty of the kriging estimates. This study provides a new method to analyze the uncertainty in 3D geological modeling using the Kriging variance. The cut-off values determined by the Kriging variance were used to effectively visualize the 3D geological models with different confidence levels. This presentation describes the method for uncertainty analysis and a case study which evaluates the amount of recoverable resources by considering the uncertainty.
Gauntt, Randall O.; Mattie, Patrick D.; Bixler, Nathan E.; Ross, Kyle; Cardoni, Jeffrey N; Kalinich, Donald A.; Osborn, Douglas M.; Sallaberry, Cedric Jean-Marie; Ghosh, S. Tina
2014-02-01
This paper describes the knowledge advancements from the uncertainty analysis for the State-of- the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout accident scenario at the Peach Bottom Atomic Power Station. This work assessed key MELCOR and MELCOR Accident Consequence Code System, Version 2 (MACCS2) modeling uncertainties in an integrated fashion to quantify the relative importance of each uncertain input on potential accident progression, radiological releases, and off-site consequences. This quantitative uncertainty analysis provides measures of the effects on consequences, of each of the selected uncertain parameters both individually and in interaction with other parameters. The results measure the model response (e.g., variance in the output) to uncertainty in the selected input. Investigation into the important uncertain parameters in turn yields insights into important phenomena for accident progression and off-site consequences. This uncertainty analysis confirmed the known importance of some parameters, such as failure rate of the Safety Relief Valve in accident progression modeling and the dry deposition velocity in off-site consequence modeling. The analysis also revealed some new insights, such as dependent effect of cesium chemical form for different accident progressions. (auth)