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Sample records for avt-147 computational uncertainty

  1. Technical Evaluation Report for Symposium AVT-147: Computational Uncertainty in Military Vehicle Design

    NASA Technical Reports Server (NTRS)

    Radespiel, Rolf; Hemsch, Michael J.

    2007-01-01

    The complexity of modern military systems, as well as the cost and difficulty associated with experimentally verifying system and subsystem design makes the use of high-fidelity based simulation a future alternative for design and development. The predictive ability of such simulations such as computational fluid dynamics (CFD) and computational structural mechanics (CSM) have matured significantly. However, for numerical simulations to be used with confidence in design and development, quantitative measures of uncertainty must be available. The AVT 147 Symposium has been established to compile state-of-the art methods of assessing computational uncertainty, to identify future research and development needs associated with these methods, and to present examples of how these needs are being addressed and how the methods are being applied. Papers were solicited that address uncertainty estimation associated with high fidelity, physics-based simulations. The solicitation included papers that identify sources of error and uncertainty in numerical simulation from either the industry perspective or from the disciplinary or cross-disciplinary research perspective. Examples of the industry perspective were to include how computational uncertainty methods are used to reduce system risk in various stages of design or development.

  2. Uncertainty in Computational Aerodynamics

    NASA Technical Reports Server (NTRS)

    Luckring, J. M.; Hemsch, M. J.; Morrison, J. H.

    2003-01-01

    An approach is presented to treat computational aerodynamics as a process, subject to the fundamental quality assurance principles of process control and process improvement. We consider several aspects affecting uncertainty for the computational aerodynamic process and present a set of stages to determine the level of management required to meet risk assumptions desired by the customer of the predictions.

  3. Credible Computations: Standard and Uncertainty

    NASA Technical Reports Server (NTRS)

    Mehta, Unmeel B.; VanDalsem, William (Technical Monitor)

    1995-01-01

    The discipline of computational fluid dynamics (CFD) is at a crossroad. Most of the significant advances related to computational methods have taken place. The emphasis is now shifting from methods to results. Significant efforts are made in applying CFD to solve design problems. The value of CFD results in design depends on the credibility of computed results for the intended use. The process of establishing credibility requires a standard so that there is a consistency and uniformity in this process and in the interpretation of its outcome. The key element for establishing the credibility is the quantification of uncertainty. This paper presents salient features of a proposed standard and a procedure for determining the uncertainty. A customer of CFD products - computer codes and computed results - expects the following: A computer code in terms of its logic, numerics, and fluid dynamics and the results generated by this code are in compliance with specified requirements. This expectation is fulfilling by verification and validation of these requirements. The verification process assesses whether the problem is solved correctly and the validation process determines whether the right problem is solved. Standards for these processes are recommended. There is always some uncertainty, even if one uses validated models and verified computed results. The value of this uncertainty is important in the design process. This value is obtained by conducting a sensitivity-uncertainty analysis. Sensitivity analysis is generally defined as the procedure for determining the sensitivities of output parameters to input parameters. This analysis is a necessary step in the uncertainty analysis, and the results of this analysis highlight which computed quantities and integrated quantities in computations need to be determined accurately and which quantities do not require such attention. Uncertainty analysis is generally defined as the analysis of the effect of the uncertainties

  4. Numerical uncertainty in computational engineering and physics

    SciTech Connect

    Hemez, Francois M

    2009-01-01

    Obtaining a solution that approximates ordinary or partial differential equations on a computational mesh or grid does not necessarily mean that the solution is accurate or even 'correct'. Unfortunately assessing the quality of discrete solutions by questioning the role played by spatial and temporal discretizations generally comes as a distant third to test-analysis comparison and model calibration. This publication is contributed to raise awareness of the fact that discrete solutions introduce numerical uncertainty. This uncertainty may, in some cases, overwhelm in complexity and magnitude other sources of uncertainty that include experimental variability, parametric uncertainty and modeling assumptions. The concepts of consistency, convergence and truncation error are overviewed to explain the articulation between the exact solution of continuous equations, the solution of modified equations and discrete solutions computed by a code. The current state-of-the-practice of code and solution verification activities is discussed. An example in the discipline of hydro-dynamics illustrates the significant effect that meshing can have on the quality of code predictions. A simple method is proposed to derive bounds of solution uncertainty in cases where the exact solution of the continuous equations, or its modified equations, is unknown. It is argued that numerical uncertainty originating from mesh discretization should always be quantified and accounted for in the overall uncertainty 'budget' that supports decision-making for applications in computational physics and engineering.

  5. Uncertainty and error in computational simulations

    SciTech Connect

    Oberkampf, W.L.; Diegert, K.V.; Alvin, K.F.; Rutherford, B.M.

    1997-10-01

    The present paper addresses the question: ``What are the general classes of uncertainty and error sources in complex, computational simulations?`` This is the first step of a two step process to develop a general methodology for quantitatively estimating the global modeling and simulation uncertainty in computational modeling and simulation. The second step is to develop a general mathematical procedure for representing, combining and propagating all of the individual sources through the simulation. The authors develop a comprehensive view of the general phases of modeling and simulation. The phases proposed are: conceptual modeling of the physical system, mathematical modeling of the system, discretization of the mathematical model, computer programming of the discrete model, numerical solution of the model, and interpretation of the results. This new view is built upon combining phases recognized in the disciplines of operations research and numerical solution methods for partial differential equations. The characteristics and activities of each of these phases is discussed in general, but examples are given for the fields of computational fluid dynamics and heat transfer. They argue that a clear distinction should be made between uncertainty and error that can arise in each of these phases. The present definitions for uncertainty and error are inadequate and. therefore, they propose comprehensive definitions for these terms. Specific classes of uncertainty and error sources are then defined that can occur in each phase of modeling and simulation. The numerical sources of error considered apply regardless of whether the discretization procedure is based on finite elements, finite volumes, or finite differences. To better explain the broad types of sources of uncertainty and error, and the utility of their categorization, they discuss a coupled-physics example simulation.

  6. Some Aspects of uncertainty in computational fluid dynamics results

    NASA Technical Reports Server (NTRS)

    Mehta, U. B.

    1991-01-01

    Uncertainties are inherent in computational fluid dynamics (CFD). These uncertainties need to be systematically addressed and managed. Sources of these uncertainty analysis are discussed. Some recommendations are made for quantification of CFD uncertainties. A practical method of uncertainty analysis is based on sensitivity analysis. When CFD is used to design fluid dynamic systems, sensitivity-uncertainty analysis is essential.

  7. Probabilistic numerics and uncertainty in computations

    PubMed Central

    Hennig, Philipp; Osborne, Michael A.; Girolami, Mark

    2015-01-01

    We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations. PMID:26346321

  8. Estimating Uncertainties in Statistics Computed from DNS

    NASA Astrophysics Data System (ADS)

    Malaya, Nicholas; Oliver, Todd; Ulerich, Rhys; Moser, Robert

    2013-11-01

    Rigorous assessment of uncertainty is crucial to the utility of DNS results. Uncertainties in the computed statistics arise from two sources: finite sampling and the discretization of the Navier-Stokes equations. Due to the presence of non-trivial sampling error, standard techniques for estimating discretization error (such as Richardson Extrapolation) fail or are unreliable. This talk provides a systematic and unified approach for estimating these errors. First, a sampling error estimator that accounts for correlation in the input data is developed. Then, this sampling error estimate is used as an input to a probabilistic extension of Richardson extrapolation in order to characterize the discretization error. These techniques are used to investigate the sampling and discretization errors in the DNS of a wall-bounded turbulent flow at Reτ = 180. We will show a well-resolved DNS simulation which, for the centerline velocity, possesses 0.02% sampling error and discretization errors of 0.003%. These results imply that standard resolution heuristics for DNS accurately predict required grid sizes. This work is supported by the Department of Energy [National Nuclear Security Administration] under Award Number [DE-FC52-08NA28615].

  9. Methodology for characterizing modeling and discretization uncertainties in computational simulation

    SciTech Connect

    ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.

    2000-03-01

    This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

  10. Applying uncertainty quantification to multiphase flow computational fluid dynamics

    SciTech Connect

    Gel, A; Garg, R; Tong, C; Shahnam, M; Guenther, C

    2013-07-01

    Multiphase computational fluid dynamics plays a major role in design and optimization of fossil fuel based reactors. There is a growing interest in accounting for the influence of uncertainties associated with physical systems to increase the reliability of computational simulation based engineering analysis. The U.S. Department of Energy's National Energy Technology Laboratory (NETL) has recently undertaken an initiative to characterize uncertainties associated with computer simulation of reacting multiphase flows encountered in energy producing systems such as a coal gasifier. The current work presents the preliminary results in applying non-intrusive parametric uncertainty quantification and propagation techniques with NETL's open-source multiphase computational fluid dynamics software MFIX. For this purpose an open-source uncertainty quantification toolkit, PSUADE developed at the Lawrence Livermore National Laboratory (LLNL) has been interfaced with MFIX software. In this study, the sources of uncertainty associated with numerical approximation and model form have been neglected, and only the model input parametric uncertainty with forward propagation has been investigated by constructing a surrogate model based on data-fitted response surface for a multiphase flow demonstration problem. Monte Carlo simulation was employed for forward propagation of the aleatory type input uncertainties. Several insights gained based on the outcome of these simulations are presented such as how inadequate characterization of uncertainties can affect the reliability of the prediction results. Also a global sensitivity study using Sobol' indices was performed to better understand the contribution of input parameters to the variability observed in response variable.

  11. Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models

    EPA Science Inventory

    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...

  12. Uncertainty and Intelligence in Computational Stochastic Mechanics

    NASA Technical Reports Server (NTRS)

    Ayyub, Bilal M.

    1996-01-01

    Classical structural reliability assessment techniques are based on precise and crisp (sharp) definitions of failure and non-failure (survival) of a structure in meeting a set of strength, function and serviceability criteria. These definitions are provided in the form of performance functions and limit state equations. Thus, the criteria provide a dichotomous definition of what real physical situations represent, in the form of abrupt change from structural survival to failure. However, based on observing the failure and survival of real structures according to the serviceability and strength criteria, the transition from a survival state to a failure state and from serviceability criteria to strength criteria are continuous and gradual rather than crisp and abrupt. That is, an entire spectrum of damage or failure levels (grades) is observed during the transition to total collapse. In the process, serviceability criteria are gradually violated with monotonically increasing level of violation, and progressively lead into the strength criteria violation. Classical structural reliability methods correctly and adequately include the ambiguity sources of uncertainty (physical randomness, statistical and modeling uncertainty) by varying amounts. However, they are unable to adequately incorporate the presence of a damage spectrum, and do not consider in their mathematical framework any sources of uncertainty of the vagueness type. Vagueness can be attributed to sources of fuzziness, unclearness, indistinctiveness, sharplessness and grayness; whereas ambiguity can be attributed to nonspecificity, one-to-many relations, variety, generality, diversity and divergence. Using the nomenclature of structural reliability, vagueness and ambiguity can be accounted for in the form of realistic delineation of structural damage based on subjective judgment of engineers. For situations that require decisions under uncertainty with cost/benefit objectives, the risk of failure should

  13. Automated uncertainty analysis methods in the FRAP computer codes. [PWR

    SciTech Connect

    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.

  14. MOUSE (MODULAR ORIENTED UNCERTAINTY SYSTEM): A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM (FOR MICRO- COMPUTERS)

    EPA Science Inventory

    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...

  15. Propagation of Computational Uncertainty Using the Modern Design of Experiments

    NASA Technical Reports Server (NTRS)

    DeLoach, Richard

    2007-01-01

    This paper describes the use of formally designed experiments to aid in the error analysis of a computational experiment. A method is described by which the underlying code is approximated with relatively low-order polynomial graduating functions represented by truncated Taylor series approximations to the true underlying response function. A resource-minimal approach is outlined by which such graduating functions can be estimated from a minimum number of case runs of the underlying computational code. Certain practical considerations are discussed, including ways and means of coping with high-order response functions. The distributional properties of prediction residuals are presented and discussed. A practical method is presented for quantifying that component of the prediction uncertainty of a computational code that can be attributed to imperfect knowledge of independent variable levels. This method is illustrated with a recent assessment of uncertainty in computational estimates of Space Shuttle thermal and structural reentry loads attributable to ice and foam debris impact on ascent.

  16. Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics

    NASA Technical Reports Server (NTRS)

    Zhu, J. Z.; He, Guowei; Bushnell, Dennis M. (Technical Monitor)

    2002-01-01

    Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. Verification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically provide quantitative information about the reliability of the numerical simulation by estimating numerical approximation error, computational model induced errors and the uncertainties contained in the mathematical models so that the reliability of the numerical simulation can be verified and validated. We also propose to develop and implement methodologies and techniques that can control the error and uncertainty during the numerical simulation so that the reliability of the numerical simulation can be improved.

  17. Sensitivity and uncertainty studies of the CRAC2 computer code.

    PubMed

    Kocher, D C; Ward, R C; Killough, G G; Dunning, D E; Hicks, B B; Hosker, R P; Ku, J Y; Rao, K S

    1987-12-01

    We have studied the sensitivity of health impacts from nuclear reactor accidents, as predicted by the CRAC2 computer code, to the following sources of uncertainty: (1) the model for plume rise, (2) the model for wet deposition, (3) the meteorological bin-sampling procedure for selecting weather sequences with rain, (4) the dose conversion factors for inhalation as affected by uncertainties in the particle size of the carrier aerosol and the clearance rates of radionuclides from the respiratory tract, (5) the weathering half-time for external ground-surface exposure, and (6) the transfer coefficients for terrestrial foodchain pathways. Predicted health impacts usually showed little sensitivity to use of an alternative plume-rise model or a modified rain-bin structure in bin-sampling. Health impacts often were quite sensitive to use of an alternative wet-deposition model in single-trial runs with rain during plume passage, but were less sensitive to the model in bin-sampling runs. Uncertainties in the inhalation dose conversion factors had important effects on early injuries in single-trial runs. Latent cancer fatalities were moderately sensitive to uncertainties in the weathering half-time for ground-surface exposure, but showed little sensitivity to the transfer coefficients for terrestrial foodchain pathways. Sensitivities of CRAC2 predictions to uncertainties in the models and parameters also depended on the magnitude of the source term, and some of the effects on early health effects were comparable to those that were due only to selection of different sets of weather sequences in bin-sampling. PMID:3444936

  18. Uncertainty of microwave radiative transfer computations in rain

    NASA Astrophysics Data System (ADS)

    Hong, Sung Wook

    Currently, the effect of the vertical resolution on the brightness temperature (BT) has not been examined in depth. The uncertainty of the freezing level (FL) retrieved using two different satellites' data is large. Various radiative transfer (RT) codes yield different BTs in strong scattering conditions. The purposes of this research were: (1) to understand the uncertainty of the BT contributed by the vertical resolution numerically and analytically; (2) to reduce the uncertainty of the FL retrieval using new thermodynamic observations; and (3) to investigate the characteristics of four different RT codes. Firstly, a plane-parallel RT Model (RTM) of n layers in light rainfall was used for the analytical and computational derivation of the vertical resolution effect on the BT. Secondly, a new temperature profile based on observations was absorbed in the Texas A&M University (TAMU) algorithm. The Precipitation Radar (PR) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data were utilized for the improved FL retrieval. Thirdly, the TAMU, Eddington approximation (EDD), Discrete Ordinate, and backward Monte Carlo codes were compared under various view angles, rain rates, FLs, frequencies, and surface properties. The uncertainty of the BT decreased as the number of layers increased. The uncertainty was due to the optical thickness rather than due to relative humidity, pressure distribution, water vapor, and temperature profile. The mean TMI FL showed a good agreement with mean bright band height. A new temperature profile reduced the uncertainty of the TMI FL by about 10%. The differences of the BTs among the four different RT codes were within 1 K at the current sensor view angle over the entire dynamic rain rate range of 10-37 GHz. The differences between the TAMU and EDD solutions were less than 0.5 K for the specular surface. In conclusion, this research suggested the vertical resolution should be considered as a parameter in the forward model

  19. Statistical models and computation to evaluate measurement uncertainty

    NASA Astrophysics Data System (ADS)

    Possolo, Antonio

    2014-08-01

    In the course of the twenty years since the publication of the Guide to the Expression of Uncertainty in Measurement (GUM), the recognition has been steadily growing of the value that statistical models and statistical computing bring to the evaluation of measurement uncertainty, and of how they enable its probabilistic interpretation. These models and computational methods can address all the problems originally discussed and illustrated in the GUM, and enable addressing other, more challenging problems, that measurement science is facing today and that it is expected to face in the years ahead. These problems that lie beyond the reach of the techniques in the GUM include (i) characterizing the uncertainty associated with the assignment of value to measurands of greater complexity than, or altogether different in nature from, the scalar or vectorial measurands entertained in the GUM: for example, sequences of nucleotides in DNA, calibration functions and optical and other spectra, spatial distribution of radioactivity over a geographical region, shape of polymeric scaffolds for bioengineering applications, etc; (ii) incorporating relevant information about the measurand that predates or is otherwise external to the measurement experiment; (iii) combining results from measurements of the same measurand that are mutually independent, obtained by different methods or produced by different laboratories. This review of several of these statistical models and computational methods illustrates some of the advances that they have enabled, and in the process invites a reflection on the interesting historical fact that these very same models and methods, by and large, were already available twenty years ago, when the GUM was first published—but then the dialogue between metrologists, statisticians and mathematicians was still in bud. It is in full bloom today, much to the benefit of all.

  20. A High Performance Bayesian Computing Framework for Spatiotemporal Uncertainty Modeling

    NASA Astrophysics Data System (ADS)

    Cao, G.

    2015-12-01

    All types of spatiotemporal measurements are subject to uncertainty. With spatiotemporal data becomes increasingly involved in scientific research and decision making, it is important to appropriately model the impact of uncertainty. Quantitatively modeling spatiotemporal uncertainty, however, is a challenging problem considering the complex dependence and dataheterogeneities.State-space models provide a unifying and intuitive framework for dynamic systems modeling. In this paper, we aim to extend the conventional state-space models for uncertainty modeling in space-time contexts while accounting for spatiotemporal effects and data heterogeneities. Gaussian Markov Random Field (GMRF) models, also known as conditional autoregressive models, are arguably the most commonly used methods for modeling of spatially dependent data. GMRF models basically assume that a geo-referenced variable primarily depends on its neighborhood (Markov property), and the spatial dependence structure is described via a precision matrix. Recent study has shown that GMRFs are efficient approximation to the commonly used Gaussian fields (e.g., Kriging), and compared with Gaussian fields, GMRFs enjoy a series of appealing features, such as fast computation and easily accounting for heterogeneities in spatial data (e.g, point and areal). This paper represents each spatial dataset as a GMRF and integrates them into a state-space form to statistically model the temporal dynamics. Different types of spatial measurements (e.g., categorical, count or continuous), can be accounted for by according link functions. A fast alternative to MCMC framework, so-called Integrated Nested Laplace Approximation (INLA), was adopted for model inference.Preliminary case studies will be conducted to showcase the advantages of the described framework. In the first case, we apply the proposed method for modeling the water table elevation of Ogallala aquifer over the past decades. In the second case, we analyze the

  1. COMPUTATIONAL METHODS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS FOR ENVIRONMENTAL AND BIOLOGICAL MODELS

    EPA Science Inventory

    This work introduces a computationally efficient alternative method for uncertainty propagation, the Stochastic Response Surface Method (SRSM). The SRSM approximates uncertainties in model outputs through a series expansion in normal random variables (polynomial chaos expansion)...

  2. Estimating uncertainties in statistics computed from direct numerical simulation

    NASA Astrophysics Data System (ADS)

    Oliver, Todd A.; Malaya, Nicholas; Ulerich, Rhys; Moser, Robert D.

    2014-03-01

    Rigorous assessment of uncertainty is crucial to the utility of direct numerical simulation (DNS) results. Uncertainties in the computed statistics arise from two sources: finite statistical sampling and the discretization of the Navier-Stokes equations. Due to the presence of non-trivial sampling error, standard techniques for estimating discretization error (such as Richardson extrapolation) fail or are unreliable. This work provides a systematic and unified approach for estimating these errors. First, a sampling error estimator that accounts for correlation in the input data is developed. Then, this sampling error estimate is used as part of a Bayesian extension of Richardson extrapolation in order to characterize the discretization error. These methods are tested using the Lorenz equations and are shown to perform well. These techniques are then used to investigate the sampling and discretization errors in the DNS of a wall-bounded turbulent flow at Reτ ≈ 180. Both small (Lx/δ × Lz/δ = 4π × 2π) and large (Lx/δ × Lz/δ = 12π × 4π) domain sizes are investigated. For each case, a sequence of meshes was generated by first designing a "nominal" mesh using standard heuristics for wall-bounded simulations. These nominal meshes were then coarsened to generate a sequence of grid resolutions appropriate for the Bayesian Richardson extrapolation method. In addition, the small box case is computationally inexpensive enough to allow simulation on a finer mesh, enabling the results of the extrapolation to be validated in a weak sense. For both cases, it is found that while the sampling uncertainty is large enough to make the order of accuracy difficult to determine, the estimated discretization errors are quite small. This indicates that the commonly used heuristics provide adequate resolution for this class of problems. However, it is also found that, for some quantities, the discretization error is not small relative to sampling error, indicating that the

  3. Uncertainty quantification based on pillars of experiment, theory, and computation. Part II: Theory and computation

    NASA Astrophysics Data System (ADS)

    Elishakoff, I.; Sarlin, N.

    2016-06-01

    This paper deals with theoretical and computational aspects of different uncertainty calculi, introduced in Part I, specifically when the data is bounded by any of the following five figures: triangle; rectangle; parallelogram; ellipse or super ellipse. We consider elastic structures subjected to uncertainty, and evaluate the least favorable, maximum response and the most favorable, minimum response. Comparison is conducted between the treated uncertainty calculi with preference given to the one which predicts the least estimate for the favorable response. In considered elastic structures the solution or displacements is available analytically; in cases when analytical solution is absent purely numerical solution ought to be implemented. Such a case is now under development and will be published elsewhere.

  4. Computations of uncertainty mediate acute stress responses in humans

    PubMed Central

    de Berker, Archy O.; Rutledge, Robb B.; Mathys, Christoph; Marshall, Louise; Cross, Gemma F.; Dolan, Raymond J.; Bestmann, Sven

    2016-01-01

    The effects of stress are frequently studied, yet its proximal causes remain unclear. Here we demonstrate that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses. Subjects learned a probabilistic mapping between visual stimuli and electric shocks. Salivary cortisol confirmed that our stressor elicited changes in endocrine activity. Using a hierarchical Bayesian learning model, we quantified the relationship between the different forms of subjective task uncertainty and acute stress responses. Subjective stress, pupil diameter and skin conductance all tracked the evolution of irreducible uncertainty. We observed a coupling between emotional and somatic state, with subjective and physiological tuning to uncertainty tightly correlated. Furthermore, the uncertainty tuning of subjective and physiological stress predicted individual task performance, consistent with an adaptive role for stress in learning under uncertain threat. Our finding that stress responses are tuned to environmental uncertainty provides new insight into their generation and likely adaptive function. PMID:27020312

  5. Uncertainty Modeling of Pollutant Transport in Atmosphere and Aquatic Route Using Soft Computing

    SciTech Connect

    Datta, D.

    2010-10-26

    Hazardous radionuclides are released as pollutants in the atmospheric and aquatic environment (ATAQE) during the normal operation of nuclear power plants. Atmospheric and aquatic dispersion models are routinely used to assess the impact of release of radionuclide from any nuclear facility or hazardous chemicals from any chemical plant on the ATAQE. Effect of the exposure from the hazardous nuclides or chemicals is measured in terms of risk. Uncertainty modeling is an integral part of the risk assessment. The paper focuses the uncertainty modeling of the pollutant transport in atmospheric and aquatic environment using soft computing. Soft computing is addressed due to the lack of information on the parameters that represent the corresponding models. Soft-computing in this domain basically addresses the usage of fuzzy set theory to explore the uncertainty of the model parameters and such type of uncertainty is called as epistemic uncertainty. Each uncertain input parameters of the model is described by a triangular membership function.

  6. Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

    NASA Technical Reports Server (NTRS)

    Groves, Curtis; Ilie, Marcel; Schallhorn, Paul

    2014-01-01

    Spacecraft components may be damaged due to airflow produced by Environmental Control Systems (ECS). There are uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field around a spacecraft from the ECS System. This paper describes an approach to estimate the uncertainty in using CFD to predict the airflow speeds around an encapsulated spacecraft.

  7. Computational methods estimating uncertainties for profile reconstruction in scatterometry

    NASA Astrophysics Data System (ADS)

    Gross, H.; Rathsfeld, A.; Scholze, F.; Model, R.; Bär, M.

    2008-04-01

    The solution of the inverse problem in scatterometry, i.e. the determination of periodic surface structures from light diffraction patterns, is incomplete without knowledge of the uncertainties associated with the reconstructed surface parameters. With decreasing feature sizes of lithography masks, increasing demands on metrology techniques arise. Scatterometry as a non-imaging indirect optical method is applied to periodic line-space structures in order to determine geometric parameters like side-wall angles, heights, top and bottom widths and to evaluate the quality of the manufacturing process. The numerical simulation of the diffraction process is based on the finite element solution of the Helmholtz equation. The inverse problem seeks to reconstruct the grating geometry from measured diffraction patterns. Restricting the class of gratings and the set of measurements, this inverse problem can be reformulated as a non-linear operator equation in Euclidean spaces. The operator maps the grating parameters to the efficiencies of diffracted plane wave modes. We employ a Gauss-Newton type iterative method to solve this operator equation and end up minimizing the deviation of the measured efficiency or phase shift values from the simulated ones. The reconstruction properties and the convergence of the algorithm, however, is controlled by the local conditioning of the non-linear mapping and the uncertainties of the measured efficiencies or phase shifts. In particular, the uncertainties of the reconstructed geometric parameters essentially depend on the uncertainties of the input data and can be estimated by various methods. We compare the results obtained from a Monte Carlo procedure to the estimations gained from the approximative covariance matrix of the profile parameters close to the optimal solution and apply them to EUV masks illuminated by plane waves with wavelengths in the range of 13 nm.

  8. On the uncertainty of surface determination in x-ray computed tomography for dimensional metrology

    NASA Astrophysics Data System (ADS)

    Lifton, J. J.; Malcolm, A. A.; McBride, J. W.

    2015-03-01

    With x-ray computed tomography (CT) it is possible to evaluate the dimensions of an object’s internal and external features non-destructively. Dimensional measurements evaluated via x-ray CT require the object’s surfaces first be estimated; this work is concerned with evaluating the uncertainty of this surface estimate and how it impacts the uncertainty of fitted geometric features. The measurement uncertainty due to surface determination is evaluated through the use of a discrete ramp edge model and a Monte Carlo simulation. Based on the results of the Monte Carlo simulation the uncertainty structure of a coordinate set is estimated, allowing individual coordinate uncertainties to be propagated through the geometry fit to the final measurement result. The developed methodology enables the uncertainty due to surface determination to be evaluated for a given measurement task; the method is demonstrated for both measured and simulated data.

  9. Computer simulations in room acoustics: concepts and uncertainties.

    PubMed

    Vorländer, Michael

    2013-03-01

    Geometrical acoustics are used as a standard model for room acoustic design and consulting. Research on room acoustic simulation focuses on a more accurate modeling of propagation effects such as diffraction and other wave effects in rooms, and on scattering. Much progress was made in this field so that wave models also (for example, the boundary element method and the finite differences in time domain) can now be used for higher frequencies. The concepts and implementations of room simulation methods are briefly reviewed. After all, simulations in architectural acoustics are indeed powerful tools, but their reliability depends on the skills of the operator who has to create an adequate polygon model and has to choose the correct input data of boundary conditions such as absorption and scattering. Very little is known about the uncertainty of this input data. With the theory of error propagation of uncertainties it can be shown that prediction of reverberation times with accuracy better than the just noticeable difference requires input data in a quality which is not available from reverberation room measurements. PMID:23463991

  10. Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

    NASA Technical Reports Server (NTRS)

    Groves, Curtis E.

    2013-01-01

    Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around

  11. Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

    NASA Technical Reports Server (NTRS)

    Groves, Curtis Edward

    2014-01-01

    Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions

  12. Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

    NASA Technical Reports Server (NTRS)

    Groves, Curtis Edward

    2014-01-01

    Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics

  13. A general program to compute the multivariable stability margin for systems with parametric uncertainty

    NASA Technical Reports Server (NTRS)

    Sanchez Pena, Ricardo S.; Sideris, Athanasios

    1988-01-01

    A computer program implementing an algorithm for computing the multivariable stability margin to check the robust stability of feedback systems with real parametric uncertainty is proposed. The authors present in some detail important aspects of the program. An example is presented using lateral directional control system.

  14. Effect of Random Geometric Uncertainty on the Computational Design of a 3-D Flexible Wing

    NASA Technical Reports Server (NTRS)

    Gumbert, C. R.; Newman, P. A.; Hou, G. J.-W.

    2002-01-01

    The effect of geometric uncertainty due to statistically independent, random, normally distributed shape parameters is demonstrated in the computational design of a 3-D flexible wing. A first-order second-moment statistical approximation method is used to propagate the assumed input uncertainty through coupled Euler CFD aerodynamic / finite element structural codes for both analysis and sensitivity analysis. First-order sensitivity derivatives obtained by automatic differentiation are used in the input uncertainty propagation. These propagated uncertainties are then used to perform a robust design of a simple 3-D flexible wing at supercritical flow conditions. The effect of the random input uncertainties is shown by comparison with conventional deterministic design results. Sample results are shown for wing planform, airfoil section, and structural sizing variables.

  15. Assessment of uncertainties of the models used in thermal-hydraulic computer codes

    NASA Astrophysics Data System (ADS)

    Gricay, A. S.; Migrov, Yu. A.

    2015-09-01

    The article deals with matters concerned with the problem of determining the statistical characteristics of variable parameters (the variation range and distribution law) in analyzing the uncertainty and sensitivity of calculation results to uncertainty in input data. A comparative analysis of modern approaches to uncertainty in input data is presented. The need to develop an alternative method for estimating the uncertainty of model parameters used in thermal-hydraulic computer codes, in particular, in the closing correlations of the loop thermal hydraulics block, is shown. Such a method shall feature the minimal degree of subjectivism and must be based on objective quantitative assessment criteria. The method includes three sequential stages: selecting experimental data satisfying the specified criteria, identifying the key closing correlation using a sensitivity analysis, and carrying out case calculations followed by statistical processing of the results. By using the method, one can estimate the uncertainty range of a variable parameter and establish its distribution law in the above-mentioned range provided that the experimental information is sufficiently representative. Practical application of the method is demonstrated taking as an example the problem of estimating the uncertainty of a parameter appearing in the model describing transition to post-burnout heat transfer that is used in the thermal-hydraulic computer code KORSAR. The performed study revealed the need to narrow the previously established uncertainty range of this parameter and to replace the uniform distribution law in the above-mentioned range by the Gaussian distribution law. The proposed method can be applied to different thermal-hydraulic computer codes. In some cases, application of the method can make it possible to achieve a smaller degree of conservatism in the expert estimates of uncertainties pertinent to the model parameters used in computer codes.

  16. Computational Fluid Dynamics Uncertainty Analysis Applied to Heat Transfer over a Flat Plate

    NASA Technical Reports Server (NTRS)

    Groves, Curtis Edward; Ilie, Marcel; Schallhorn, Paul A.

    2013-01-01

    There have been few discussions on using Computational Fluid Dynamics (CFD) without experimental validation. Pairing experimental data, uncertainty analysis, and analytical predictions provides a comprehensive approach to verification and is the current state of the art. With pressed budgets, collecting experimental data is rare or non-existent. This paper investigates and proposes a method to perform CFD uncertainty analysis only from computational data. The method uses current CFD uncertainty techniques coupled with the Student-T distribution to predict the heat transfer coefficient over a at plate. The inputs to the CFD model are varied from a specified tolerance or bias error and the difference in the results are used to estimate the uncertainty. The variation in each input is ranked from least to greatest to determine the order of importance. The results are compared to heat transfer correlations and conclusions drawn about the feasibility of using CFD without experimental data. The results provide a tactic to analytically estimate the uncertainty in a CFD model when experimental data is unavailable

  17. Efficient Methods for Bayesian Uncertainty Analysis and Global Optimization of Computationally Expensive Environmental Models

    NASA Astrophysics Data System (ADS)

    Shoemaker, Christine; Espinet, Antoine; Pang, Min

    2015-04-01

    Models of complex environmental systems can be computationally expensive in order to describe the dynamic interactions of the many components over a sizeable time period. Diagnostics of these systems can include forward simulations of calibrated models under uncertainty and analysis of alternatives of systems management. This discussion will focus on applications of new surrogate optimization and uncertainty analysis methods to environmental models that can enhance our ability to extract information and understanding. For complex models, optimization and especially uncertainty analysis can require a large number of model simulations, which is not feasible for computationally expensive models. Surrogate response surfaces can be used in Global Optimization and Uncertainty methods to obtain accurate answers with far fewer model evaluations, which made the methods practical for computationally expensive models for which conventional methods are not feasible. In this paper we will discuss the application of the SOARS surrogate method for estimating Bayesian posterior density functions for model parameters for a TOUGH2 model of geologic carbon sequestration. We will also briefly discuss new parallel surrogate global optimization algorithm applied to two groundwater remediation sites that was implemented on a supercomputer with up to 64 processors. The applications will illustrate the use of these methods to predict the impact of monitoring and management on subsurface contaminants.

  18. Interpolation Method Needed for Numerical Uncertainty Analysis of Computational Fluid Dynamics

    NASA Technical Reports Server (NTRS)

    Groves, Curtis; Ilie, Marcel; Schallhorn, Paul

    2014-01-01

    Using Computational Fluid Dynamics (CFD) to predict a flow field is an approximation to the exact problem and uncertainties exist. There is a method to approximate the errors in CFD via Richardson's Extrapolation. This method is based off of progressive grid refinement. To estimate the errors in an unstructured grid, the analyst must interpolate between at least three grids. This paper describes a study to find an appropriate interpolation scheme that can be used in Richardson's extrapolation or other uncertainty method to approximate errors. Nomenclature

  19. A Novel Method for the Evaluation of Uncertainty in Dose-Volume Histogram Computation

    SciTech Connect

    Henriquez, Francisco Cutanda M.Sc. Castrillon, Silvia Vargas

    2008-03-15

    Purpose: Dose-volume histograms (DVHs) are a useful tool in state-of-the-art radiotherapy treatment planning, and it is essential to recognize their limitations. Even after a specific dose-calculation model is optimized, dose distributions computed by using treatment-planning systems are affected by several sources of uncertainty, such as algorithm limitations, measurement uncertainty in the data used to model the beam, and residual differences between measured and computed dose. This report presents a novel method to take them into account. Methods and Materials: To take into account the effect of associated uncertainties, a probabilistic approach using a new kind of histogram, a dose-expected volume histogram, is introduced. The expected value of the volume in the region of interest receiving an absorbed dose equal to or greater than a certain value is found by using the probability distribution of the dose at each point. A rectangular probability distribution is assumed for this point dose, and a formulation that accounts for uncertainties associated with point dose is presented for practical computations. Results: This method is applied to a set of DVHs for different regions of interest, including 6 brain patients, 8 lung patients, 8 pelvis patients, and 6 prostate patients planned for intensity-modulated radiation therapy. Conclusions: Results show a greater effect on planning target volume coverage than in organs at risk. In cases of steep DVH gradients, such as planning target volumes, this new method shows the largest differences with the corresponding DVH; thus, the effect of the uncertainty is larger.

  20. Prediction and Uncertainty in Computational Modeling of Complex Phenomena: A Whitepaper

    SciTech Connect

    Trucano, T.G.

    1999-01-20

    This report summarizes some challenges associated with the use of computational science to predict the behavior of complex phenomena. As such, the document is a compendium of ideas that have been generated by various staff at Sandia. The report emphasizes key components of the use of computational to predict complex phenomena, including computational complexity and correctness of implementations, the nature of the comparison with data, the importance of uncertainty quantification in comprehending what the prediction is telling us, and the role of risk in making and using computational predictions. Both broad and more narrowly focused technical recommendations for research are given. Several computational problems are summarized that help to illustrate the issues we have emphasized. The tone of the report is informal, with virtually no mathematics. However, we have attempted to provide a useful bibliography that would assist the interested reader in pursuing the content of this report in greater depth.

  1. PUQ: A code for non-intrusive uncertainty propagation in computer simulations

    NASA Astrophysics Data System (ADS)

    Hunt, Martin; Haley, Benjamin; McLennan, Michael; Koslowski, Marisol; Murthy, Jayathi; Strachan, Alejandro

    2015-09-01

    We present a software package for the non-intrusive propagation of uncertainties in input parameters through computer simulation codes or mathematical models and associated analysis; we demonstrate its use to drive micromechanical simulations using a phase field approach to dislocation dynamics. The PRISM uncertainty quantification framework (PUQ) offers several methods to sample the distribution of input variables and to obtain surrogate models (or response functions) that relate the uncertain inputs with the quantities of interest (QoIs); the surrogate models are ultimately used to propagate uncertainties. PUQ requires minimal changes in the simulation code, just those required to annotate the QoI(s) for its analysis. Collocation methods include Monte Carlo, Latin Hypercube and Smolyak sparse grids and surrogate models can be obtained in terms of radial basis functions and via generalized polynomial chaos. PUQ uses the method of elementary effects for sensitivity analysis in Smolyak runs. The code is available for download and also available for cloud computing in nanoHUB. PUQ orchestrates runs of the nanoPLASTICITY tool at nanoHUB where users can propagate uncertainties in dislocation dynamics simulations using simply a web browser, without downloading or installing any software.

  2. Numerical Uncertainty Analysis for Computational Fluid Dynamics using Student T Distribution -- Application of CFD Uncertainty Analysis Compared to Exact Analytical Solution

    NASA Technical Reports Server (NTRS)

    Groves, Curtis E.; Ilie, marcel; Shallhorn, Paul A.

    2014-01-01

    Computational Fluid Dynamics (CFD) is the standard numerical tool used by Fluid Dynamists to estimate solutions to many problems in academia, government, and industry. CFD is known to have errors and uncertainties and there is no universally adopted method to estimate such quantities. This paper describes an approach to estimate CFD uncertainties strictly numerically using inputs and the Student-T distribution. The approach is compared to an exact analytical solution of fully developed, laminar flow between infinite, stationary plates. It is shown that treating all CFD input parameters as oscillatory uncertainty terms coupled with the Student-T distribution can encompass the exact solution.

  3. Bayesian uncertainty analysis for complex physical systems modelled by computer simulators with applications to tipping points

    NASA Astrophysics Data System (ADS)

    Caiado, C. C. S.; Goldstein, M.

    2015-09-01

    In this paper we present and illustrate basic Bayesian techniques for the uncertainty analysis of complex physical systems modelled by computer simulators. We focus on emulation and history matching and also discuss the treatment of observational errors and structural discrepancies in time series. We exemplify such methods using a four-box model for the termohaline circulation. We show how these methods may be applied to systems containing tipping points and how to treat possible discontinuities using multiple emulators.

  4. How does our ignorance of rainfall affect the uncertainty of hydrological computations?

    NASA Astrophysics Data System (ADS)

    Lebecherel, Laure; Andréassian, Vazken

    2014-05-01

    Precipitation is an essential input to hydrological studies, fundamental for water balance studies, for hydrological simulation and forecasting. Since precipitation can be spatially and temporally variable, the configuration of the raingauge network can have a major impact on the accuracy of hydrological computations. Hydrological good sense tells us that the less we know about catchment rainfall, the more uncertain our hydrological computations will be. Quantifying this trend, i.e. the sensitivity of our computations to the design of the rainfall measurement network is essential in a context of increasing requests and decreasing finance. We keep hearing about the need to "rationalize" observation networks. However, this rationalization, which often means a reduction of network density, can deteriorate our rainfall knowledge and can particularly increase the hydrological computation uncertainties. We evaluate here on a large set of French catchments the impact of the rain gauge density and rain gauge network configuration on the uncertainty of several hydrological computations, based on the GR4J daily rainfall-runoff model [Perrin et al., 2003]. Four hydrological applications are considered: (i) daily runoff simulation, (ii) long-term average streamflow assessment, (iii) high-flow quantiles assessment, and (iv) low-flow quantiles assessment. Perrin, C., C. Michel, and V. Andréassian (2003), Improvement of a parsimonious model for streamflow simulation, Journal of Hydrology, 279(1-4), 275-289, doi: 10.1016/s0022-1694(03)00225-7.

  5. Flood risk assessment at the regional scale: Computational challenges and the monster of uncertainty

    NASA Astrophysics Data System (ADS)

    Efstratiadis, Andreas; Papalexiou, Simon-Michael; Markonis, Yiannis; Koukouvinos, Antonis; Vasiliades, Lampros; Papaioannou, George; Loukas, Athanasios

    2016-04-01

    We present a methodological framework for flood risk assessment at the regional scale, developed within the implementation of the EU Directive 2007/60 in Greece. This comprises three phases: (a) statistical analysis of extreme rainfall data, resulting to spatially-distributed parameters of intensity-duration-frequency (IDF) relationships and their confidence intervals, (b) hydrological simulations, using event-based semi-distributed rainfall-runoff approaches, and (c) hydraulic simulations, employing the propagation of flood hydrographs across the river network and the mapping of inundated areas. The flood risk assessment procedure is employed over the River Basin District of Thessaly, Greece, which requires schematization and modelling of hundreds of sub-catchments, each one examined for several risk scenarios. This is a challenging task, involving multiple computational issues to handle, such as the organization, control and processing of huge amount of hydrometeorological and geographical data, the configuration of model inputs and outputs, and the co-operation of several software tools. In this context, we have developed supporting applications allowing massive data processing and effective model coupling, thus drastically reducing the need for manual interventions and, consequently, the time of the study. Within flood risk computations we also account for three major sources of uncertainty, in an attempt to provide upper and lower confidence bounds of flood maps, i.e. (a) statistical uncertainty of IDF curves, (b) structural uncertainty of hydrological models, due to varying anteceded soil moisture conditions, and (c) parameter uncertainty of hydraulic models, with emphasis to roughness coefficients. Our investigations indicate that the combined effect of the above uncertainties (which are certainly not the unique ones) result to extremely large bounds of potential inundation, thus rising many questions about the interpretation and usefulness of current flood

  6. A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

    SciTech Connect

    Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

    2006-10-01

    Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

  7. Anthropometric approaches and their uncertainties to assigning computational phantoms to individual patients in pediatric dosimetry studies

    NASA Astrophysics Data System (ADS)

    Whalen, Scott; Lee, Choonsik; Williams, Jonathan L.; Bolch, Wesley E.

    2008-01-01

    Current efforts to reconstruct organ doses in children undergoing diagnostic imaging or therapeutic interventions using ionizing radiation typically rely upon the use of reference anthropomorphic computational phantoms coupled to Monte Carlo radiation transport codes. These phantoms are generally matched to individual patients based upon nearest age or sometimes total body mass. In this study, we explore alternative methods of phantom-to-patient matching with the goal of identifying those methods which yield the lowest residual errors in internal organ volumes. Various thoracic and abdominal organs were segmented and organ volumes obtained from chest-abdominal-pelvic (CAP) computed tomography (CT) image sets from 38 pediatric patients ranging in age from 2 months to 15 years. The organs segmented included the skeleton, heart, kidneys, liver, lungs and spleen. For each organ, least-squared regression lines, 95th percentile confidence intervals and 95th percentile prediction intervals were established as a function of patient age, trunk volume, estimated trunk mass, trunk height, and three estimates of the ventral body cavity volume based on trunk height alone, or in combination with circumferential, width and/or breadth measurements in the mid-chest of the patient. When matching phantom to patient based upon age, residual uncertainties in organ volumes ranged from 53% (lungs) to 33% (kidneys), and when trunk mass was used (surrogate for total body mass as we did not have images of patient head, arms or legs), these uncertainties ranged from 56% (spleen) to 32% (liver). When trunk height is used as the matching parameter, residual uncertainties in organ volumes were reduced to between 21 and 29% for all organs except the spleen (40%). In the case of the lungs and skeleton, the two-fold reduction in organ volume uncertainties was seen in moving from patient age to trunk height—a parameter easily measured in the clinic. When ventral body cavity volumes were used

  8. Deterministic methods for sensitivity and uncertainty analysis in large-scale computer models

    SciTech Connect

    Worley, B.A.; Oblow, E.M.; Pin, F.G.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.; Lucius, J.L.

    1987-01-01

    The fields of sensitivity and uncertainty analysis are dominated by statistical techniques when large-scale modeling codes are being analyzed. This paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. The paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. The paper demonstrates the deterministic approach to sensitivity and uncertainty analysis as applied to a sample problem that models the flow of water through a borehole. The sample problem is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. The DUA method gives a more accurate result based upon only two model executions compared to fifty executions in the statistical case.

  9. Efficient Parameter Estimation and Uncertainty Quantification for Computationally Expensive Simulations with Subsurface and Hydrology Applications

    NASA Astrophysics Data System (ADS)

    Shoemaker, C. A.; Singh, A.; Wang, Y.; Woodbury, J.

    2011-12-01

    Solving inverse problems for nonlinear simulation models with a nonlinear objective is usually a global optimization problem. This talk will discuss algorithms that employ response surfaces as a surrogate for an expensive simulation model or parallel computing to significantly reduce the computational time required to solve continuous global optimization problems and uncertainty analysis of simulation models that require a substantial amount of CPU time for each simulation. In order to reduce the number of simulations required, we are interested in utilizing information from all previous simulations done as part of an optimization search by building a (radial basis function) multivariate response surface that interpolates these earlier simulations. We will present examples of the application of these methods to significant environmental problems described by computationally intensive simulation models used worldwide including a large groundwater aquifer and a watershed model SWAT, which is used to describe potential pollution of NYC's drinking water. The models use site-specific data and the new algorithms are compared to well-known methods like PEST, SQP, and genetic algorithms. We will also describe an uncertainty analysis method SOARS that uses derivative-free optimization to help construct a response surface of the likelihood function to which Markov Chain Monte Carlo is applied. This approach has been shown to reduce CPU requirements to less than 1/10 of what is required by conventional MCMC uncertainty analysis. The computational methods described here are general and can be applied to a wide range of scientific and engineering problems described by nonlinear simulation models including those in the geosciences. Contact the senior author about open source software.

  10. Universal Quantum Computing:. Third Gen Prototyping Utilizing Relativistic `Trivector' R-Qubit Modeling Surmounting Uncertainty

    NASA Astrophysics Data System (ADS)

    Amoroso, Richard L.; Kauffman, Louis H.; Giandinoto, Salvatore

    2013-09-01

    We postulate bulk universal quantum computing (QC) cannot be achieved without surmounting the quantum uncertainty principle, an inherent barrier by empirical definition in the regime described by the Copenhagen interpretation of quantum theory - the last remaining hurdle to bulk QC. To surmount uncertainty with probability 1, we redefine the basis for the qubit utilizing a unique form of M-Theoretic Calabi-Yau mirror symmetry cast in an LSXD Dirac covariant polarized vacuum with an inherent `Feynman synchronization backbone'. This also incorporates a relativistic qubit (r-qubit) providing additional degrees of freedom beyond the traditional Block 2-sphere qubit bringing the r-qubit into correspondence with our version of Relativistic Topological Quantum Field Theory (RTQFT). We present a 3rd generation prototype design for simplifying bulk QC implementation.

  11. Advanced computational tools for optimization and uncertainty quantification of carbon capture processes

    SciTech Connect

    Miller, David C.; Ng, Brenda; Eslick, John

    2014-01-01

    Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and universities that is developing, demonstrating, and deploying a suite of multi-scale modeling and simulation tools. One significant computational tool is FOQUS, a Framework for Optimization and Quantification of Uncertainty and Sensitivity, which enables basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to rapidly synthesize and optimize a process and determine the level of uncertainty associated with the resulting process. The overall approach of CCSI is described with a more detailed discussion of FOQUS and its application to carbon capture systems.

  12. Uncertainty quantification based on pillars of experiment, theory, and computation. Part I: Data analysis

    NASA Astrophysics Data System (ADS)

    Elishakoff, I.; Sarlin, N.

    2016-06-01

    In this paper we provide a general methodology of analysis and design of systems involving uncertainties. Available experimental data is enclosed by some geometric figures (triangle, rectangle, ellipse, parallelogram, super ellipse) of minimum area. Then these areas are inflated resorting to the Chebyshev inequality in order to take into account the forecasted data. Next step consists in evaluating response of system when uncertainties are confined to one of the above five suitably inflated geometric figures. This step involves a combined theoretical and computational analysis. We evaluate the maximum response of the system subjected to variation of uncertain parameters in each hypothesized region. The results of triangular, interval, ellipsoidal, parallelogram, and super ellipsoidal calculi are compared with the view of identifying the region that leads to minimum of maximum response. That response is identified as a result of the suggested predictive inference. The methodology thus synthesizes probabilistic notion with each of the five calculi. Using the term "pillar" in the title was inspired by the News Release (2013) on according Honda Prize to J. Tinsley Oden, stating, among others, that "Dr. Oden refers to computational science as the "third pillar" of scientific inquiry, standing beside theoretical and experimental science. Computational science serves as a new paradigm for acquiring knowledge and informing decisions important to humankind". Analysis of systems with uncertainties necessitates employment of all three pillars. The analysis is based on the assumption that that the five shapes are each different conservative estimates of the true bounding region. The smallest of the maximal displacements in x and y directions (for a 2D system) therefore provides the closest estimate of the true displacements based on the above assumption.

  13. Evaluating Statistical Process Control (SPC) techniques and computing the uncertainty of force calibrations

    NASA Technical Reports Server (NTRS)

    Navard, Sharon E.

    1989-01-01

    In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

  14. Dynamic rating curve assessment for hydrometric stations and computation of the associated uncertainties: Quality and station management indicators

    NASA Astrophysics Data System (ADS)

    Morlot, Thomas; Perret, Christian; Favre, Anne-Catherine; Jalbert, Jonathan

    2014-09-01

    A rating curve is used to indirectly estimate the discharge in rivers based on water level measurements. The discharge values obtained from a rating curve include uncertainties related to the direct stage-discharge measurements (gaugings) used to build the curves, the quality of fit of the curve to these measurements and the constant changes in the river bed morphology. Moreover, the uncertainty of discharges estimated from a rating curve increases with the “age” of the rating curve. The level of uncertainty at a given point in time is therefore particularly difficult to assess. A “dynamic” method has been developed to compute rating curves while calculating associated uncertainties, thus making it possible to regenerate streamflow data with uncertainty estimates. The method is based on historical gaugings at hydrometric stations. A rating curve is computed for each gauging and a model of the uncertainty is fitted for each of them. The model of uncertainty takes into account the uncertainties in the measurement of the water level, the quality of fit of the curve, the uncertainty of gaugings and the increase of the uncertainty of discharge estimates with the age of the rating curve computed with a variographic analysis (Jalbert et al., 2011). The presented dynamic method can answer important questions in the field of hydrometry such as “How many gaugings a year are required to produce streamflow data with an average uncertainty of X%?” and “When and in what range of water flow rates should these gaugings be carried out?”. The Rocherousse hydrometric station (France, Haute-Durance watershed, 946 [km2]) is used as an example throughout the paper. Others stations are used to illustrate certain points.

  15. NASTRAN variance analysis and plotting of HBDY elements. [analysis of uncertainties of the computer results as a function of uncertainties in the input data

    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.

  16. Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

    NASA Astrophysics Data System (ADS)

    Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

    2015-03-01

    We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

  17. Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

    SciTech Connect

    Hadjidoukas, P.E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

    2015-03-01

    We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

  18. "I Guess My Question Is": What Is the Co-Occurrence of Uncertainty and Learning in Computer-Mediated Discourse?

    ERIC Educational Resources Information Center

    Jordan, Michelle E.; Cheng, An-Chih Janne; Schallert, Diane; Song, Kwangok; Lee, SoonAh; Park, Yangjoo

    2014-01-01

    The purpose of this study was to contribute to a better understanding of learning in computer-supported collaborative learning (CSCL) environments by investigating the co-occurrence of uncertainty expressions and expressions of learning in a graduate course in which students collaborated in classroom computer-mediated discussions. Results showed…

  19. Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation.

    PubMed

    Vo, Brenda N; Drovandi, Christopher C; Pettitt, Anthony N; Simpson, Matthew J

    2015-05-01

    Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2-6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled

  20. Experimental evaluation of the uncertainty associated with the result of feature-of-size measurements through computed tomography

    NASA Astrophysics Data System (ADS)

    Fernandes, T. L.; Donatelli, G. D.; Baldo, C. R.

    2016-07-01

    Computed tomography for dimensional metrology has been introduced in quality control loop for about a decade. Due to the complex measurement-error cause system, generally no consistent measurement uncertainty reporting has been made. The ISO 15530-3 experimental approach, which makes use of calibrated parts, has been tested for estimating the uncertainty of CT-based measurements of features of size of a test object made of POM. Particular attention is given to the design of experiment and to the measurement uncertainty components. The most significant experimental findings are outlined and discussed in this paper.

  1. Dynamic rating curve assessment for hydrometric stations computation of the associated uncertainties: Quality and station management indicators

    NASA Astrophysics Data System (ADS)

    Morlot, Thomas; Perret, Christian; Favre, Anne-Catherine; Despax, Aurélien; Hauet, Alexandre; Sevrez, Damien; Belleville, Arnaud

    2015-04-01

    Whether we talk about safety reasons, energy production or regulation, water resources management is one of EDF's (French hydropower company) main concerns. To meet these needs, since the fifties EDF-DTG operates a hydrometric network that includes more than 350 hydrometric stations. The data collected allow real time monitoring of rivers (hydro meteorological forecasts at points of interests), as well as hydrological studies and the sizing of structures. Ensuring the quality of stream flow data is a priority. A rating curve is used to indirectly estimate the discharge in rivers based on water level measurements. The discharge values obtained from a rating curve include uncertainties related to the direct stage-discharge measurements (gaugings) used to build the curves, the quality of fit of the curve to these measurements and the constant changes in the river bed morphology. Moreover, the uncertainty of discharges estimated from a rating curve increases with the ''age'' of the rating curve. The level of uncertainty at a given point in time is therefore particularly difficult to assess. Moreover, the current capacity to produce a rating curve is not suited to the frequency of change of the stage-discharge relationship. The actual method does not take into consideration the variation of the flow conditions and the modifications of the river bed which occur due to natural processes such as erosion, sedimentation and seasonal vegetation growth. A « dynamic » method has been developed to compute rating curves while calculating associated uncertainties, thus making it possible to regenerate streamflow data with uncertainty estimates. The method is based on historical gaugings at hydrometric stations. A rating curve is computed for each gauging and a model of the uncertainty is fitted for each of them. The model of uncertainty takes into account the uncertainties in the measurement of the water level, the quality of fit of the curve, the uncertainty of gaugings and the

  2. Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections

    SciTech Connect

    Reutter, Bryan W.; Gullberg, Grant T.; Huesman, Ronald H.

    2001-04-09

    The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of

  3. Dose computation in conformal radiation therapy including geometric uncertainties: Methods and clinical implications

    NASA Astrophysics Data System (ADS)

    Rosu, Mihaela

    The aim of any radiotherapy is to tailor the tumoricidal radiation dose to the target volume and to deliver as little radiation dose as possible to all other normal tissues. However, the motion and deformation induced in human tissue by ventilatory motion is a major issue, as standard practice usually uses only one computed tomography (CT) scan (and hence one instance of the patient's anatomy) for treatment planning. The interfraction movement that occurs due to physiological processes over time scales shorter than the delivery of one treatment fraction leads to differences between the planned and delivered dose distributions. Due to the influence of these differences on tumors and normal tissues, the tumor control probabilities and normal tissue complication probabilities are likely to be impacted upon in the face of organ motion. In this thesis we apply several methods to compute dose distributions that include the effects of the treatment geometric uncertainties by using the time-varying anatomical information as an alternative to the conventional Planning Target Volume (PTV) approach. The proposed methods depend on the model used to describe the patient's anatomy. The dose and fluence convolution approaches for rigid organ motion are discussed first, with application to liver tumors and the rigid component of the lung tumor movements. For non-rigid behavior a dose reconstruction method that allows the accumulation of the dose to the deforming anatomy is introduced, and applied for lung tumor treatments. Furthermore, we apply the cumulative dose approach to investigate how much information regarding the deforming patient anatomy is needed at the time of treatment planning for tumors located in thorax. The results are evaluated from a clinical perspective. All dose calculations are performed using a Monte Carlo based algorithm to ensure more realistic and more accurate handling of tissue heterogeneities---of particular importance in lung cancer treatment planning.

  4. A Bootstrap Approach to Computing Uncertainty in Inferred Oil and Gas Reserve Estimates

    SciTech Connect

    Attanasi, Emil D. Coburn, Timothy C.

    2004-03-15

    This study develops confidence intervals for estimates of inferred oil and gas reserves based on bootstrap procedures. Inferred reserves are expected additions to proved reserves in previously discovered conventional oil and gas fields. Estimates of inferred reserves accounted for 65% of the total oil and 34% of the total gas assessed in the U.S. Geological Survey's 1995 National Assessment of oil and gas in US onshore and State offshore areas. When the same computational methods used in the 1995 Assessment are applied to more recent data, the 80-year (from 1997 through 2076) inferred reserve estimates for pre-1997 discoveries located in the lower 48 onshore and state offshore areas amounted to a total of 39.7 billion barrels of oil (BBO) and 293 trillion cubic feet (TCF) of gas. The 90% confidence interval about the oil estimate derived from the bootstrap approach is 22.4 BBO to 69.5 BBO. The comparable 90% confidence interval for the inferred gas reserve estimate is 217 TCF to 413 TCF. The 90% confidence interval describes the uncertainty that should be attached to the estimates. It also provides a basis for developing scenarios to explore the implications for energy policy analysis.

  5. Evaluation of Computational Techniques for Parameter Estimation and Uncertainty Analysis of Comprehensive Watershed Models

    NASA Astrophysics Data System (ADS)

    Yen, H.; Arabi, M.; Records, R.

    2012-12-01

    The structural complexity of comprehensive watershed models continues to increase in order to incorporate inputs at finer spatial and temporal resolutions and simulate a larger number of hydrologic and water quality responses. Hence, computational methods for parameter estimation and uncertainty analysis of complex models have gained increasing popularity. This study aims to evaluate the performance and applicability of a range of algorithms from computationally frugal approaches to formal implementations of Bayesian statistics using Markov Chain Monte Carlo (MCMC) techniques. The evaluation procedure hinges on the appraisal of (i) the quality of final parameter solution in terms of the minimum value of the objective function corresponding to weighted errors; (ii) the algorithmic efficiency in reaching the final solution; (iii) the marginal posterior distributions of model parameters; (iv) the overall identifiability of the model structure; and (v) the effectiveness in drawing samples that can be classified as behavior-giving solutions. The proposed procedure recognize an important and often neglected issue in watershed modeling that solutions with minimum objective function values may not necessarily reflect the behavior of the system. The general behavior of a system is often characterized by the analysts according to the goals of studies using various error statistics such as percent bias or Nash-Sutcliffe efficiency coefficient. Two case studies are carried out to examine the efficiency and effectiveness of four Bayesian approaches including Metropolis-Hastings sampling (MHA), Gibbs sampling (GSA), uniform covering by probabilistic rejection (UCPR), and differential evolution adaptive Metropolis (DREAM); a greedy optimization algorithm dubbed dynamically dimensioned search (DDS); and shuffle complex evolution (SCE-UA), a widely implemented evolutionary heuristic optimization algorithm. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrologic and

  6. Expressing Uncertainty in Computer-Mediated Discourse: Language as a Marker of Intellectual Work

    ERIC Educational Resources Information Center

    Jordan, Michelle E.; Schallert, Diane L.; Park, Yangjoo; Lee, SoonAh; Chiang, Yueh-hui Vanessa; Cheng, An-Chih Janne; Song, Kwangok; Chu, Hsiang-Ning Rebecca; Kim, Taehee; Lee, Haekyung

    2012-01-01

    Learning and dialogue may naturally engender feelings and expressions of uncertainty for a variety of reasons and purposes. Yet, little research has examined how patterns of linguistic uncertainty are enacted and changed over time as students reciprocally influence one another and the dialogical system they are creating. This study describes the…

  7. A methodology for computing uncertainty bounds of multivariable systems based on sector stability theory concepts

    NASA Technical Reports Server (NTRS)

    Waszak, Martin R.

    1992-01-01

    The application of a sector-based stability theory approach to the formulation of useful uncertainty descriptions for linear, time-invariant, multivariable systems is explored. A review of basic sector properties and sector-based approach are presented first. The sector-based approach is then applied to several general forms of parameter uncertainty to investigate its advantages and limitations. The results indicate that the sector uncertainty bound can be used effectively to evaluate the impact of parameter uncertainties on the frequency response of the design model. Inherent conservatism is a potential limitation of the sector-based approach, especially for highly dependent uncertain parameters. In addition, the representation of the system dynamics can affect the amount of conservatism reflected in the sector bound. Careful application of the model can help to reduce this conservatism, however, and the solution approach has some degrees of freedom that may be further exploited to reduce the conservatism.

  8. Computing continuous record of discharge with quantified uncertainty using index velocity observations: A probabilistic machine learning approach

    NASA Astrophysics Data System (ADS)

    Farahmand, Touraj; Hamilton, Stuart

    2016-04-01

    Application of the index velocity method for computing continuous records of discharge has become increasingly common, especially since the introduction of low-cost acoustic Doppler velocity meters (ADVMs). In general, the index velocity method can be used at locations where stage-discharge methods are used, but it is especially appropriate and recommended when more than one specific discharge can be measured for a specific stage such as backwater and unsteady flow conditions caused by but not limited to the following; stream confluences, streams flowing into lakes or reservoirs, tide-affected streams, regulated streamflows (dams or control structures), or streams affected by meteorological forcing, such as strong prevailing winds. In existing index velocity modeling techniques, two models (ratings) are required; index velocity model and stage-area model. The outputs from each of these models, mean channel velocity (Vm) and cross-sectional area (A), are then multiplied together to compute a discharge. Mean channel velocity (Vm) can generally be determined by a multivariate regression parametric model such as linear regression in the simplest case. The main challenges in the existing index velocity modeling techniques are; 1) Preprocessing and QA/QC of continuous index velocity data and synchronizing them with discharge measurements. 2) Nonlinear relationship between mean velocity and index velocity which is not uncommon at monitoring locations. 3)Model exploration and analysis in order to find the optimal regression model predictor(s) and model type (linear vs nonlinear and if nonlinear number of the parameters). 3) Model changes caused by dynamical changes in the environment (geomorphic, biological) over time 5) Deployment of the final model into the Data Management Systems (DMS) for real-time discharge calculation 6) Objective estimation of uncertainty caused by: field measurement errors; structural uncertainty; parameter uncertainty; and continuous sensor data

  9. Computation of the intervals of uncertainties about the parameters found for identification

    NASA Technical Reports Server (NTRS)

    Mereau, P.; Raymond, J.

    1982-01-01

    A modeling method to calculate the intervals of uncertainty for parameters found by identification is described. The region of confidence and the general approach to the calculation of these intervals are discussed. The general subprograms for determination of dimensions are described. They provide the organizational charts for the subprograms, the tests carried out and the listings of the different subprograms.

  10. PABS: A Computer Program to Normalize Emission Probabilities and Calculate Realistic Uncertainties

    SciTech Connect

    Caron, D. S.; Browne, E.; Norman, E. B.

    2009-08-21

    The program PABS normalizes relative particle emission probabilities to an absolute scale and calculates the relevant uncertainties on this scale. The program is written in Java using the JDK 1.6 library. For additional information about system requirements, the code itself, and compiling from source, see the README file distributed with this program. The mathematical procedures used are given below.

  11. Uncertainty Evaluation of Computational Model Used to Support the Integrated Powerhead Demonstration Project

    NASA Technical Reports Server (NTRS)

    Steele, W. G.; Molder, K. J.; Hudson, S. T.; Vadasy, K. V.; Rieder, P. T.; Giel, T.

    2005-01-01

    NASA and the U.S. Air Force are working on a joint project to develop a new hydrogen-fueled, full-flow, staged combustion rocket engine. The initial testing and modeling work for the Integrated Powerhead Demonstrator (IPD) project is being performed by NASA Marshall and Stennis Space Centers. A key factor in the testing of this engine is the ability to predict and measure the transient fluid flow during engine start and shutdown phases of operation. A model built by NASA Marshall in the ROCket Engine Transient Simulation (ROCETS) program is used to predict transient engine fluid flows. The model is initially calibrated to data from previous tests on the Stennis E1 test stand. The model is then used to predict the next run. Data from this run can then be used to recalibrate the model providing a tool to guide the test program in incremental steps to reduce the risk to the prototype engine. In this paper, they define this type of model as a calibrated model. This paper proposes a method to estimate the uncertainty of a model calibrated to a set of experimental test data. The method is similar to that used in the calibration of experiment instrumentation. For the IPD example used in this paper, the model uncertainty is determined for both LOX and LH flow rates using previous data. The successful use of this model is then demonstrated to predict another similar test run within the uncertainty bounds. The paper summarizes the uncertainty methodology when a model is continually recalibrated with new test data. The methodology is general and can be applied to other calibrated models.

  12. Evaluation of the measurement uncertainty in screening immunoassays in blood establishments: computation of diagnostic accuracy models.

    PubMed

    Pereira, Paulo; Westgard, James O; Encarnação, Pedro; Seghatchian, Jerard

    2015-02-01

    The European Union regulation for blood establishments does not require the evaluation of measurement uncertainty in virology screening tests, which is required by ISO 15189 guideline following GUM principles. GUM modular approaches have been discussed by medical laboratory researchers but no consensus has been achieved regarding practical application. Meanwhile, the application of empirical approaches fulfilling GUM principles has gained support. Blood establishments' screening tests accredited by ISO 15189 need to select an appropriate model even GUM models are intended uniquely for quantitative examination procedures. Alternative (to GUM) models focused on probability have been proposed in medical laboratories' diagnostic tests. This article reviews, discusses and proposes models for diagnostic accuracy in blood establishments' screening tests. The output of these models is an alternative to VIM's measurement uncertainty concept. Example applications are provided for an anti-HCV test where calculations were performed using a commercial spreadsheet. The results show that these models satisfy ISO 15189 principles and that the estimation of clinical sensitivity, clinical specificity, binary results agreement and area under the ROC curve are alternatives to the measurement uncertainty concept. PMID:25617905

  13. Mathematical and Computational Tools for Predictive Simulation of Complex Coupled Systems under Uncertainty

    SciTech Connect

    Ghanem, Roger

    2013-03-25

    Methods and algorithms are developed to enable the accurate analysis of problems that exhibit interacting physical processes with uncertainties. These uncertainties can pertain either to each of the physical processes or to the manner in which they depend on each others. These problems are cast within a polynomial chaos framework and their solution then involves either solving a large system of algebraic equations or a high dimensional numerical quadrature. In both cases, the curse of dimensionality is manifested. Procedures are developed for the efficient evaluation of the resulting linear equations that advantage of the block sparse structure of these equations, resulting in a block recursive Schur complement construction. In addition, embedded quadratures are constructed that permit the evaluation of very high-dimensional integrals using low-dimensional quadratures adapted to particular quantities of interest. The low-dimensional integration is carried out in a transformed measure space in which the quantity of interest is low-dimensional. Finally, a procedure is also developed to discover a low-dimensional manifold, embedded in the initial high-dimensional one, in which scalar quantities of interest exist. This approach permits the functional expression of the reduced space in terms of the original space, thus permitting cross-scale sensitivity analysis.

  14. Development of a Computational Framework for Stochastic Co-optimization of Water and Energy Resource Allocations under Climatic Uncertainty

    NASA Astrophysics Data System (ADS)

    Xuan, Y.; Mahinthakumar, K.; Arumugam, S.; DeCarolis, J.

    2015-12-01

    Owing to the lack of a consistent approach to assimilate probabilistic forecasts for water and energy systems, utilization of climate forecasts for conjunctive management of these two systems is very limited. Prognostic management of these two systems presents a stochastic co-optimization problem that seeks to determine reservoir releases and power allocation strategies while minimizing the expected operational costs subject to probabilistic climate forecast constraints. To address these issues, we propose a high performance computing (HPC) enabled computational framework for stochastic co-optimization of water and energy resource allocations under climate uncertainty. The computational framework embodies a new paradigm shift in which attributes of climate (e.g., precipitation, temperature) and its forecasted probability distribution are employed conjointly to inform seasonal water availability and electricity demand. The HPC enabled cyberinfrastructure framework is developed to perform detailed stochastic analyses, and to better quantify and reduce the uncertainties associated with water and power systems management by utilizing improved hydro-climatic forecasts. In this presentation, our stochastic multi-objective solver extended from Optimus (Optimization Methods for Universal Simulators), is introduced. The solver uses parallel cooperative multi-swarm method to solve for efficient solution of large-scale simulation-optimization problems on parallel supercomputers. The cyberinfrastructure harnesses HPC resources to perform intensive computations using ensemble forecast models of streamflow and power demand. The stochastic multi-objective particle swarm optimizer we developed is used to co-optimize water and power system models under constraints over a large number of ensembles. The framework sheds light on the application of climate forecasts and cyber-innovation framework to improve management and promote the sustainability of water and energy systems.

  15. Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

    DOE PAGESBeta

    Stracuzzi, David John; Brost, Randolph C.; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane M. -K.

    2015-09-26

    Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match qualitymore » scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.« less

  16. Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

    SciTech Connect

    Stracuzzi, David John; Brost, Randolph C.; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane M. -K.

    2015-09-26

    Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.

  17. Three-dimensional computer-assisted craniometrics: a comparison of the uncertainty in measurement induced by surface reconstruction performed by two computer programs.

    PubMed

    Guyomarc'h, Pierre; Santos, Frédéric; Dutailly, Bruno; Desbarats, Pascal; Bou, Christophe; Coqueugniot, Hélène

    2012-06-10

    Digital investigation of anthropological material through computed tomography (CT) offers several new opportunities in morphometrics. However, an object measured with computer-assisted methods does not necessarily exactly match the original one. The scanning and surface reconstruction of the object induce some alterations, and data acquisition is prone to measurement uncertainty. The purpose of this research is to evaluate the intra- and inter-observers variations in medical CT scan measurements of a known-size phantom and two dry crania. Two software packages, AMIRA and Treatment and Increased Vision for Medical Imaging (TIVMI), which use different techniques of surface reconstructions, were compared. The mean difference between the measurements was lower for TIVMI, using an objective algorithm based on the half-maximum height (HMH) protocol in three dimensions (3D). AMIRA can induce up to a 4% error in known measurements and 5% uncertainty in dry skull measurements. This study emphasises the risk of object shape alteration in each step of its digitisation. PMID:22297143

  18. Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing.

    ERIC Educational Resources Information Center

    Chen, Hsinchun; Martinez, Joanne; Kirchhoff, Amy; Ng, Tobun D.; Schatz, Bruce R.

    1998-01-01

    Grounded on object filtering, automatic indexing, and co-occurrence analysis, an experiment was performed using a parallel supercomputer to analyze over 400,000 abstracts in an INSPEC computer engineering collection. A user evaluation revealed that system-generated thesauri were better than the human-generated INSPEC subject thesaurus in concept…

  19. Bayesian Uncertainty Analysis SOARS for Computationally Expensive Simulation Models with Application to Contaminant Hydrology in the Cannonsville Watershed

    NASA Astrophysics Data System (ADS)

    Shoemaker, C. A.; Cowan, D.; Woodbury, J.; Ruppert, D.; Bliznyuk, N.; Wang, Y.; Li, Y.

    2009-12-01

    This paper presents application of a new computationally efficient method SOARS for statistically rigorous assessment of uncertainty in parameters and model output when the model is calibrated to field data. The SOARS method is general and is here applied to watershed problems The innovative aspect of this procedure is that an optimization method is first used to find the maximum likelihood estimator and then the costly simulations done during the optimization are re-used to build a response surface model of the likelihood function. Markov Chain Monte Carlo is applied then to the response surface model to obtain the posterior distributions of the model parameters and the appropriate transformations to correct for non-normal error. On a hazardous spill in channel problem and on a small watershed (37 km2), the computational effort to obtain roughly the same accuracy of solution is 150 model simulations for the SOARS method versus 10,000 simulations for conventional MCMC analysis, which is more than a 60 fold reduction in computational effort. For the larger Cannonsville Watershed (1200 km2) the method is expanded to provide posterior densities not only on parameter values but also on multiple model predictions. Available software for the method will be discussed as well as SOAR’s use for assessing the impact of climate change on hydrology and water-borne pollutant transport in the Cannonsville basin and other watersheds.

  20. Numerical study of premixed HCCI engine combustion and its sensitivity to computational mesh and model uncertainties

    NASA Astrophysics Data System (ADS)

    Kong, Song-Charng; Reitz, Rolf D.

    2003-06-01

    This study used a numerical model to investigate the combustion process in a premixed iso-octane homogeneous charge compression ignition (HCCI) engine. The engine was a supercharged Cummins C engine operated under HCCI conditions. The CHEMKIN code was implemented into an updated KIVA-3V code so that the combustion could be modelled using detailed chemistry in the context of engine CFD simulations. The model was able to accurately simulate the ignition timing and combustion phasing for various engine conditions. The unburned hydrocarbon emissions were also well predicted while the carbon monoxide emissions were under predicted. Model results showed that the majority of unburned hydrocarbon is located in the piston-ring crevice region and the carbon monoxide resides in the vicinity of the cylinder walls. A sensitivity study of the computational grid resolution indicated that the combustion predictions were relatively insensitive to the grid density. However, the piston-ring crevice region needed to be simulated with high resolution to obtain accurate emissions predictions. The model results also indicated that HCCI combustion and emissions are very sensitive to the initial mixture temperature. The computations also show that the carbon monoxide emissions prediction can be significantly improved by modifying a key oxidation reaction rate constant.

  1. Reducing annotation cost and uncertainty in computer-aided diagnosis through selective iterative classification

    NASA Astrophysics Data System (ADS)

    Riely, Amelia; Sablan, Kyle; Xiaotao, Thomas; Furst, Jacob; Raicu, Daniela

    2015-03-01

    Medical imaging technology has always provided radiologists with the opportunity to view and keep records of anatomy of the patient. With the development of machine learning and intelligent computing, these images can be used to create Computer-Aided Diagnosis (CAD) systems, which can assist radiologists in analyzing image data in various ways to provide better health care to patients. This paper looks at increasing accuracy and reducing cost in creating CAD systems, specifically in predicting the malignancy of lung nodules in the Lung Image Database Consortium (LIDC). Much of the cost in creating an accurate CAD system stems from the need for multiple radiologist diagnoses or annotations of each image, since there is rarely a ground truth diagnosis and even different radiologists' diagnoses of the same nodule often disagree. To resolve this issue, this paper outlines an method of selective iterative classification that predicts lung nodule malignancy by using multiple radiologist diagnoses only for cases that can benefit from them. Our method achieved 81% accuracy while costing only 46% of the method that indiscriminately used all annotations, which achieved a lower accuracy of 70%, while costing more.

  2. UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

    USGS Publications Warehouse

    Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen

    2006-01-01

    This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a

  3. Uncertainty in Aspiration Efficiency Estimates from Torso Simplifications in Computational Fluid Dynamics Simulations

    PubMed Central

    Anthony, T. Renée

    2013-01-01

    Computational fluid dynamics (CFD) has been used to report particle inhalability in low velocity freestreams, where realistic faces but simplified, truncated, and cylindrical human torsos were used. When compared to wind tunnel velocity studies, the truncated models were found to underestimate the air’s upward velocity near the humans, raising questions about aspiration estimation. This work compares aspiration efficiencies for particles ranging from 7 to 116 µm using three torso geometries: (i) a simplified truncated cylinder, (ii) a non-truncated cylinder, and (iii) an anthropometrically realistic humanoid body. The primary aim of this work is to (i) quantify the errors introduced by using a simplified geometry and (ii) determine the required level of detail to adequately represent a human form in CFD studies of aspiration efficiency. Fluid simulations used the standard k-epsilon turbulence models, with freestream velocities at 0.1, 0.2, and 0.4 m s−1 and breathing velocities at 1.81 and 12.11 m s−1 to represent at-rest and heavy breathing rates, respectively. Laminar particle trajectory simulations were used to determine the upstream area, also known as the critical area, where particles would be inhaled. These areas were used to compute aspiration efficiencies for facing the wind. Significant differences were found in both vertical velocity estimates and the location of the critical area between the three models. However, differences in aspiration efficiencies between the three forms were <8.8% over all particle sizes, indicating that there is little difference in aspiration efficiency between torso models. PMID:23006817

  4. ESTIMATION OF INTERNAL EXPOSURE TO URANIUM WITH UNCERTAINTY FROM URINALYSIS DATA USING THE InDEP COMPUTER CODE

    PubMed Central

    Anderson, Jeri L.; Apostoaei, A. Iulian; Thomas, Brian A.

    2015-01-01

    The National Institute for Occupational Safety and Health (NIOSH) is currently studying mortality in a cohort of 6409 workers at a former uranium processing facility. As part of this study, over 220 000 urine samples were used to reconstruct organ doses due to internal exposure to uranium. Most of the available computational programs designed for analysis of bioassay data handle a single case at a time, and thus require a significant outlay of time and resources for the exposure assessment of a large cohort. NIOSH is currently supporting the development of a computer program, InDEP (Internal Dose Evaluation Program), to facilitate internal radiation exposure assessment as part of epidemiological studies of both uranium- and plutonium-exposed cohorts. A novel feature of InDEP is its batch processing capability which allows for the evaluation of multiple study subjects simultaneously. InDEP analyses bioassay data and derives intakes and organ doses with uncertainty estimates using least-squares regression techniques or using the Bayes’ Theorem as applied to internal dosimetry (Bayesian method). This paper describes the application of the current version of InDEP to formulate assumptions about the characteristics of exposure at the study facility that were used in a detailed retrospective intake and organ dose assessment of the cohort. PMID:22683620

  5. The Personality Trait of Intolerance to Uncertainty Affects Behavior in a Novel Computer-Based Conditioned Place Preference Task.

    PubMed

    Radell, Milen L; Myers, Catherine E; Beck, Kevin D; Moustafa, Ahmed A; Allen, Michael Todd

    2016-01-01

    Recent work has found that personality factors that confer vulnerability to addiction can also affect learning and economic decision making. One personality trait which has been implicated in vulnerability to addiction is intolerance to uncertainty (IU), i.e., a preference for familiar over unknown (possibly better) options. In animals, the motivation to obtain drugs is often assessed through conditioned place preference (CPP), which compares preference for contexts where drug reward was previously received. It is an open question whether participants with high IU also show heightened preference for previously rewarded contexts. To address this question, we developed a novel computer-based CPP task for humans in which participants guide an avatar through a paradigm in which one room contains frequent reward (i.e., rich) and one contains less frequent reward (i.e., poor). Following exposure to both contexts, subjects are assessed for preference to enter the previously rich and previously poor room. Individuals with low IU showed little bias to enter the previously rich room first, and instead entered both rooms at about the same rate which may indicate a foraging behavior. By contrast, those with high IU showed a strong bias to enter the previously rich room first. This suggests an increased tendency to chase reward in the intolerant group, consistent with previously observed behavior in opioid-addicted individuals. Thus, the personality factor of high IU may produce a pre-existing cognitive bias that provides a mechanism to promote decision-making processes that increase vulnerability to addiction. PMID:27555829

  6. The Personality Trait of Intolerance to Uncertainty Affects Behavior in a Novel Computer-Based Conditioned Place Preference Task

    PubMed Central

    Radell, Milen L.; Myers, Catherine E.; Beck, Kevin D.; Moustafa, Ahmed A.; Allen, Michael Todd

    2016-01-01

    Recent work has found that personality factors that confer vulnerability to addiction can also affect learning and economic decision making. One personality trait which has been implicated in vulnerability to addiction is intolerance to uncertainty (IU), i.e., a preference for familiar over unknown (possibly better) options. In animals, the motivation to obtain drugs is often assessed through conditioned place preference (CPP), which compares preference for contexts where drug reward was previously received. It is an open question whether participants with high IU also show heightened preference for previously rewarded contexts. To address this question, we developed a novel computer-based CPP task for humans in which participants guide an avatar through a paradigm in which one room contains frequent reward (i.e., rich) and one contains less frequent reward (i.e., poor). Following exposure to both contexts, subjects are assessed for preference to enter the previously rich and previously poor room. Individuals with low IU showed little bias to enter the previously rich room first, and instead entered both rooms at about the same rate which may indicate a foraging behavior. By contrast, those with high IU showed a strong bias to enter the previously rich room first. This suggests an increased tendency to chase reward in the intolerant group, consistent with previously observed behavior in opioid-addicted individuals. Thus, the personality factor of high IU may produce a pre-existing cognitive bias that provides a mechanism to promote decision-making processes that increase vulnerability to addiction. PMID:27555829

  7. Direct Aerosol Forcing Uncertainty

    DOE Data Explorer

    Mccomiskey, Allison

    2008-01-15

    Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are calculated at three locations representing distinct aerosol types and radiative environments. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective aerosol or surface property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 1.3 W m-2 (42 to 20%) depending on location (from tropical to polar sites), solar zenith angle, surface reflectance, aerosol type, and aerosol optical depth. The largest contributor to total uncertainty in DRF is usually single scattering albedo; however decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.

  8. Uncertainty and Cognitive Control

    PubMed Central

    Mushtaq, Faisal; Bland, Amy R.; Schaefer, Alexandre

    2011-01-01

    A growing trend of neuroimaging, behavioral, and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1) There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2) There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3) The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the “need for control”; (4) Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders. PMID:22007181

  9. THE USE OF COMPUTER MODELING PACKAGES TO ILLUSTRATE UNCERTAINTY IN RISK ASSESSMENTS: AN EASE OF USE AND INTERPRETATION COMPARISON

    EPA Science Inventory

    Consistent improvements in processor speed and computer access have substantially increased the use of computer modeling by experts and non-experts alike. Several new computer modeling packages operating under graphical operating systems (i.e. Microsoft Windows or Macintosh) m...

  10. Uncertainty analysis

    SciTech Connect

    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.

  11. Uncertainty budget for a whole body counter in the scan geometry and computer simulation of the calibration phantoms.

    PubMed

    Schlagbauer, M; Hrnecek, E; Rollet, S; Fischer, H; Brandl, A; Kindl, P

    2007-01-01

    At the Austrian Research Centers Seibersdorf (ARCS), a whole body counter (WBC) in the scan geometry is used to perform routine measurements for the determination of radioactive intake of workers. The calibration of the WBC is made using bottle phantoms with a homogeneous activity distribution. The same calibration procedures have been simulated using Monte Carlo N-Particle (MCNP) code and FLUKA and the results of the full energy peak efficiencies for eight energies and five phantoms have been compared with the experimental results. The deviation between experiment and simulation results is within 10%. Furthermore, uncertainty budget evaluations have been performed to find out which parameters make substantial contributions to these differences. Therefore, statistical errors of the Monte Carlo simulation, uncertainties in the cross section tables and differences due to geometrical considerations have been taken into account. Comparisons between these results and the one with inhomogeneous distribution, for which the activity is concentrated only in certain parts of the body (such as head, lung, arms and legs), have been performed. The maximum deviation of 43% from the homogeneous case has been found when the activity is concentrated on the arms. PMID:17656442

  12. Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty

    PubMed Central

    Ueyama, Yuki

    2014-01-01

    We propose a mini-max feedback control (MMFC) model as a robust approach to human motor control under conditions of uncertain dynamics, such as structural uncertainty. The MMFC model is an expansion of the optimal feedback control (OFC) model. According to this scheme, motor commands are generated to minimize the maximal cost, based on an assumption of worst-case uncertainty, characterized by familiarity with novel dynamics. We simulated linear dynamic systems with different types of force fields–stable and unstable dynamics–and compared the performance of MMFC to that of OFC. MMFC delivered better performance than OFC in terms of stability and the achievement of tasks. Moreover, the gain in positional feedback with the MMFC model in the unstable dynamics was tuned to the direction of instability. It is assumed that the shape modulations of the gain in positional feedback in unstable dynamics played the same role as that played by end-point stiffness observed in human studies. Accordingly, we suggest that MMFC is a plausible model that predicts motor behavior under conditions of uncertain dynamics. PMID:25309415

  13. Exploring the Impact of Nuclear Data Uncertainties in Ultra-high Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation

    SciTech Connect

    Burr, T.; Hoover, A.; Croft, S.; Rabin, M.

    2015-01-15

    High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced X-rays and gamma-rays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signal-to-background ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and half-lives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.

  14. Computing the Risk of Postprandial Hypo- and Hyperglycemia in Type 1 Diabetes Mellitus Considering Intrapatient Variability and Other Sources of Uncertainty

    PubMed Central

    García-Jaramillo, Maira; Calm, Remei; Bondia, Jorge; Tarín, Cristina; Vehí, Josep

    2009-01-01

    Objective The objective of this article was to develop a methodology to quantify the risk of suffering different grades of hypo- and hyperglycemia episodes in the postprandial state. Methods Interval predictions of patient postprandial glucose were performed during a 5-hour period after a meal for a set of 3315 scenarios. Uncertainty in the patient's insulin sensitivities and carbohydrate (CHO) contents of the planned meal was considered. A normalized area under the curve of the worst-case predicted glucose excursion for severe and mild hypo- and hyperglycemia glucose ranges was obtained and weighted accordingly to their importance. As a result, a comprehensive risk measure was obtained. A reference model of preprandial glucose values representing the behavior in different ranges was chosen by a ξ2 test. The relationship between the computed risk index and the probability of occurrence of events was analyzed for these reference models through 19,500 Monte Carlo simulations. Results The obtained reference models for each preprandial glucose range were 100, 160, and 220 mg/dl. A relationship between the risk index ranges <10, 10–60, 60–120, and >120 and the probability of occurrence of mild and severe postprandial hyper- and hypoglycemia can be derived. Conclusions When intrapatient variability and uncertainty in the CHO content of the meal are considered, a safer prediction of possible hyper- and hypoglycemia episodes induced by the tested insulin therapy can be calculated. PMID:20144339

  15. A new surrogate modeling technique combining Kriging and polynomial chaos expansions – Application to uncertainty analysis in computational dosimetry

    SciTech Connect

    Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe

    2015-04-01

    In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representation of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.

  16. A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry

    NASA Astrophysics Data System (ADS)

    Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe

    2015-04-01

    In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representation of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.

  17. Conundrums with uncertainty factors.

    PubMed

    Cooke, Roger

    2010-03-01

    The practice of uncertainty factors as applied to noncancer endpoints in the IRIS database harkens back to traditional safety factors. In the era before risk quantification, these were used to build in a "margin of safety." As risk quantification takes hold, the safety factor methods yield to quantitative risk calculations to guarantee safety. Many authors believe that uncertainty factors can be given a probabilistic interpretation as ratios of response rates, and that the reference values computed according to the IRIS methodology can thus be converted to random variables whose distributions can be computed with Monte Carlo methods, based on the distributions of the uncertainty factors. Recent proposals from the National Research Council echo this view. Based on probabilistic arguments, several authors claim that the current practice of uncertainty factors is overprotective. When interpreted probabilistically, uncertainty factors entail very strong assumptions on the underlying response rates. For example, the factor for extrapolating from animal to human is the same whether the dosage is chronic or subchronic. Together with independence assumptions, these assumptions entail that the covariance matrix of the logged response rates is singular. In other words, the accumulated assumptions entail a log-linear dependence between the response rates. This in turn means that any uncertainty analysis based on these assumptions is ill-conditioned; it effectively computes uncertainty conditional on a set of zero probability. The practice of uncertainty factors is due for a thorough review. Two directions are briefly sketched, one based on standard regression models, and one based on nonparametric continuous Bayesian belief nets. PMID:20030767

  18. LIKELIHOOD-FREE COSMOLOGICAL INFERENCE WITH TYPE Ia SUPERNOVAE: APPROXIMATE BAYESIAN COMPUTATION FOR A COMPLETE TREATMENT OF UNCERTAINTY

    SciTech Connect

    Weyant, Anja; Wood-Vasey, W. Michael; Schafer, Chad

    2013-02-20

    Cosmological inference becomes increasingly difficult when complex data-generating processes cannot be modeled by simple probability distributions. With the ever-increasing size of data sets in cosmology, there is an increasing burden placed on adequate modeling; systematic errors in the model will dominate where previously these were swamped by statistical errors. For example, Gaussian distributions are an insufficient representation for errors in quantities like photometric redshifts. Likewise, it can be difficult to quantify analytically the distribution of errors that are introduced in complex fitting codes. Without a simple form for these distributions, it becomes difficult to accurately construct a likelihood function for the data as a function of parameters of interest. Approximate Bayesian computation (ABC) provides a means of probing the posterior distribution when direct calculation of a sufficiently accurate likelihood is intractable. ABC allows one to bypass direct calculation of the likelihood but instead relies upon the ability to simulate the forward process that generated the data. These simulations can naturally incorporate priors placed on nuisance parameters, and hence these can be marginalized in a natural way. We present and discuss ABC methods in the context of supernova cosmology using data from the SDSS-II Supernova Survey. Assuming a flat cosmology and constant dark energy equation of state, we demonstrate that ABC can recover an accurate posterior distribution. Finally, we show that ABC can still produce an accurate posterior distribution when we contaminate the sample with Type IIP supernovae.

  19. PIV uncertainty propagation

    NASA Astrophysics Data System (ADS)

    Sciacchitano, Andrea; Wieneke, Bernhard

    2016-08-01

    This paper discusses the propagation of the instantaneous uncertainty of PIV measurements to statistical and instantaneous quantities of interest derived from the velocity field. The expression of the uncertainty of vorticity, velocity divergence, mean value and Reynolds stresses is derived. It is shown that the uncertainty of vorticity and velocity divergence requires the knowledge of the spatial correlation between the error of the x and y particle image displacement, which depends upon the measurement spatial resolution. The uncertainty of statistical quantities is often dominated by the random uncertainty due to the finite sample size and decreases with the square root of the effective number of independent samples. Monte Carlo simulations are conducted to assess the accuracy of the uncertainty propagation formulae. Furthermore, three experimental assessments are carried out. In the first experiment, a turntable is used to simulate a rigid rotation flow field. The estimated uncertainty of the vorticity is compared with the actual vorticity error root-mean-square, with differences between the two quantities within 5–10% for different interrogation window sizes and overlap factors. A turbulent jet flow is investigated in the second experimental assessment. The reference velocity, which is used to compute the reference value of the instantaneous flow properties of interest, is obtained with an auxiliary PIV system, which features a higher dynamic range than the measurement system. Finally, the uncertainty quantification of statistical quantities is assessed via PIV measurements in a cavity flow. The comparison between estimated uncertainty and actual error demonstrates the accuracy of the proposed uncertainty propagation methodology.

  20. Quantification of Emission Factor Uncertainty

    EPA Science Inventory

    Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult...

  1. Visualization of Uncertainty

    NASA Astrophysics Data System (ADS)

    Jones, P. W.; Strelitz, R. A.

    2012-12-01

    The output of a simulation is best comprehended through the agency and methods of visualization, but a vital component of good science is knowledge of uncertainty. While great strides have been made in the quantification of uncertainty, especially in simulation, there is still a notable gap: there is no widely accepted means of simultaneously viewing the data and the associated uncertainty in one pane. Visualization saturates the screen, using the full range of color, shadow, opacity and tricks of perspective to display even a single variable. There is no room in the visualization expert's repertoire left for uncertainty. We present a method of visualizing uncertainty without sacrificing the clarity and power of the underlying visualization that works as well in 3-D and time-varying visualizations as it does in 2-D. At its heart, it relies on a principal tenet of continuum mechanics, replacing the notion of value at a point with a more diffuse notion of density as a measure of content in a region. First, the uncertainties calculated or tabulated at each point are transformed into a piecewise continuous field of uncertainty density . We next compute a weighted Voronoi tessellation of a user specified N convex polygonal/polyhedral cells such that each cell contains the same amount of uncertainty as defined by . The problem thus devolves into minimizing . Computation of such a spatial decomposition is O(N*N ), and can be computed iteratively making it possible to update easily over time as well as faster. The polygonal mesh does not interfere with the visualization of the data and can be easily toggled on or off. In this representation, a small cell implies a great concentration of uncertainty, and conversely. The content weighted polygons are identical to the cartogram familiar to the information visualization community in the depiction of things voting results per stat. Furthermore, one can dispense with the mesh or edges entirely to be replaced by symbols or glyphs

  2. Teaching Uncertainties

    ERIC Educational Resources Information Center

    Duerdoth, Ian

    2009-01-01

    The subject of uncertainties (sometimes called errors) is traditionally taught (to first-year science undergraduates) towards the end of a course on statistics that defines probability as the limit of many trials, and discusses probability distribution functions and the Gaussian distribution. We show how to introduce students to the concepts of…

  3. Calibration Under Uncertainty.

    SciTech Connect

    Swiler, Laura Painton; Trucano, Timothy Guy

    2005-03-01

    This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.

  4. Uncertainty, joint uncertainty, and the quantum uncertainty principle

    NASA Astrophysics Data System (ADS)

    Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad

    2016-03-01

    Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found.

  5. Orbital State Uncertainty Realism

    NASA Astrophysics Data System (ADS)

    Horwood, J.; Poore, A. B.

    2012-09-01

    Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten

  6. Uncertainty-induced quantum nonlocality

    NASA Astrophysics Data System (ADS)

    Wu, Shao-xiong; Zhang, Jun; Yu, Chang-shui; Song, He-shan

    2014-01-01

    Based on the skew information, we present a quantity, uncertainty-induced quantum nonlocality (UIN) to measure the quantum correlation. It can be considered as the updated version of the original measurement-induced nonlocality (MIN) preserving the good computability but eliminating the non-contractivity problem. For 2×d-dimensional state, it is shown that UIN can be given by a closed form. In addition, we also investigate the maximal uncertainty-induced nonlocality.

  7. Uncertainty Calculation for Spectral-Responsivity Measurements

    PubMed Central

    Lehman, John H; Wang, CM; Dowell, Marla L; Hadler, Joshua A

    2009-01-01

    This paper discusses a procedure for measuring the absolute spectral responsivity of optical-fiber power meters and computation of the calibration uncertainty. The procedure reconciles measurement results associated with a monochromator-based measurement system with those obtained with laser sources coupled with optical fiber. Relative expanded uncertainties based on the methods from the Guide to the Expression of Uncertainty in Measurement and from Supplement 1 to the “Guide to the Expression of Uncertainty in Measurement”-Propagation of Distributions using a Monte Carlo Method are derived and compared. An example is used to illustrate the procedures and calculation of uncertainties.

  8. Uncertainty analysis of minimum vessel liquid inventory during a small-break LOCA in a B W Plant: An application of the CSAU methodology using the RELAP5/MOD3 computer code

    SciTech Connect

    Ortiz, M G; Ghan, L S

    1992-12-01

    The Nuclear Regulatory Commission (NRC) revised the emergency core cooling system licensing rule to allow the use of best estimate computer codes, provided the uncertainty of the calculations are quantified and used in the licensing and regulation process. The NRC developed a generic methodology called Code Scaling, Applicability, and Uncertainty (CSAU) to evaluate best estimate code uncertainties. The objective of this work was to adapt and demonstrate the CSAU methodology for a small-break loss-of-coolant accident (SBLOCA) in a Pressurized Water Reactor of Babcock Wilcox Company lowered loop design using RELAP5/MOD3 as the simulation tool. The CSAU methodology was successfully demonstrated for the new set of variants defined in this project (scenario, plant design, code). However, the robustness of the reactor design to this SBLOCA scenario limits the applicability of the specific results to other plants or scenarios. Several aspects of the code were not exercised because the conditions of the transient never reached enough severity. The plant operator proved to be a determining factor in the course of the transient scenario, and steps were taken to include the operator in the model, simulation, and analyses.

  9. Messaging climate change uncertainty

    NASA Astrophysics Data System (ADS)

    Cooke, Roger M.

    2015-01-01

    Climate change is full of uncertainty and the messengers of climate science are not getting the uncertainty narrative right. To communicate uncertainty one must first understand it, and then avoid repeating the mistakes of the past.

  10. Ozone Uncertainties Study Algorithm (OUSA)

    NASA Technical Reports Server (NTRS)

    Bahethi, O. P.

    1982-01-01

    An algorithm to carry out sensitivities, uncertainties and overall imprecision studies to a set of input parameters for a one dimensional steady ozone photochemistry model is described. This algorithm can be used to evaluate steady state perturbations due to point source or distributed ejection of H2O, CLX, and NOx, besides, varying the incident solar flux. This algorithm is operational on IBM OS/360-91 computer at NASA/Goddard Space Flight Center's Science and Applications Computer Center (SACC).

  11. Ozone Uncertainties Study Algorithm (OUSA)

    NASA Astrophysics Data System (ADS)

    Bahethi, O. P.

    An algorithm to carry out sensitivities, uncertainties and overall imprecision studies to a set of input parameters for a one dimensional steady ozone photochemistry model is described. This algorithm can be used to evaluate steady state perturbations due to point source or distributed ejection of H2O, CLX, and NOx, besides, varying the incident solar flux. This algorithm is operational on IBM OS/360-91 computer at NASA/Goddard Space Flight Center's Science and Applications Computer Center (SACC).

  12. Uncertainty quantification and error analysis

    SciTech Connect

    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.

  13. Uncertainty of empirical correlation equations

    NASA Astrophysics Data System (ADS)

    Feistel, R.; Lovell-Smith, J. W.; Saunders, P.; Seitz, S.

    2016-08-01

    The International Association for the Properties of Water and Steam (IAPWS) has published a set of empirical reference equations of state, forming the basis of the 2010 Thermodynamic Equation of Seawater (TEOS-10), from which all thermodynamic properties of seawater, ice, and humid air can be derived in a thermodynamically consistent manner. For each of the equations of state, the parameters have been found by simultaneously fitting equations for a range of different derived quantities using large sets of measurements of these quantities. In some cases, uncertainties in these fitted equations have been assigned based on the uncertainties of the measurement results. However, because uncertainties in the parameter values have not been determined, it is not possible to estimate the uncertainty in many of the useful quantities that can be calculated using the parameters. In this paper we demonstrate how the method of generalised least squares (GLS), in which the covariance of the input data is propagated into the values calculated by the fitted equation, and in particular into the covariance matrix of the fitted parameters, can be applied to one of the TEOS-10 equations of state, namely IAPWS-95 for fluid pure water. Using the calculated parameter covariance matrix, we provide some preliminary estimates of the uncertainties in derived quantities, namely the second and third virial coefficients for water. We recommend further investigation of the GLS method for use as a standard method for calculating and propagating the uncertainties of values computed from empirical equations.

  14. Uncertainties in maximum entropy (ME) reconstructions

    SciTech Connect

    Bevensee, R.M.

    1987-04-01

    This paper summarizes recent work done at the Lawrence Livermore National Laboratory by the writer on the effects of statistical uncertainty and image noise in Boltzmann ME inversion. The object of this work is the formulation of a Theory of Uncertainties which would allow one to compute confidence intervals for an object parameter near an ME reference value.

  15. Uncertainty and Engagement with Learning Games

    ERIC Educational Resources Information Center

    Howard-Jones, Paul A.; Demetriou, Skevi

    2009-01-01

    Uncertainty may be an important component of the motivation provided by learning games, especially when associated with gaming rather than learning. Three studies are reported that explore the influence of gaming uncertainty on engagement with computer-based learning games. In the first study, children (10-11 years) played a simple maths quiz.…

  16. Quantification of uncertainties in composites

    NASA Technical Reports Server (NTRS)

    Liaw, D. G.; Singhal, S. N.; Murthy, P. L. N.; Chamis, Christos C.

    1993-01-01

    An integrated methodology is developed for computationally simulating the probabilistic composite material properties at all composite scales. The simulation requires minimum input consisting of the description of uncertainties at the lowest scale (fiber and matrix constituents) of the composite and in the fabrication process variables. The methodology allows the determination of the sensitivity of the composite material behavior to all the relevant primitive variables. This information is crucial for reducing the undesirable scatter in composite behavior at its macro scale by reducing the uncertainties in the most influential primitive variables at the micro scale. The methodology is computationally efficient. The computational time required by the methodology described herein is an order of magnitude less than that for Monte Carlo Simulation. The methodology has been implemented into the computer code PICAN (Probabilistic Integrated Composite ANalyzer). The accuracy and efficiency of the methodology/code are demonstrated by simulating the uncertainties in the heat-transfer, thermal, and mechanical properties of a typical laminate and comparing the results with the Monte Carlo simulation method and experimental data. The important observation is that the computational simulation for probabilistic composite mechanics has sufficient flexibility to capture the observed scatter in composite properties.

  17. Dyke leakage localization and hydraulic permeability estimation through self-potential and hydro-acoustic measurements: Self-potential 'abacus' diagram for hydraulic permeability estimation and uncertainty computation

    NASA Astrophysics Data System (ADS)

    Bolève, A.; Vandemeulebrouck, J.; Grangeon, J.

    2012-11-01

    In the present study, we propose the combination of two geophysical techniques, which we have applied to a dyke located in southeastern France that has a visible downstream flood area: the self-potential (SP) and hydro-acoustic methods. These methods are sensitive to two different types of signals: electric signals and water-soil pressure disturbances, respectively. The advantages of the SP technique lie in the high rate of data acquisition, which allows assessment of long dykes, and direct diagnosis in terms of leakage area delimitation and quantification. Coupled with punctual hydro-acoustic cartography, a leakage position can be precisely located, therefore allowing specific remediation decisions with regard to the results of the geophysical investigation. Here, the precise localization of leakage from an earth dyke has been identified using SP and hydro-acoustic signals, with the permeability of the preferential fluid flow area estimated by forward SP modeling. Moreover, we propose a general 'abacus' diagram for the estimation of hydraulic permeability of dyke leakage according to the magnitude of over water SP anomalies and the associated uncertainty.

  18. Picturing Data With Uncertainty

    NASA Technical Reports Server (NTRS)

    Kao, David; Love, Alison; Dungan, Jennifer L.; Pang, Alex

    2004-01-01

    NASA is in the business of creating maps for scientific purposes to represent important biophysical or geophysical quantities over space and time. For example, maps of surface temperature over the globe tell scientists where and when the Earth is heating up; regional maps of the greenness of vegetation tell scientists where and when plants are photosynthesizing. There is always uncertainty associated with each value in any such map due to various factors. When uncertainty is fully modeled, instead of a single value at each map location, there is a distribution expressing a set of possible outcomes at each location. We consider such distribution data as multi-valued data since it consists of a collection of values about a single variable. Thus, a multi-valued data represents both the map and its uncertainty. We have been working on ways to visualize spatial multi-valued data sets effectively for fields with regularly spaced units or grid cells such as those in NASA's Earth science applications. A new way to display distributions at multiple grid locations is to project the distributions from an individual row, column or other user-selectable straight transect from the 2D domain. First at each grid cell in a given slice (row, column or transect), we compute a smooth density estimate from the underlying data. Such a density estimate for the probability density function (PDF) is generally more useful than a histogram, which is a classic density estimate. Then, the collection of PDFs along a given slice are presented vertically above the slice and form a wall. To minimize occlusion of intersecting slices, the corresponding walls are positioned at the far edges of the boundary. The PDF wall depicts the shapes of the distributions very dearly since peaks represent the modes (or bumps) in the PDFs. We've defined roughness as the number of peaks in the distribution. Roughness is another useful summary information for multimodal distributions. The uncertainty of the multi

  19. Uncertainties in hydrocarbon charge prediction

    NASA Astrophysics Data System (ADS)

    Visser, W.; Bell, A.

    Computer simulations allow the prediction of hydrocarbon volumes, composition and charge timing in undrilled petroleum prospects. Whereas different models may give different hydrocarbon charge predictions, it has now become evident that a dominant cause of erroneous predictions is the poor quality of input data. The main culprit for prediction errors is the uncertainty in the initial hydrogen index (H/C) of the source rock. A 10% uncertainty in the H/C may lead to 50% error in the predicted hydrocarbon volumes, and associated gas-oil ratio. Similarly, uncertainties in the maximum burial temperature and the kinetics of hydrocarbon generation may lead to 20-50% error. Despite this, charge modelling can have great value for the ranking of prospects in the same area with comparable geological histories.

  20. Aleatoric and epistemic uncertainties in sampling based nuclear data uncertainty and sensitivity analyses

    SciTech Connect

    Zwermann, W.; Krzykacz-Hausmann, B.; Gallner, L.; Klein, M.; Pautz, A.; Velkov, K.

    2012-07-01

    Sampling based uncertainty and sensitivity analyses due to epistemic input uncertainties, i.e. to an incomplete knowledge of uncertain input parameters, can be performed with arbitrary application programs to solve the physical problem under consideration. For the description of steady-state particle transport, direct simulations of the microscopic processes with Monte Carlo codes are often used. This introduces an additional source of uncertainty, the aleatoric sampling uncertainty, which is due to the randomness of the simulation process performed by sampling, and which adds to the total combined output sampling uncertainty. So far, this aleatoric part of uncertainty is minimized by running a sufficiently large number of Monte Carlo histories for each sample calculation, thus making its impact negligible as compared to the impact from sampling the epistemic uncertainties. Obviously, this process may cause high computational costs. The present paper shows that in many applications reliable epistemic uncertainty results can also be obtained with substantially lower computational effort by performing and analyzing two appropriately generated series of samples with much smaller number of Monte Carlo histories each. The method is applied along with the nuclear data uncertainty and sensitivity code package XSUSA in combination with the Monte Carlo transport code KENO-Va to various critical assemblies and a full scale reactor calculation. It is shown that the proposed method yields output uncertainties and sensitivities equivalent to the traditional approach, with a high reduction of computing time by factors of the magnitude of 100. (authors)

  1. Uncertainty as knowledge

    PubMed Central

    Lewandowsky, Stephan; Ballard, Timothy; Pancost, Richard D.

    2015-01-01

    This issue of Philosophical Transactions examines the relationship between scientific uncertainty about climate change and knowledge. Uncertainty is an inherent feature of the climate system. Considerable effort has therefore been devoted to understanding how to effectively respond to a changing, yet uncertain climate. Politicians and the public often appeal to uncertainty as an argument to delay mitigative action. We argue that the appropriate response to uncertainty is exactly the opposite: uncertainty provides an impetus to be concerned about climate change, because greater uncertainty increases the risks associated with climate change. We therefore suggest that uncertainty can be a source of actionable knowledge. We survey the papers in this issue, which address the relationship between uncertainty and knowledge from physical, economic and social perspectives. We also summarize the pervasive psychological effects of uncertainty, some of which may militate against a meaningful response to climate change, and we provide pointers to how those difficulties may be ameliorated. PMID:26460108

  2. Assessment of the computational uncertainty of temperature rise and SAR in the eyes and brain under far-field exposure from 1 to 10 GHz

    NASA Astrophysics Data System (ADS)

    Laakso, Ilkka

    2009-06-01

    This paper presents finite-difference time-domain (FDTD) calculations of specific absorption rate (SAR) values in the head under plane-wave exposure from 1 to 10 GHz using a resolution of 0.5 mm in adult male and female voxel models. Temperature rise due to the power absorption is calculated by the bioheat equation using a multigrid method solver. The computational accuracy is investigated by repeating the calculations with resolutions of 1 mm and 2 mm and comparing the results. Cubically averaged 10 g SAR in the eyes and brain and eye-averaged SAR are calculated and compared to the corresponding temperature rise as well as the recommended limits for exposure. The results suggest that 2 mm resolution should only be used for frequencies smaller than 2.5 GHz, and 1 mm resolution only under 5 GHz. Morphological differences in models seemed to be an important cause of variation: differences in results between the two different models were usually larger than the computational error due to the grid resolution, and larger than the difference between the results for open and closed eyes. Limiting the incident plane-wave power density to smaller than 100 W m-2 was sufficient for ensuring that the temperature rise in the eyes and brain were less than 1 °C in the whole frequency range.

  3. Experimental uncertainty estimation and statistics for data having interval uncertainty.

    SciTech Connect

    Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)

    2007-05-01

    This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.

  4. Coastal 'Big Data' and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

    NASA Astrophysics Data System (ADS)

    Millie, David F.; Weckman, Gary R.; Young, William A.; Ivey, James E.; Fries, David P.; Ardjmand, Ehsan; Fahnenstiel, Gary L.

    2013-07-01

    Coastal monitoring has become reliant upon automated sensors for data acquisition. Such a technical commitment comes with a cost; particularly, the generation of large, high-dimensional data streams ('Big Data') that personnel must search through to identify data structures. Nature-inspired computation, inclusive of artificial neural networks (ANNs), affords the unearthing of complex, recurring patterns within sizable data volumes. In 2009, select meteorological and hydrological data were acquired via autonomous instruments in Sarasota Bay, Florida (USA). ANNs estimated continuous chlorophyll (CHL) a concentrations from abiotic predictors, with correlations between measured:modeled concentrations >0.90 and model efficiencies ranging from 0.80 to 0.90. Salinity and water temperature were the principal influences for modeled CHL within the Bay; concentrations steadily increased at temperatures >28° C and were greatest at salinities <36 (maximizing at ca. 35.3). Categorical ANNs modeled CHL classes of 6.1 and 11 μg CHL L-1 (representative of local and state-imposed constraint thresholds, respectively), with an accuracy of ca. 83% and class precision ranging from 0.79 to 0.91. The occurrence likelihood of concentrations > 6.1 μg CHL L-1 maximized at a salinity of ca. 36.3 and a temperature of ca. 29.5 °C. A 10th-order Chebyshev bivariate polynomial equation was fit (adj. r2 = 0.99, p < 0.001) to a three-dimensional response surface portraying modeled CHL concentrations, conditional to the temperature-salinity interaction. The TREPAN algorithm queried a continuous ANN to extract a decision tree for delineation of CHL classes; turbidity, temperature, and salinity (and to lesser degrees, wind speed, wind/current direction, irradiance, and urea-nitrogen) were key variables for quantitative rules in tree formalisms. Taken together, computations enabled knowledge provision for and quantifiable representations of the non-linear relationships between environmental

  5. 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.

  6. Credible Software and Simulation Uncertainty

    NASA Technical Reports Server (NTRS)

    Mehta, Unmeel B.; Nixon, David (Technical Monitor)

    1998-01-01

    The utility of software primarily depends on its reliability and performance; whereas, its significance depends solely on its credibility for intended use. The credibility of simulations confirms the credibility of software. The level of veracity and the level of validity of simulations determine the degree of credibility of simulations. The process of assessing this credibility in fields such as computational mechanics (CM) differs from that followed by the Defense Modeling and Simulation Office in operations research. Verification and validation (V&V) of CM simulations is not the same as V&V of CM software. Uncertainty is the measure of simulation credibility. Designers who use software are concerned with management of simulation uncertainty. Terminology and concepts are presented with a few examples from computational fluid dynamics.

  7. Estimating uncertainties in complex joint inverse problems

    NASA Astrophysics Data System (ADS)

    Afonso, Juan Carlos

    2016-04-01

    Sources of uncertainty affecting geophysical inversions can be classified either as reflective (i.e. the practitioner is aware of her/his ignorance) or non-reflective (i.e. the practitioner does not know that she/he does not know!). Although we should be always conscious of the latter, the former are the ones that, in principle, can be estimated either empirically (by making measurements or collecting data) or subjectively (based on the experience of the researchers). For complex parameter estimation problems in geophysics, subjective estimation of uncertainty is the most common type. In this context, probabilistic (aka Bayesian) methods are commonly claimed to offer a natural and realistic platform from which to estimate model uncertainties. This is because in the Bayesian approach, errors (whatever their nature) can be naturally included as part of the global statistical model, the solution of which represents the actual solution to the inverse problem. However, although we agree that probabilistic inversion methods are the most powerful tool for uncertainty estimation, the common claim that they produce "realistic" or "representative" uncertainties is not always justified. Typically, ALL UNCERTAINTY ESTIMATES ARE MODEL DEPENDENT, and therefore, besides a thorough characterization of experimental uncertainties, particular care must be paid to the uncertainty arising from model errors and input uncertainties. We recall here two quotes by G. Box and M. Gunzburger, respectively, of special significance for inversion practitioners and for this session: "…all models are wrong, but some are useful" and "computational results are believed by no one, except the person who wrote the code". In this presentation I will discuss and present examples of some problems associated with the estimation and quantification of uncertainties in complex multi-observable probabilistic inversions, and how to address them. Although the emphasis will be on sources of uncertainty related

  8. Fission Spectrum Related Uncertainties

    SciTech Connect

    G. Aliberti; I. Kodeli; G. Palmiotti; M. Salvatores

    2007-10-01

    The paper presents a preliminary uncertainty analysis related to potential uncertainties on the fission spectrum data. Consistent results are shown for a reference fast reactor design configuration and for experimental thermal configurations. However the results obtained indicate the need for further analysis, in particular in terms of fission spectrum uncertainty data assessment.

  9. Universal Uncertainty Relations

    NASA Astrophysics Data System (ADS)

    Gour, Gilad

    2014-03-01

    Uncertainty relations are a distinctive characteristic of quantum theory that imposes intrinsic limitations on the precision with which physical properties can be simultaneously determined. The modern work on uncertainty relations employs entropic measures to quantify the lack of knowledge associated with measuring non-commuting observables. However, I will show here that there is no fundamental reason for using entropies as quantifiers; in fact, any functional relation that characterizes the uncertainty of the measurement outcomes can be used to define an uncertainty relation. Starting from a simple assumption that any measure of uncertainty is non-decreasing under mere relabeling of the measurement outcomes, I will show that Schur-concave functions are the most general uncertainty quantifiers. I will then introduce a novel fine-grained uncertainty relation written in terms of a majorization relation, which generates an infinite family of distinct scalar uncertainty relations via the application of arbitrary measures of uncertainty. This infinite family of uncertainty relations includes all the known entropic uncertainty relations, but is not limited to them. In this sense, the relation is universally valid and captures the essence of the uncertainty principle in quantum theory. This talk is based on a joint work with Shmuel Friedland and Vlad Gheorghiu. This research is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada and by the Pacific Institute for Mathematical Sciences (PIMS).

  10. 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.

  11. Uncertainty Propagation for Terrestrial Mobile Laser Scanner

    NASA Astrophysics Data System (ADS)

    Mezian, c.; Vallet, Bruno; Soheilian, Bahman; Paparoditis, Nicolas

    2016-06-01

    Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

  12. MOUSE (MODULAR ORIENTED UNCERTAINTY SYSTEM): A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM. OPERATIONAL MANUAL.

    EPA Science Inventory

    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...

  13. Pore Velocity Estimation Uncertainties

    NASA Astrophysics Data System (ADS)

    Devary, J. L.; Doctor, P. G.

    1982-08-01

    Geostatistical data analysis techniques were used to stochastically model the spatial variability of groundwater pore velocity in a potential waste repository site. Kriging algorithms were applied to Hanford Reservation data to estimate hydraulic conductivities, hydraulic head gradients, and pore velocities. A first-order Taylor series expansion for pore velocity was used to statistically combine hydraulic conductivity, hydraulic head gradient, and effective porosity surfaces and uncertainties to characterize the pore velocity uncertainty. Use of these techniques permits the estimation of pore velocity uncertainties when pore velocity measurements do not exist. Large pore velocity estimation uncertainties were found to be located in the region where the hydraulic head gradient relative uncertainty was maximal.

  14. Uncertainty estimation and prediction for interdisciplinary ocean dynamics

    SciTech Connect

    Lermusiaux, Pierre F.J. . E-mail: pierrel@pacific.harvard.edu

    2006-09-01

    Scientific computations for the quantification, estimation and prediction of uncertainties for ocean dynamics are developed and exemplified. Primary characteristics of ocean data, models and uncertainties are reviewed and quantitative data assimilation concepts defined. Challenges involved in realistic data-driven simulations of uncertainties for four-dimensional interdisciplinary ocean processes are emphasized. Equations governing uncertainties in the Bayesian probabilistic sense are summarized. Stochastic forcing formulations are introduced and a new stochastic-deterministic ocean model is presented. The computational methodology and numerical system, Error Subspace Statistical Estimation, that is used for the efficient estimation and prediction of oceanic uncertainties based on these equations is then outlined. Capabilities of the ESSE system are illustrated in three data-assimilative applications: estimation of uncertainties for physical-biogeochemical fields, transfers of ocean physics uncertainties to acoustics, and real-time stochastic ensemble predictions with assimilation of a wide range of data types. Relationships with other modern uncertainty quantification schemes and promising research directions are discussed.

  15. Treatment of uncertainties in the geologic disposal of radioactive waste

    SciTech Connect

    Cranwell, R.M.

    1985-12-31

    Uncertainty in the analysis of geologic waste disposal is generally considered to have three primary components: (1) computer code/model uncertainty, (2) model parameter uncertainty, and (3) scenario uncertainty. Computer code/model uncertainty arises from problems associated with determination of appropriate parameters for use in model construction, mathematical formulatin of models, and numerical techniques used in conjunction with the mathematical formulation of models. Model parameter uncertainty arises from problems associated with selection of appropriate values for model input, data interpretation and possible misuse of data, and variation of data. Scenario uncertainty arises from problems associated with the "completeness` of scenarios, the definition of parameters which describe scenarios, and the rate or probability of scenario occurrence. The preceding sources of uncertainty are discussed below.

  16. Analysis of the Uncertainty in the Computation of Receiver Functions and Improvement in the Estimation of Receiver, PP and SS functions

    NASA Astrophysics Data System (ADS)

    Huang, X.; Gurrola, H.

    2013-12-01

    methods. All of these methods performed well in terms of stdev but we chose ARU for its high quality data and low signal to noise ratios (the average S/N ratio for these data were 4%). With real data, we tend to assume the method that has the lowest stdev is the best. But stdev does not account for a systematic bias toward incorrect values. In this case the LSD once again had the lowest stdev in computed amplitudes of Pds phases but it had the smallest values. But the FID, FWLD and MID tended to produce the largest amplitude while the LSD and TID tended toward the lower amplitudes. Considering that in the synthetics all these methods showed bias toward low amplitude, we believe that with real data those methods producing the largest amplitudes will be closest to the 'true values' and that is a better measure of the better method than a small stdev in amplitude estimates. We will also present results for applying TID and FID methods to the production of PP and SS precursor functions. When applied to these data, it is possible to moveout correct the cross-correlation functions before extracting the signal from each PdP (or SdS) phase in these data. As a result a much cleaner Earth function is produced and feequency content is significantly improved.

  17. Uncertainty Quantification in Climate Modeling

    NASA Astrophysics Data System (ADS)

    Sargsyan, K.; Safta, C.; Berry, R.; Debusschere, B.; Najm, H.

    2011-12-01

    We address challenges that sensitivity analysis and uncertainty quantification methods face when dealing with complex computational models. In particular, climate models are computationally expensive and typically depend on a large number of input parameters. We consider the Community Land Model (CLM), which consists of a nested computational grid hierarchy designed to represent the spatial heterogeneity of the land surface. Each computational cell can be composed of multiple land types, and each land type can incorporate one or more sub-models describing the spatial and depth variability. Even for simulations at a regional scale, the computational cost of a single run is quite high and the number of parameters that control the model behavior is very large. Therefore, the parameter sensitivity analysis and uncertainty propagation face significant difficulties for climate models. This work employs several algorithmic avenues to address some of the challenges encountered by classical uncertainty quantification methodologies when dealing with expensive computational models, specifically focusing on the CLM as a primary application. First of all, since the available climate model predictions are extremely sparse due to the high computational cost of model runs, we adopt a Bayesian framework that effectively incorporates this lack-of-knowledge as a source of uncertainty, and produces robust predictions with quantified uncertainty even if the model runs are extremely sparse. In particular, we infer Polynomial Chaos spectral expansions that effectively encode the uncertain input-output relationship and allow efficient propagation of all sources of input uncertainties to outputs of interest. Secondly, the predictability analysis of climate models strongly suffers from the curse of dimensionality, i.e. the large number of input parameters. While single-parameter perturbation studies can be efficiently performed in a parallel fashion, the multivariate uncertainty analysis

  18. Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

    NASA Technical Reports Server (NTRS)

    Green, Lawrence L.; Lin, Hong-Zong; Khalessi, Mohammad R.

    2002-01-01

    Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

  19. Interpolation Method Needed for Numerical Uncertainty

    NASA Technical Reports Server (NTRS)

    Groves, Curtis E.; Ilie, Marcel; Schallhorn, Paul A.

    2014-01-01

    Using Computational Fluid Dynamics (CFD) to predict a flow field is an approximation to the exact problem and uncertainties exist. There is a method to approximate the errors in CFD via Richardson's Extrapolation. This method is based off of progressive grid refinement. To estimate the errors, the analyst must interpolate between at least three grids. This paper describes a study to find an appropriate interpolation scheme that can be used in Richardson's extrapolation or other uncertainty method to approximate errors.

  20. Design Optimization of Composite Structures under Uncertainty

    NASA Technical Reports Server (NTRS)

    Haftka, Raphael T.

    2003-01-01

    Design optimization under uncertainty is computationally expensive and is also challenging in terms of alternative formulation. The work under the grant focused on developing methods for design against uncertainty that are applicable to composite structural design with emphasis on response surface techniques. Applications included design of stiffened composite plates for improved damage tolerance, the use of response surfaces for fitting weights obtained by structural optimization, and simultaneous design of structure and inspection periods for fail-safe structures.

  1. Uncertainty in hydrological signatures

    NASA Astrophysics Data System (ADS)

    Westerberg, I. K.; McMillan, H. K.

    2015-09-01

    Information about rainfall-runoff processes is essential for hydrological analyses, modelling and water-management applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data - including measurement inaccuracy and representativeness as well as errors relating to data management - propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty. We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment. We found that the uncertainties were often large (i.e. typical intervals of ±10-40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.

  2. Uncertainty in hydrological signatures

    NASA Astrophysics Data System (ADS)

    Westerberg, I. K.; McMillan, H. K.

    2015-04-01

    Information about rainfall-runoff processes is essential for hydrological analyses, modelling and water-management applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, including for catchment classification, model calibration and change detection. Uncertainties in the observed data - including measurement inaccuracy and representativeness as well as errors relating to data management - propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty. We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment. We found that the uncertainties were often large (i.e. typical intervals of ±10-40% relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.

  3. Measurement Uncertainty and Probability

    NASA Astrophysics Data System (ADS)

    Willink, Robin

    2013-02-01

    Part I. Principles: 1. Introduction; 2. Foundational ideas in measurement; 3. Components of error or uncertainty; 4. Foundational ideas in probability and statistics; 5. The randomization of systematic errors; 6. Beyond the standard confidence interval; Part II. Evaluation of Uncertainty: 7. Final preparation; 8. Evaluation using the linear approximation; 9. Evaluation without the linear approximations; 10. Uncertainty information fit for purpose; Part III. Related Topics: 11. Measurement of vectors and functions; 12. Why take part in a measurement comparison?; 13. Other philosophies; 14. An assessment of objective Bayesian methods; 15. A guide to the expression of uncertainty in measurement; 16. Measurement near a limit - an insoluble problem?; References; Index.

  4. Uncertainty of decibel levels.

    PubMed

    Taraldsen, Gunnar; Berge, Truls; Haukland, Frode; Lindqvist, Bo Henry; Jonasson, Hans

    2015-09-01

    The mean sound exposure level from a source is routinely estimated by the mean of the observed sound exposures from repeated measurements. A formula for the standard uncertainty based on the Guide to the expression of Uncertainty in Measurement (GUM) is derived. An alternative formula is derived for the case where the GUM method fails. The formulas are applied on several examples, and compared with a Monte Carlo calculation of the standard uncertainty. The recommended formula can be seen simply as a convenient translation of the uncertainty on an energy scale into the decibel level scale, but with a theoretical foundation. PMID:26428824

  5. Uncertainty in hydrological signatures

    NASA Astrophysics Data System (ADS)

    McMillan, Hilary; Westerberg, Ida

    2015-04-01

    Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty

  6. Impact of uncertainty on modeling and testing

    NASA Technical Reports Server (NTRS)

    Coleman, Hugh W.; Brown, Kendall K.

    1995-01-01

    A thorough understanding of the uncertainties associated with the modeling and testing of the Space Shuttle Main Engine (SSME) Engine will greatly aid decisions concerning hardware performance and future development efforts. This report will describe the determination of the uncertainties in the modeling and testing of the Space Shuttle Main Engine test program at the Technology Test Bed facility at Marshall Space Flight Center. Section 2 will present a summary of the uncertainty analysis methodology used and discuss the specific applications to the TTB SSME test program. Section 3 will discuss the application of the uncertainty analysis to the test program and the results obtained. Section 4 presents the results of the analysis of the SSME modeling effort from an uncertainty analysis point of view. The appendices at the end of the report contain a significant amount of information relative to the analysis, including discussions of venturi flowmeter data reduction and uncertainty propagation, bias uncertainty documentations, technical papers published, the computer code generated to determine the venturi uncertainties, and the venturi data and results used in the analysis.

  7. Electoral Knowledge and Uncertainty.

    ERIC Educational Resources Information Center

    Blood, R. Warwick; And Others

    Research indicates that the media play a role in shaping the information that voters have about election options. Knowledge of those options has been related to actual vote, but has not been shown to be strongly related to uncertainty. Uncertainty, however, does seem to motivate voters to engage in communication activities, some of which may…

  8. MOUSE UNCERTAINTY ANALYSIS SYSTEM

    EPA Science Inventory

    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...

  9. Uncertainty in flood risk mapping

    NASA Astrophysics Data System (ADS)

    Gonçalves, Luisa M. S.; Fonte, Cidália C.; Gomes, Ricardo

    2014-05-01

    A flood refers to a sharp increase of water level or volume in rivers and seas caused by sudden rainstorms or melting ice due to natural factors. In this paper, the flooding of riverside urban areas caused by sudden rainstorms will be studied. In this context, flooding occurs when the water runs above the level of the minor river bed and enters the major river bed. The level of the major bed determines the magnitude and risk of the flooding. The prediction of the flooding extent is usually deterministic, and corresponds to the expected limit of the flooded area. However, there are many sources of uncertainty in the process of obtaining these limits, which influence the obtained flood maps used for watershed management or as instruments for territorial and emergency planning. In addition, small variations in the delineation of the flooded area can be translated into erroneous risk prediction. Therefore, maps that reflect the uncertainty associated with the flood modeling process have started to be developed, associating a degree of likelihood with the boundaries of the flooded areas. In this paper an approach is presented that enables the influence of the parameters uncertainty to be evaluated, dependent on the type of Land Cover Map (LCM) and Digital Elevation Model (DEM), on the estimated values of the peak flow and the delineation of flooded areas (different peak flows correspond to different flood areas). The approach requires modeling the DEM uncertainty and its propagation to the catchment delineation. The results obtained in this step enable a catchment with fuzzy geographical extent to be generated, where a degree of possibility of belonging to the basin is assigned to each elementary spatial unit. Since the fuzzy basin may be considered as a fuzzy set, the fuzzy area of the basin may be computed, generating a fuzzy number. The catchment peak flow is then evaluated using fuzzy arithmetic. With this methodology a fuzzy number is obtained for the peak flow

  10. Uncertainty Quantification Techniques of SCALE/TSUNAMI

    SciTech Connect

    Rearden, Bradley T; Mueller, Don

    2011-01-01

    The Standardized Computer Analysis for Licensing Evaluation (SCALE) code system developed at Oak Ridge National Laboratory (ORNL) includes Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI). The TSUNAMI code suite can quantify the predicted change in system responses, such as k{sub eff}, reactivity differences, or ratios of fluxes or reaction rates, due to changes in the energy-dependent, nuclide-reaction-specific cross-section data. Where uncertainties in the neutron cross-section data are available, the sensitivity of the system to the cross-section data can be applied to propagate the uncertainties in the cross-section data to an uncertainty in the system response. Uncertainty quantification is useful for identifying potential sources of computational biases and highlighting parameters important to code validation. Traditional validation techniques often examine one or more average physical parameters to characterize a system and identify applicable benchmark experiments. However, with TSUNAMI correlation coefficients are developed by propagating the uncertainties in neutron cross-section data to uncertainties in the computed responses for experiments and safety applications through sensitivity coefficients. The bias in the experiments, as a function of their correlation coefficient with the intended application, is extrapolated to predict the bias and bias uncertainty in the application through trending analysis or generalized linear least squares techniques, often referred to as 'data adjustment.' Even with advanced tools to identify benchmark experiments, analysts occasionally find that the application models include some feature or material for which adequately similar benchmark experiments do not exist to support validation. For example, a criticality safety analyst may want to take credit for the presence of fission products in spent nuclear fuel. In such cases, analysts sometimes rely on 'expert judgment' to select an

  11. Predictive uncertainty in auditory sequence processing.

    PubMed

    Hansen, Niels Chr; Pearce, Marcus T

    2014-01-01

    Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty-a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music. PMID:25295018

  12. Economic uncertainty and econophysics

    NASA Astrophysics Data System (ADS)

    Schinckus, Christophe

    2009-10-01

    The objective of this paper is to provide a methodological link between econophysics and economics. I will study a key notion of both fields: uncertainty and the ways of thinking about it developed by the two disciplines. After having presented the main economic theories of uncertainty (provided by Knight, Keynes and Hayek), I show how this notion is paradoxically excluded from the economic field. In economics, uncertainty is totally reduced by an a priori Gaussian framework-in contrast to econophysics, which does not use a priori models because it works directly on data. Uncertainty is then not shaped by a specific model, and is partially and temporally reduced as models improve. This way of thinking about uncertainty has echoes in the economic literature. By presenting econophysics as a Knightian method, and a complementary approach to a Hayekian framework, this paper shows that econophysics can be methodologically justified from an economic point of view.

  13. Physical Uncertainty Bounds (PUB)

    SciTech Connect

    Vaughan, Diane Elizabeth; Preston, Dean L.

    2015-03-19

    This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.

  14. Optimal uncertainty quantification with model uncertainty and legacy data

    NASA Astrophysics Data System (ADS)

    Kamga, P.-H. T.; Li, B.; McKerns, M.; Nguyen, L. H.; Ortiz, M.; Owhadi, H.; Sullivan, T. J.

    2014-12-01

    We present an optimal uncertainty quantification (OUQ) protocol for systems that are characterized by an existing physics-based model and for which only legacy data is available, i.e., no additional experimental testing of the system is possible. Specifically, the OUQ strategy developed in this work consists of using the legacy data to establish, in a probabilistic sense, the level of error of the model, or modeling error, and to subsequently use the validated model as a basis for the determination of probabilities of outcomes. The quantification of modeling uncertainty specifically establishes, to a specified confidence, the probability that the actual response of the system lies within a certain distance of the model. Once the extent of model uncertainty has been established in this manner, the model can be conveniently used to stand in for the actual or empirical response of the system in order to compute probabilities of outcomes. To this end, we resort to the OUQ reduction theorem of Owhadi et al. (2013) in order to reduce the computation of optimal upper and lower bounds on probabilities of outcomes to a finite-dimensional optimization problem. We illustrate the resulting UQ protocol by means of an application concerned with the response to hypervelocity impact of 6061-T6 Aluminum plates by Nylon 6/6 impactors at impact velocities in the range of 5-7 km/s. The ability of the legacy OUQ protocol to process diverse information on the system and its ability to supply rigorous bounds on system performance under realistic-and less than ideal-scenarios demonstrated by the hypervelocity impact application is remarkable.

  15. Quantifying radar-rainfall uncertainties in urban drainage flow modelling

    NASA Astrophysics Data System (ADS)

    Rico-Ramirez, M. A.; Liguori, S.; Schellart, A. N. A.

    2015-09-01

    This work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.

  16. Uncertainty Representation in Stochastic Reservoir Optimization

    NASA Astrophysics Data System (ADS)

    Lamontagne, J. R.; Stedinger, J. R.; Shoemaker, C. A.; Tan, S. N.

    2014-12-01

    Water resources managers attempt to operate reservoir and hydropower systems to maximize system objectives, subject to a host of physical and policy constraints, and in light of uncertainty about future conditions. Optimization models are widely used to advise the decision making process. An important aspect of such models is how uncertainties related to future hydrologic and economic conditions are represented, and the extent to which different uncertainty representations affect the quality of recommended decisions. This study explores the consequences of different uncertainty representations in stochastic optimization models of hydropower systems by comparing simulated system performance using different stochastic optimization models. An important question is whether the added computational burden from greater uncertainty resolution (which can be prohibitive for operational models in many cases) actually improves model recommendations. This is particularly relevant as more complex, ensemble forecasts are incorporated into short- and mid-term planning models. Another important consideration is how watershed hydrology (both seasonal and episodic characteristics), system size, economic context, and the temporal resolution of the model influence how uncertainty should be represented. These topics are explored through several US examples including a sampling stochastic dynamic programming (SSDP) model of a small single-reservoir system on the Kennebec River in Maine, and a stochastic programming model of the large multi-reservoir Federal Columbia River system in the Pacific Northwest. These studies highlight the importance of flexible model frameworks which allow exploration of different representations of a system and of uncertainties before locking operational decision support system development into a specific representation.

  17. Utilizing general information theories for uncertainty quantification

    SciTech Connect

    Booker, J. M.

    2002-01-01

    Uncertainties enter into a complex problem from many sources: variability, errors, and lack of knowledge. A fundamental question arises in how to characterize the various kinds of uncertainty and then combine within a problem such as the verification and validation of a structural dynamics computer model, reliability of a dynamic system, or a complex decision problem. Because uncertainties are of different types (e.g., random noise, numerical error, vagueness of classification), it is difficult to quantify all of them within the constructs of a single mathematical theory, such as probability theory. Because different kinds of uncertainty occur within a complex modeling problem, linkages between these mathematical theories are necessary. A brief overview of some of these theories and their constituents under the label of Generalized lnforrnation Theory (GIT) is presented, and a brief decision example illustrates the importance of linking at least two such theories.

  18. Approximate Techniques for Representing Nuclear Data Uncertainties

    SciTech Connect

    Williams, Mark L; Broadhead, Bryan L; Dunn, Michael E; Rearden, Bradley T

    2007-01-01

    Computational tools are available to utilize sensitivity and uncertainty (S/U) methods for a wide variety of applications in reactor analysis and criticality safety. S/U analysis generally requires knowledge of the underlying uncertainties in evaluated nuclear data, as expressed by covariance matrices; however, only a few nuclides currently have covariance information available in ENDF/B-VII. Recently new covariance evaluations have become available for several important nuclides, but a complete set of uncertainties for all materials needed in nuclear applications is unlikely to be available for several years at least. Therefore if the potential power of S/U techniques is to be realized for near-term projects in advanced reactor design and criticality safety analysis, it is necessary to establish procedures for generating approximate covariance data. This paper discusses an approach to create applications-oriented covariance data by applying integral uncertainties to differential data within the corresponding energy range.

  19. Predictive uncertainty in auditory sequence processing

    PubMed Central

    Hansen, Niels Chr.; Pearce, Marcus T.

    2014-01-01

    Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty—a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music. PMID:25295018

  20. Evaluating prediction uncertainty

    SciTech Connect

    McKay, M.D.

    1995-03-01

    The probability distribution of a model prediction is presented as a proper basis for evaluating the uncertainty in a model prediction that arises from uncertainty in input values. Determination of important model inputs and subsets of inputs is made through comparison of the prediction distribution with conditional prediction probability distributions. Replicated Latin hypercube sampling and variance ratios are used in estimation of the distributions and in construction of importance indicators. The assumption of a linear relation between model output and inputs is not necessary for the indicators to be effective. A sequential methodology which includes an independent validation step is applied in two analysis applications to select subsets of input variables which are the dominant causes of uncertainty in the model predictions. Comparison with results from methods which assume linearity shows how those methods may fail. Finally, suggestions for treating structural uncertainty for submodels are presented.

  1. Communicating scientific uncertainty

    PubMed Central

    Fischhoff, Baruch; Davis, Alex L.

    2014-01-01

    All science has uncertainty. Unless that uncertainty is communicated effectively, decision makers may put too much or too little faith in it. The information that needs to be communicated depends on the decisions that people face. Are they (i) looking for a signal (e.g., whether to evacuate before a hurricane), (ii) choosing among fixed options (e.g., which medical treatment is best), or (iii) learning to create options (e.g., how to regulate nanotechnology)? We examine these three classes of decisions in terms of how to characterize, assess, and convey the uncertainties relevant to each. We then offer a protocol for summarizing the many possible sources of uncertainty in standard terms, designed to impose a minimal burden on scientists, while gradually educating those whose decisions depend on their work. Its goals are better decisions, better science, and better support for science. PMID:25225390

  2. A surrogate-based uncertainty quantification with quantifiable errors

    SciTech Connect

    Bang, Y.; Abdel-Khalik, H. S.

    2012-07-01

    Surrogate models are often employed to reduce the computational cost required to complete uncertainty quantification, where one is interested in propagating input parameters uncertainties throughout a complex engineering model to estimate responses uncertainties. An improved surrogate construction approach is introduced here which places a premium on reducing the associated computational cost. Unlike existing methods where the surrogate is constructed first, then employed to propagate uncertainties, the new approach combines both sensitivity and uncertainty information to render further reduction in the computational cost. Mathematically, the reduction is described by a range finding algorithm that identifies a subspace in the parameters space, whereby parameters uncertainties orthogonal to the subspace contribute negligible amount to the propagated uncertainties. Moreover, the error resulting from the reduction can be upper-bounded. The new approach is demonstrated using a realistic nuclear assembly model and compared to existing methods in terms of computational cost and accuracy of uncertainties. Although we believe the algorithm is general, it will be applied here for linear-based surrogates and Gaussian parameters uncertainties. The generalization to nonlinear models will be detailed in a separate article. (authors)

  3. Uncertainty relations and precession of perihelion

    NASA Astrophysics Data System (ADS)

    Scardigli, Fabio; Casadio, Roberto

    2016-03-01

    We compute the corrections to the Schwarzschild metric necessary to reproduce the Hawking temperature derived from a Generalized Uncertainty Principle (GUP), so that the GUP deformation parameter is directly linked to the deformation of the metric. Using this modified Schwarzschild metric, we compute corrections to the standard General Relativistic predictions for the perihelion precession for planets in the solar system, and for binary pulsars. This analysis allows us to set bounds for the GUP deformation parameter from well-known astronomical measurements.

  4. 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.

  5. MOUSE - A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM

    EPA Science Inventory

    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...

  6. Classification images with uncertainty

    PubMed Central

    Tjan, Bosco S.; Nandy, Anirvan S.

    2009-01-01

    Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a “haze” that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms. PMID:16889477

  7. Remaining Useful Life Estimation in Prognosis: An Uncertainty Propagation Problem

    NASA Technical Reports Server (NTRS)

    Sankararaman, Shankar; Goebel, Kai

    2013-01-01

    The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculations, and in turn, render the remaining useful life prediction uncertain. It is challenging to identify and characterize the various sources of uncertainty in prognosis, understand how each of these sources of uncertainty affect the uncertainty in the remaining useful life prediction, and thereby compute the overall uncertainty in the remaining useful life prediction. In order to achieve these goals, this paper proposes that the task of estimating the remaining useful life must be approached as an uncertainty propagation problem. In this context, uncertainty propagation methods which are available in the literature are reviewed, and their applicability to prognostics and health monitoring are discussed.

  8. Network planning under uncertainties

    NASA Astrophysics Data System (ADS)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2008-11-01

    One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a

  9. Interpreting uncertainty terms.

    PubMed

    Holtgraves, Thomas

    2014-08-01

    Uncertainty terms (e.g., some, possible, good, etc.) are words that do not have a fixed referent and hence are relatively ambiguous. A model is proposed that specifies how, from the hearer's perspective, recognition of facework as a potential motive for the use of an uncertainty term results in a calibration of the intended meaning of that term. Four experiments are reported that examine the impact of face threat, and the variables that affect it (e.g., power), on the manner in which a variety of uncertainty terms (probability terms, quantifiers, frequency terms, etc.) are interpreted. Overall, the results demonstrate that increased face threat in a situation will result in a more negative interpretation of an utterance containing an uncertainty term. That the interpretation of so many different types of uncertainty terms is affected in the same way suggests the operation of a fundamental principle of language use, one with important implications for the communication of risk, subjective experience, and so on. PMID:25090127

  10. Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

    EPA Science Inventory

    Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

  11. Measurement uncertainty relations

    SciTech Connect

    Busch, Paul; Lahti, Pekka; Werner, Reinhard F.

    2014-04-15

    Measurement uncertainty relations are quantitative bounds on the errors in an approximate joint measurement of two observables. They can be seen as a generalization of the error/disturbance tradeoff first discussed heuristically by Heisenberg. Here we prove such relations for the case of two canonically conjugate observables like position and momentum, and establish a close connection with the more familiar preparation uncertainty relations constraining the sharpness of the distributions of the two observables in the same state. Both sets of relations are generalized to means of order α rather than the usual quadratic means, and we show that the optimal constants are the same for preparation and for measurement uncertainty. The constants are determined numerically and compared with some bounds in the literature. In both cases, the near-saturation of the inequalities entails that the state (resp. observable) is uniformly close to a minimizing one.

  12. Measurement uncertainty relations

    NASA Astrophysics Data System (ADS)

    Busch, Paul; Lahti, Pekka; Werner, Reinhard F.

    2014-04-01

    Measurement uncertainty relations are quantitative bounds on the errors in an approximate joint measurement of two observables. They can be seen as a generalization of the error/disturbance tradeoff first discussed heuristically by Heisenberg. Here we prove such relations for the case of two canonically conjugate observables like position and momentum, and establish a close connection with the more familiar preparation uncertainty relations constraining the sharpness of the distributions of the two observables in the same state. Both sets of relations are generalized to means of order α rather than the usual quadratic means, and we show that the optimal constants are the same for preparation and for measurement uncertainty. The constants are determined numerically and compared with some bounds in the literature. In both cases, the near-saturation of the inequalities entails that the state (resp. observable) is uniformly close to a minimizing one.

  13. Techniques to quantify the sensitivity of deterministic model uncertainties

    SciTech Connect

    Ishigami, T. ); Cazzoli, E. . Nuclear Energy Dept.); Khatib-Rahbar ); Unwin, S.D. )

    1989-04-01

    Several existing methods for the assessment of the sensitivity of output uncertainty distributions generated by deterministic computer models to the uncertainty distributions assigned to the input parameters are reviewed and new techniques are proposed. Merits and limitations of the various techniques are examined by detailed application to the suppression pool aerosol removal code (SPARC).

  14. Serenity in political uncertainty.

    PubMed

    Doumit, Rita; Afifi, Rema A; Devon, Holli A

    2015-01-01

    College students are often faced with academic and personal stressors that threaten their well-being. Added to that may be political and environmental stressors such as acts of violence on the streets, interruptions in schooling, car bombings, targeted religious intimidations, financial hardship, and uncertainty of obtaining a job after graduation. Research on how college students adapt to the latter stressors is limited. The aims of this study were (1) to investigate the associations between stress, uncertainty, resilience, social support, withdrawal coping, and well-being for Lebanese youth during their first year of college and (2) to determine whether these variables predicted well-being. A sample of 293 first-year students enrolled in a private university in Lebanon completed a self-reported questionnaire in the classroom setting. The mean age of sample participants was 18.1 years, with nearly an equal percentage of males and females (53.2% vs 46.8%), who lived with their family (92.5%), and whose family reported high income levels (68.4%). Multiple regression analyses revealed that best determinants of well-being are resilience, uncertainty, social support, and gender that accounted for 54.1% of the variance. Despite living in an environment of frequent violence and political uncertainty, Lebanese youth in this study have a strong sense of well-being and are able to go on with their lives. This research adds to our understanding on how adolescents can adapt to stressors of frequent violence and political uncertainty. Further research is recommended to understand the mechanisms through which young people cope with political uncertainty and violence. PMID:25658930

  15. Weighted Uncertainty Relations

    NASA Astrophysics Data System (ADS)

    Xiao, Yunlong; Jing, Naihuan; Li-Jost, Xianqing; Fei, Shao-Ming

    2016-03-01

    Recently, Maccone and Pati have given two stronger uncertainty relations based on the sum of variances and one of them is nontrivial when the quantum state is not an eigenstate of the sum of the observables. We derive a family of weighted uncertainty relations to provide an optimal lower bound for all situations and remove the restriction on the quantum state. Generalization to multi-observable cases is also given and an optimal lower bound for the weighted sum of the variances is obtained in general quantum situation.

  16. Weighted Uncertainty Relations

    PubMed Central

    Xiao, Yunlong; Jing, Naihuan; Li-Jost, Xianqing; Fei, Shao-Ming

    2016-01-01

    Recently, Maccone and Pati have given two stronger uncertainty relations based on the sum of variances and one of them is nontrivial when the quantum state is not an eigenstate of the sum of the observables. We derive a family of weighted uncertainty relations to provide an optimal lower bound for all situations and remove the restriction on the quantum state. Generalization to multi-observable cases is also given and an optimal lower bound for the weighted sum of the variances is obtained in general quantum situation. PMID:26984295

  17. The legacy of uncertainty

    NASA Technical Reports Server (NTRS)

    Brown, Laurie M.

    1993-01-01

    An historical account is given of the circumstances whereby the uncertainty relations were introduced into physics by Heisenberg. The criticisms of QED on measurement-theoretical grounds by Landau and Peierls are then discussed, as well as the response to them by Bohr and Rosenfeld. Finally, some examples are given of how the new freedom to advance radical proposals, in part the result of the revolution brought about by 'uncertainty,' was implemented in dealing with the new phenomena encountered in elementary particle physics in the 1930's.

  18. A Certain Uncertainty

    NASA Astrophysics Data System (ADS)

    Silverman, Mark P.

    2014-07-01

    1. Tools of the trade; 2. The 'fundamental problem' of a practical physicist; 3. Mother of all randomness I: the random disintegration of matter; 4. Mother of all randomness II: the random creation of light; 5. A certain uncertainty; 6. Doing the numbers: nuclear physics and the stock market; 7. On target: uncertainties of projectile flight; 8. The guesses of groups; 9. The random flow of energy I: power to the people; 10. The random flow of energy II: warning from the weather underground; Index.

  19. Uncertainty in NIST Force Measurements

    PubMed Central

    Bartel, Tom

    2005-01-01

    This paper focuses upon the uncertainty of force calibration measurements at the National Institute of Standards and Technology (NIST). The uncertainty of the realization of force for the national deadweight force standards at NIST is discussed, as well as the uncertainties associated with NIST’s voltage-ratio measuring instruments and with the characteristics of transducers being calibrated. The combined uncertainty is related to the uncertainty of dissemination for force transfer standards sent to NIST for calibration. PMID:27308181

  20. Coping with Uncertainty.

    ERIC Educational Resources Information Center

    Wargo, John

    1985-01-01

    Draws conclusions on the scientific uncertainty surrounding most chemical use regulatory decisions, examining the evolution of law and science, benefit analysis, and improving information. Suggests: (1) rapid development of knowledge of chemical risks and (2) a regulatory system which is flexible to new scientific knowledge. (DH)

  1. Uncertainty and nonseparability

    NASA Astrophysics Data System (ADS)

    de La Torre, A. C.; Catuogno, P.; Ferrando, S.

    1989-06-01

    A quantum covariance function is introduced whose real and imaginary parts are related to the independent contributions to the uncertainty principle: noncommutativity of the operators and nonseparability. It is shown that factorizability of states is a sufficient but not necessary condition for separability. It is suggested that all quantum effects could be considered to be a consequence of nonseparability alone.

  2. Asymptotic entropic uncertainty relations

    NASA Astrophysics Data System (ADS)

    Adamczak, Radosław; Latała, Rafał; Puchała, Zbigniew; Życzkowski, Karol

    2016-03-01

    We analyze entropic uncertainty relations for two orthogonal measurements on a N-dimensional Hilbert space, performed in two generic bases. It is assumed that the unitary matrix U relating both bases is distributed according to the Haar measure on the unitary group. We provide lower bounds on the average Shannon entropy of probability distributions related to both measurements. The bounds are stronger than those obtained with use of the entropic uncertainty relation by Maassen and Uffink, and they are optimal up to additive constants. We also analyze the case of a large number of measurements and obtain strong entropic uncertainty relations, which hold with high probability with respect to the random choice of bases. The lower bounds we obtain are optimal up to additive constants and allow us to prove a conjecture by Wehner and Winter on the asymptotic behavior of constants in entropic uncertainty relations as the dimension tends to infinity. As a tool we develop estimates on the maximum operator norm of a submatrix of a fixed size of a random unitary matrix distributed according to the Haar measure, which are of independent interest.

  3. Uncertainties in repository modeling

    SciTech Connect

    Wilson, J.R.

    1996-12-31

    The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling.

  4. An uncertainty inventory demonstration - a primary step in uncertainty quantification

    SciTech Connect

    Langenbrunner, James R.; Booker, Jane M; Hemez, Francois M; Salazar, Issac F; Ross, Timothy J

    2009-01-01

    Tools, methods, and theories for assessing and quantifying uncertainties vary by application. Uncertainty quantification tasks have unique desiderata and circumstances. To realistically assess uncertainty requires the engineer/scientist to specify mathematical models, the physical phenomena of interest, and the theory or framework for assessments. For example, Probabilistic Risk Assessment (PRA) specifically identifies uncertainties using probability theory, and therefore, PRA's lack formal procedures for quantifying uncertainties that are not probabilistic. The Phenomena Identification and Ranking Technique (PIRT) proceeds by ranking phenomena using scoring criteria that results in linguistic descriptors, such as importance ranked with words, 'High/Medium/Low.' The use of words allows PIRT to be flexible, but the analysis may then be difficult to combine with other uncertainty theories. We propose that a necessary step for the development of a procedure or protocol for uncertainty quantification (UQ) is the application of an Uncertainty Inventory. An Uncertainty Inventory should be considered and performed in the earliest stages of UQ.

  5. Strategy under uncertainty.

    PubMed

    Courtney, H; Kirkland, J; Viguerie, P

    1997-01-01

    At the heart of the traditional approach to strategy lies the assumption that by applying a set of powerful analytic tools, executives can predict the future of any business accurately enough to allow them to choose a clear strategic direction. But what happens when the environment is so uncertain that no amount of analysis will allow us to predict the future? What makes for a good strategy in highly uncertain business environments? The authors, consultants at McKinsey & Company, argue that uncertainty requires a new way of thinking about strategy. All too often, they say, executives take a binary view: either they underestimate uncertainty to come up with the forecasts required by their companies' planning or capital-budging processes, or they overestimate it, abandon all analysis, and go with their gut instinct. The authors outline a new approach that begins by making a crucial distinction among four discrete levels of uncertainty that any company might face. They then explain how a set of generic strategies--shaping the market, adapting to it, or reserving the right to play at a later time--can be used in each of the four levels. And they illustrate how these strategies can be implemented through a combination of three basic types of actions: big bets, options, and no-regrets moves. The framework can help managers determine which analytic tools can inform decision making under uncertainty--and which cannot. At a broader level, it offers executives a discipline for thinking rigorously and systematically about uncertainty and its implications for strategy. PMID:10174798

  6. Comment on "A procedure for the estimation of the numerical uncertainty of CFD calculations based on grid refinement studies" (L. Eça and M. Hoekstra, Journal of Computational Physics 262 (2014) 104-130)

    NASA Astrophysics Data System (ADS)

    Xing, Tao; Stern, Frederick

    2015-11-01

    Eça and Hoekstra [1] proposed a procedure for the estimation of the numerical uncertainty of CFD calculations based on the least squares root (LSR) method. We believe that the LSR method has potential value for providing an extended Richardson-extrapolation solution verification procedure for mixed monotonic and oscillatory or only oscillatory convergent solutions (based on the usual systematic grid-triplet convergence condition R). Current Richardson-extrapolation solution verification procedures [2-7] are restricted to monotonic convergent solutions 0 < R < 1. Procedures for oscillatory convergence simply either use uncertainty estimate based on average maximum minus minimum solutions [8,9] or arbitrarily large factors of safety (FS) [2]. However, in our opinion several issues preclude the usefulness of the presented LSR method: five criticisms follow. The solution verification literature needs technical discussion in order to put the LSR method in context. The LSR method has many options making it very difficult to follow. Fig. 1 provides a block diagram, which summarizes the LSR procedure and options, including some of which we are in disagreement. Compared to the grid-triplet and three-step procedure followed by most solution verification methods (convergence condition followed by error and uncertainty estimates), the LSR method follows a four-grid (minimum) and four-step procedure (error estimate, data range parameter Δϕ, FS, and uncertainty estimate).

  7. Multi-scenario modelling of uncertainty in stochastic chemical systems

    SciTech Connect

    Evans, R. David; Ricardez-Sandoval, Luis A.

    2014-09-15

    Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo.

  8. Temporal uncertainty of geographical information

    NASA Astrophysics Data System (ADS)

    Shu, Hong; Qi, Cuihong

    2005-10-01

    Temporal uncertainty is a crossing point of temporal and error-aware geographical information systems. In Geoinformatics, temporal uncertainty is of the same importance as spatial and thematic uncertainty of geographical information. However, until very recently, the standard organizations of ISO/TC211 and FGDC subsequently claimed that temporal uncertainty is one of geospatial data quality elements. Over the past decades, temporal uncertainty of geographical information is modeled insufficiently. To lay down a foundation of logically or physically modeling temporal uncertainty, this paper is aimed to clarify the semantics of temporal uncertainty to some extent. The general uncertainty is conceptualized with a taxonomy of uncertainty. Semantically, temporal uncertainty is progressively classified into uncertainty of time coordinates, changes, and dynamics. Uncertainty of multidimensional time (valid time, database time, and conceptual time, etc.) has been emphasized. It is realized that time scale (granularity) transition may lead to temporal uncertainty because of missing transition details. It is dialectically concluded that temporal uncertainty is caused by the complexity of the human-machine-earth system.

  9. 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.

  10. 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.

  11. 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.

  12. Estimating uncertainty of inference for validation

    SciTech Connect

    Booker, Jane M; Langenbrunner, James R; Hemez, Francois M; Ross, Timothy J

    2010-09-30

    We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

  13. Asymmetric Uncertainty Expression for High Gradient Aerodynamics

    NASA Technical Reports Server (NTRS)

    Pinier, Jeremy T

    2012-01-01

    When the physics of the flow around an aircraft changes very abruptly either in time or space (e.g., flow separation/reattachment, boundary layer transition, unsteadiness, shocks, etc), the measurements that are performed in a simulated environment like a wind tunnel test or a computational simulation will most likely incorrectly predict the exact location of where (or when) the change in physics happens. There are many reasons for this, includ- ing the error introduced by simulating a real system at a smaller scale and at non-ideal conditions, or the error due to turbulence models in a computational simulation. The un- certainty analysis principles that have been developed and are being implemented today do not fully account for uncertainty in the knowledge of the location of abrupt physics changes or sharp gradients, leading to a potentially underestimated uncertainty in those areas. To address this problem, a new asymmetric aerodynamic uncertainty expression containing an extra term to account for a phase-uncertainty, the magnitude of which is emphasized in the high-gradient aerodynamic regions is proposed in this paper. Additionally, based on previous work, a method for dispersing aerodynamic data within asymmetric uncer- tainty bounds in a more realistic way has been developed for use within Monte Carlo-type analyses.

  14. Uncertainties in climate stabilization

    SciTech Connect

    Wigley, T. M.; Clarke, Leon E.; Edmonds, James A.; Jacoby, H. D.; Paltsev, S.; Pitcher, Hugh M.; Reilly, J. M.; Richels, Richard G.; Sarofim, M. C.; Smith, Steven J.

    2009-11-01

    We explore the atmospheric composition, temperature and sea level implications of new reference and cost-optimized stabilization emissions scenarios produced using three different Integrated Assessment (IA) models for U.S. Climate Change Science Program (CCSP) Synthesis and Assessment Product 2.1a. We also consider an extension of one of these sets of scenarios out to 2300. Stabilization is defined in terms of radiative forcing targets for the sum of gases potentially controlled under the Kyoto Protocol. For the most stringent stabilization case (“Level 1” with CO2 concentration stabilizing at about 450 ppm), peak CO2 emissions occur close to today, implying a need for immediate CO2 emissions abatement if we wish to stabilize at this level. In the extended reference case, CO2 stabilizes at 1000 ppm in 2200 – but even to achieve this target requires large and rapid CO2 emissions reductions over the 22nd century. Future temperature changes for the Level 1 stabilization case show considerable uncertainty even when a common set of climate model parameters is used (a result of different assumptions for non-Kyoto gases). Uncertainties are about a factor of three when climate sensitivity uncertainties are accounted for. We estimate the probability that warming from pre-industrial times will be less than 2oC to be about 50%. For one of the IA models, warming in the Level 1 case is greater out to 2050 than in the reference case, due to the effect of decreasing SO2 emissions that occur as a side effect of the policy-driven reduction in CO2 emissions. Sea level rise uncertainties for the Level 1 case are very large, with increases ranging from 12 to 100 cm over 2000 to 2300.

  15. Uncertainty quantified trait predictions

    NASA Astrophysics Data System (ADS)

    Fazayeli, Farideh; Kattge, Jens; Banerjee, Arindam; Schrodt, Franziska; Reich, Peter

    2015-04-01

    Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such composite trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait-trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. For this purpose we introduce BHPMF, a hierarchical Bayesian extension of Probabilistic Matrix Factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction. Implemented in the context of a Gibbs Sampler MCMC approach BHPMF provides uncertainty estimates for each trait prediction. We present comprehensive experimental results on the problem of plant trait prediction using the largest database of plant traits, where BHPMF shows strong empirical performance in uncertainty quantified trait prediction, outperforming the state-of-the-art based on point estimates. Further, we show that BHPMF is more accurate when it is confident, whereas the error is high when the uncertainty is high.

  16. 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).

  17. Assessment of Radiative Heating Uncertainty for Hyperbolic Earth Entry

    NASA Technical Reports Server (NTRS)

    Johnston, Christopher O.; Mazaheri, Alireza; Gnoffo, Peter A.; Kleb, W. L.; Sutton, Kenneth; Prabhu, Dinesh K.; Brandis, Aaron M.; Bose, Deepak

    2011-01-01

    This paper investigates the shock-layer radiative heating uncertainty for hyperbolic Earth entry, with the main focus being a Mars return. In Part I of this work, a baseline simulation approach involving the LAURA Navier-Stokes code with coupled ablation and radiation is presented, with the HARA radiation code being used for the radiation predictions. Flight cases representative of peak-heating Mars or asteroid return are de ned and the strong influence of coupled ablation and radiation on their aerothermodynamic environments are shown. Structural uncertainties inherent in the baseline simulations are identified, with turbulence modeling, precursor absorption, grid convergence, and radiation transport uncertainties combining for a +34% and ..24% structural uncertainty on the radiative heating. A parametric uncertainty analysis, which assumes interval uncertainties, is presented. This analysis accounts for uncertainties in the radiation models as well as heat of formation uncertainties in the flow field model. Discussions and references are provided to support the uncertainty range chosen for each parameter. A parametric uncertainty of +47.3% and -28.3% is computed for the stagnation-point radiative heating for the 15 km/s Mars-return case. A breakdown of the largest individual uncertainty contributors is presented, which includes C3 Swings cross-section, photoionization edge shift, and Opacity Project atomic lines. Combining the structural and parametric uncertainty components results in a total uncertainty of +81.3% and ..52.3% for the Mars-return case. In Part II, the computational technique and uncertainty analysis presented in Part I are applied to 1960s era shock-tube and constricted-arc experimental cases. It is shown that experiments contain shock layer temperatures and radiative ux values relevant to the Mars-return cases of present interest. Comparisons between the predictions and measurements, accounting for the uncertainty in both, are made for a range

  18. Using Models that Incorporate Uncertainty

    ERIC Educational Resources Information Center

    Caulkins, Jonathan P.

    2002-01-01

    In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…

  19. Gravitational tests of the generalized uncertainty principle

    NASA Astrophysics Data System (ADS)

    Scardigli, Fabio; Casadio, Roberto

    2015-09-01

    We compute the corrections to the Schwarzschild metric necessary to reproduce the Hawking temperature derived from a generalized uncertainty principle (GUP), so that the GUP deformation parameter is directly linked to the deformation of the metric. Using this modified Schwarzschild metric, we compute corrections to the standard general relativistic predictions for the light deflection and perihelion precession, both for planets in the solar system and for binary pulsars. This analysis allows us to set bounds for the GUP deformation parameter from well-known astronomical measurements.

  20. Nonlinear dynamics and numerical uncertainties in CFD

    NASA Technical Reports Server (NTRS)

    Yee, H. C.; Sweby, P. K.

    1996-01-01

    The application of nonlinear dynamics to improve the understanding of numerical uncertainties in computational fluid dynamics (CFD) is reviewed. Elementary examples in the use of dynamics to explain the nonlinear phenomena and spurious behavior that occur in numerics are given. The role of dynamics in the understanding of long time behavior of numerical integrations and the nonlinear stability, convergence, and reliability of using time-marching, approaches for obtaining steady-state numerical solutions in CFD is explained. The study is complemented with spurious behavior observed in CFD computations.

  1. Dimensionality reduction for uncertainty quantification of nuclear engineering models.

    SciTech Connect

    Roderick, O.; Wang, Z.; Anitescu, M.

    2011-01-01

    The task of uncertainty quantification consists of relating the available information on uncertainties in the model setup to the resulting variation in the outputs of the model. Uncertainty quantification plays an important role in complex simulation models of nuclear engineering, where better understanding of uncertainty results in greater confidence in the model and in the improved safety and efficiency of engineering projects. In our previous work, we have shown that the effect of uncertainty can be approximated by polynomial regression with derivatives (PRD): a hybrid regression method that uses first-order derivatives of the model output as additional fitting conditions for a polynomial expansion. Numerical experiments have demonstrated the advantage of this approach over classical methods of uncertainty analysis: in precision, computational efficiency, or both. To obtain derivatives, we used automatic differentiation (AD) on the simulation code; hand-coded derivatives are acceptable for simpler models. We now present improvements on the method. We use a tuned version of the method of snapshots, a technique based on proper orthogonal decomposition (POD), to set up the reduced order representation of essential information on uncertainty in the model inputs. The automatically obtained sensitivity information is required to set up the method. Dimensionality reduction in combination with PRD allows analysis on a larger dimension of the uncertainty space (>100), at modest computational cost.

  2. Satellite altitude determination uncertainties

    NASA Technical Reports Server (NTRS)

    Siry, J. W.

    1972-01-01

    Satellite altitude determination uncertainties will be discussed from the standpoint of the GEOS-C satellite, from the longer range viewpoint afforded by the Geopause concept. Data are focused on methods for short-arc tracking which are essentially geometric in nature. One uses combinations of lasers and collocated cameras. The other method relies only on lasers, using three or more to obtain the position fix. Two typical locales are looked at, the Caribbean area, and a region associated with tracking sites at Goddard, Bermuda and Canada which encompasses a portion of the Gulf Stream in which meanders develop.

  3. ENHANCED UNCERTAINTY ANALYSIS FOR SRS COMPOSITE ANALYSIS

    SciTech Connect

    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

  4. Strain Gauge Balance Uncertainty Analysis at NASA Langley: A Technical Review

    NASA Technical Reports Server (NTRS)

    Tripp, John S.

    1999-01-01

    This paper describes a method to determine the uncertainties of measured forces and moments from multi-component force balances used in wind tunnel tests. A multivariate regression technique is first employed to estimate the uncertainties of the six balance sensitivities and 156 interaction coefficients derived from established balance calibration procedures. These uncertainties are then employed to calculate the uncertainties of force-moment values computed from observed balance output readings obtained during tests. Confidence and prediction intervals are obtained for each computed force and moment as functions of the actual measurands. Techniques are discussed for separate estimation of balance bias and precision uncertainties.

  5. The maintenance of uncertainty

    NASA Astrophysics Data System (ADS)

    Smith, L. A.

    Introduction Preliminaries State-space dynamics Linearized dynamics of infinitesimal uncertainties Instantaneous infinitesimal dynamics Finite-time evolution of infinitesimal uncertainties Lyapunov exponents and predictability The Baker's apprentice map Infinitesimals and predictability Dimensions The Grassberger-Procaccia algorithm Towards a better estimate from Takens' estimators Space-time-separation diagrams Intrinsic limits to the analysis of geometry Takens' theorem The method of delays Noise Prediction, prophecy, and pontification Introduction Simulations, models and physics Ground rules Data-based models: dynamic reconstructions Analogue prediction Local prediction Global prediction Accountable forecasts of chaotic systems Evaluating ensemble forecasts The annulus Prophecies Aids for more reliable nonlinear analysis Significant results: surrogate data, synthetic data and self-deception Surrogate data and the bootstrap Surrogate predictors: Is my model any good? Hints for the evaluation of new techniques Avoiding simple straw men Feasibility tests for the identification of chaos On detecting "tiny" data sets Building models consistent with the observations Cost functions ι-shadowing: Is my model any good? (reprise) Casting infinitely long shadows (out-of-sample) Distinguishing model error and system sensitivity Forecast error and model sensitivity Accountability Residual predictability Deterministic or stochastic dynamics? Using ensembles to distinguish the expectation from the expected Numerical Weather Prediction Probabilistic prediction with a deterministic model The analysis Constructing and interpreting ensembles The outlook(s) for today Conclusion Summary

  6. 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.

  7. Uncertainty in adaptive capacity

    NASA Astrophysics Data System (ADS)

    Adger, W. Neil; Vincent, Katharine

    2005-03-01

    The capacity to adapt is a critical element of the process of adaptation: it is the vector of resources that represent the asset base from which adaptation actions can be made. Adaptive capacity can in theory be identified and measured at various scales, from the individual to the nation. The assessment of uncertainty within such measures comes from the contested knowledge domain and theories surrounding the nature of the determinants of adaptive capacity and the human action of adaptation. While generic adaptive capacity at the national level, for example, is often postulated as being dependent on health, governance and political rights, and literacy, and economic well-being, the determinants of these variables at national levels are not widely understood. We outline the nature of this uncertainty for the major elements of adaptive capacity and illustrate these issues with the example of a social vulnerability index for countries in Africa. To cite this article: W.N. Adger, K. Vincent, C. R. Geoscience 337 (2005).

  8. Teaching Quantum Uncertainty1

    NASA Astrophysics Data System (ADS)

    Hobson, Art

    2011-10-01

    An earlier paper2 introduces quantum physics by means of four experiments: Youngs double-slit interference experiment using (1) a light beam, (2) a low-intensity light beam with time-lapse photography, (3) an electron beam, and (4) a low-intensity electron beam with time-lapse photography. It's ironic that, although these experiments demonstrate most of the quantum fundamentals, conventional pedagogy stresses their difficult and paradoxical nature. These paradoxes (i.e., logical contradictions) vanish, and understanding becomes simpler, if one takes seriously the fact that quantum mechanics is the nonrelativistic limit of our most accurate physical theory, namely quantum field theory, and treats the Schroedinger wave function, as well as the electromagnetic field, as quantized fields.2 Both the Schroedinger field, or "matter field," and the EM field are made of "quanta"—spatially extended but energetically discrete chunks or bundles of energy. Each quantum comes nonlocally from the entire space-filling field and interacts with macroscopic systems such as the viewing screen by collapsing into an atom instantaneously and randomly in accordance with the probability amplitude specified by the field. Thus, uncertainty and nonlocality are inherent in quantum physics. This paper is about quantum uncertainty. A planned later paper will take up quantum nonlocality.

  9. Probabilistic Mass Growth Uncertainties

    NASA Technical Reports Server (NTRS)

    Plumer, Eric; Elliott, Darren

    2013-01-01

    Mass has been widely used as a variable input parameter for Cost Estimating Relationships (CER) for space systems. As these space systems progress from early concept studies and drawing boards to the launch pad, their masses tend to grow substantially, hence adversely affecting a primary input to most modeling CERs. Modeling and predicting mass uncertainty, based on historical and analogous data, is therefore critical and is an integral part of modeling cost risk. This paper presents the results of a NASA on-going effort to publish mass growth datasheet for adjusting single-point Technical Baseline Estimates (TBE) of masses of space instruments as well as spacecraft, for both earth orbiting and deep space missions at various stages of a project's lifecycle. This paper will also discusses the long term strategy of NASA Headquarters in publishing similar results, using a variety of cost driving metrics, on an annual basis. This paper provides quantitative results that show decreasing mass growth uncertainties as mass estimate maturity increases. This paper's analysis is based on historical data obtained from the NASA Cost Analysis Data Requirements (CADRe) database.

  10. Measurement uncertainty analysis techniques applied to PV performance measurements

    SciTech Connect

    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.

  11. LCA data quality: sensitivity and uncertainty analysis.

    PubMed

    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

  12. 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.

  13. Earthquake Loss Estimation Uncertainties

    NASA Astrophysics Data System (ADS)

    Frolova, Nina; Bonnin, Jean; Larionov, Valery; Ugarov, Aleksander

    2013-04-01

    The paper addresses the reliability issues of strong earthquakes loss assessment following strong earthquakes with worldwide Systems' application in emergency mode. Timely and correct action just after an event can result in significant benefits in saving lives. In this case the information about possible damage and expected number of casualties is very critical for taking decision about search, rescue operations and offering humanitarian assistance. Such rough information may be provided by, first of all, global systems, in emergency mode. The experience of earthquakes disasters in different earthquake-prone countries shows that the officials who are in charge of emergency response at national and international levels are often lacking prompt and reliable information on the disaster scope. Uncertainties on the parameters used in the estimation process are numerous and large: knowledge about physical phenomena and uncertainties on the parameters used to describe them; global adequacy of modeling techniques to the actual physical phenomena; actual distribution of population at risk at the very time of the shaking (with respect to immediate threat: buildings or the like); knowledge about the source of shaking, etc. Needless to be a sharp specialist to understand, for example, that the way a given building responds to a given shaking obeys mechanical laws which are poorly known (if not out of the reach of engineers for a large portion of the building stock); if a carefully engineered modern building is approximately predictable, this is far not the case for older buildings which make up the bulk of inhabited buildings. The way population, inside the buildings at the time of shaking, is affected by the physical damage caused to the buildings is not precisely known, by far. The paper analyzes the influence of uncertainties in strong event parameters determination by Alert Seismological Surveys, of simulation models used at all stages from, estimating shaking intensity

  14. The Neural Representation of Unexpected Uncertainty During Value-Based Decision Making

    PubMed Central

    Payzan-LeNestour, Elise; Dunne, Simon; Bossaerts, Peter; O'Doherty, John P.

    2016-01-01

    Summary Uncertainty is an inherent property of the environment and a central feature of models of decision-making and learning. Theoretical propositions suggest that one form, unexpected uncertainty, may be used to rapidly adapt to changes in the environment, while being influenced by two other forms: risk and estimation uncertainty. While previous studies have reported neural representations of estimation uncertainty and risk, relatively little is known about unexpected uncertainty. Here, participants performed a decision-making task while undergoing functional magnetic resonance imaging (fMRI), which, in combination with a Bayesian model-based analysis, enabled each form of uncertainty to be separately measured. We found representations of unexpected uncertainty in multiple cortical areas, as well as the noradrenergic brainstem nucleus locus coeruleus. Other unique cortical regions were found to encode risk, estimation uncertainty and learning rate. Collectively, these findings support theoretical models in which several formally separable uncertainty computations determine the speed of learning. PMID:23849203

  15. Uncertainty relation in Schwarzschild spacetime

    NASA Astrophysics Data System (ADS)

    Feng, Jun; Zhang, Yao-Zhong; Gould, Mark D.; Fan, Heng

    2015-04-01

    We explore the entropic uncertainty relation in the curved background outside a Schwarzschild black hole, and find that Hawking radiation introduces a nontrivial modification on the uncertainty bound for particular observer, therefore it could be witnessed by proper uncertainty game experimentally. We first investigate an uncertainty game between a free falling observer and his static partner holding a quantum memory initially entangled with the quantum system to be measured. Due to the information loss from Hawking decoherence, we find an inevitable increase of the uncertainty on the outcome of measurements in the view of static observer, which is dependent on the mass of the black hole, the distance of observer from event horizon, and the mode frequency of quantum memory. To illustrate the generality of this paradigm, we relate the entropic uncertainty bound with other uncertainty probe, e.g., time-energy uncertainty. In an alternative game between two static players, we show that quantum information of qubit can be transferred to quantum memory through a bath of fluctuating quantum fields outside the black hole. For a particular choice of initial state, we show that the Hawking decoherence cannot counteract entanglement generation after the dynamical evolution of system, which triggers an effectively reduced uncertainty bound that violates the intrinsic limit -log2 ⁡ c. Numerically estimation for a proper choice of initial state shows that our result is comparable with possible real experiments. Finally, a discussion on the black hole firewall paradox in the context of entropic uncertainty relation is given.

  16. Parameter Uncertainty for Aircraft Aerodynamic Modeling using Recursive Least Squares

    NASA Technical Reports Server (NTRS)

    Grauer, Jared A.; Morelli, Eugene A.

    2016-01-01

    A real-time method was demonstrated for determining accurate uncertainty levels of stability and control derivatives estimated using recursive least squares and time-domain data. The method uses a recursive formulation of the residual autocorrelation to account for colored residuals, which are routinely encountered in aircraft parameter estimation and change the predicted uncertainties. Simulation data and flight test data for a subscale jet transport aircraft were used to demonstrate the approach. Results showed that the corrected uncertainties matched the observed scatter in the parameter estimates, and did so more accurately than conventional uncertainty estimates that assume white residuals. Only small differences were observed between batch estimates and recursive estimates at the end of the maneuver. It was also demonstrated that the autocorrelation could be reduced to a small number of lags to minimize computation and memory storage requirements without significantly degrading the accuracy of predicted uncertainty levels.

  17. Noise-Parameter Uncertainties: A Monte Carlo Simulation

    PubMed Central

    Randa, J.

    2002-01-01

    This paper reports the formulation and results of a Monte Carlo study of uncertainties in noise-parameter measurements. The simulator permits the computation of the dependence of the uncertainty in the noise parameters on uncertainties in the underlying quantities. Results are obtained for the effect due to uncertainties in the reflection coefficients of the input terminations, the noise temperature of the hot noise source, connector variability, the ambient temperature, and the measurement of the output noise. Representative results are presented for both uncorrelated and correlated uncertainties in the underlying quantities. The simulation program is also used to evaluate two possible enhancements of noise-parameter measurements: the use of a cold noise source as one of the input terminations and the inclusion of a measurement of the “reverse configuration,” in which the noise from the amplifier input is measured directly.

  18. On the worst case uncertainty and its evaluation

    NASA Astrophysics Data System (ADS)

    Fabbiano, L.; Giaquinto, N.; Savino, M.; Vacca, G.

    2016-02-01

    The paper is a review on the worst case uncertainty (WCU) concept, neglected in the Guide to the Expression of Uncertainty in Measurements (GUM), but necessary for a correct uncertainty assessment in a number of practical cases involving distribution with compact support. First, it is highlighted that the knowledge of the WCU is necessary to choose a sensible coverage factor, associated to a sensible coverage probability: the Maximum Acceptable Coverage Factor (MACF) is introduced as a convenient index to guide this choice. Second, propagation rules for the worst-case uncertainty are provided in matrix and scalar form. It is highlighted that when WCU propagation cannot be computed, the Monte Carlo approach is the only way to obtain a correct expanded uncertainty assessment, in contrast to what can be inferred from the GUM. Third, examples of applications of the formulae to ordinary instruments and measurements are given. Also an example taken from the GUM is discussed, underlining some inconsistencies in it.

  19. Parameterization of Model Validating Sets for Uncertainty Bound Optimizations. Revised

    NASA Technical Reports Server (NTRS)

    Lim, K. B.; Giesy, D. P.

    2000-01-01

    Given measurement data, a nominal model and a linear fractional transformation uncertainty structure with an allowance on unknown but bounded exogenous disturbances, easily computable tests for the existence of a model validating uncertainty set are given. Under mild conditions, these tests are necessary and sufficient for the case of complex, nonrepeated, block-diagonal structure. For the more general case which includes repeated and/or real scalar uncertainties, the tests are only necessary but become sufficient if a collinearity condition is also satisfied. With the satisfaction of these tests, it is shown that a parameterization of all model validating sets of plant models is possible. The new parameterization is used as a basis for a systematic way to construct or perform uncertainty tradeoff with model validating uncertainty sets which have specific linear fractional transformation structure for use in robust control design and analysis. An illustrative example which includes a comparison of candidate model validating sets is given.

  20. Satellite altitude determination uncertainties

    NASA Technical Reports Server (NTRS)

    Siry, J. W.

    1971-01-01

    Satellite altitude determination uncertainties are discussed from the standpoint of the GEOS-C satellite. GEOS-C will be tracked by a number of the conventional satellite tracking systems, as well as by two advanced systems; a satellite-to-satellite tracking system and lasers capable of decimeter accuracies which are being developed in connection with the Goddard Earth and Ocean Dynamics Applications program. The discussion is organized in terms of a specific type of GEOS-C orbit which would satisfy a number of scientific objectives including the study of the gravitational field by means of both the altimeter and the satellite-to-satellite tracking system, studies of tides, and the Gulf Stream meanders.

  1. Uncertainty as Certaint

    NASA Astrophysics Data System (ADS)

    Petzinger, Tom

    I am trying to make money in the biotech industry from complexity science. And I am doing it with inspiration that I picked up on the edge of Appalachia spending time with June Holley and ACEnet when I was a Wall Street Journal reporter. I took some of those ideas to Pittsburgh, in biotechnology, in a completely private setting with an economic development focus, but also with a mission t o return profit to private capital. And we are doing that. I submit as a hypothesis, something we are figuring out in the post- industrial era, that business evolves. It is not the definition of business, but business critically involves the design of systems in which uncertainty is treated as a certainty. That is what I have seen and what I have tried to put into practice.

  2. Living with uncertainty

    SciTech Connect

    Rau, N.; Fong, C.C.; Grigg, C.H.; Silverstein, B.

    1994-11-01

    In the electric utility industry, only one thing can be guaranteed with absolute certainty: one lives and works with many unknowns. Thus, the industry has embraced probability methods to varying degrees over the last 25 years. These techniques aid decision makers in planning, operations, and maintenance by quantifying uncertainty. Examples include power system reliability, production costing simulation, and assessment of environmental factors. A series of brainstorming sessions was conducted by the Application of Probability Methods (APM) Subcommittee of the IEEE Power Engineering Society to identify research and development needs and to ask the question, ''where should we go from here '' The subcommittee examined areas of need in data development, applications, and methods for decision making. The purpose of this article is to share the thoughts of APM members with a broader audience to the findings and to invite comments and participation.

  3. Analysis and reduction of chemical models under uncertainty.

    SciTech Connect

    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.

  4. Comprehensive Approach to Verification and Validation of CFD Simulations Applied to Backward Facing Step-Application of CFD Uncertainty Analysis

    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.

  5. 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

  6. A robust fuzzy logic controller for robot manipulators with uncertainties.

    PubMed

    Yi, S Y; Chung, M J

    1997-01-01

    Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator. PMID:18255910

  7. Thermodynamics of Black Holes and the Symmetric Generalized Uncertainty Principle

    NASA Astrophysics Data System (ADS)

    Dutta, Abhijit; Gangopadhyay, Sunandan

    2016-06-01

    In this paper, we have investigated the thermodynamics of Schwarzschild and Reissner-Nordström black holes using the symmetric generalised uncertainty principle which contains correction terms involving momentum and position uncertainty. The mass-temperature relationship and the heat capacity for these black holes have been computed using which the critical and remnant masses have been obtained. The entropy is found to satisfy the area law upto leading order logarithmic corrections and corrections of the form A 2 (which is a new finding in this paper) from the symmetric generalised uncertainty principle.

  8. Uncertainty-like relations of the relative entropy of coherence

    NASA Astrophysics Data System (ADS)

    Liu, Feng; Li, Fei; Chen, Jun; Xing, Wei

    2016-08-01

    Quantum coherence is an important physical resource in quantum computation and quantum information processing. In this paper, we firstly obtain an uncertainty-like expression relating two coherences contained in corresponding local bipartite quantum system. This uncertainty-like inequality shows that the larger the coherence of one subsystem, the less coherence contained in other subsystems. Further, we discuss in detail the uncertainty-like relation among three single-partite quantum systems. We show that the coherence contained in pure tripartite quantum system is greater than the sum of the coherence of all local subsystems.

  9. Impact of discharge data uncertainty on nutrient load uncertainty

    NASA Astrophysics Data System (ADS)

    Westerberg, Ida; Gustavsson, Hanna; Sonesten, Lars

    2016-04-01

    Uncertainty in the rating-curve model of the stage-discharge relationship leads to uncertainty in discharge time series. These uncertainties in turn affect many other analyses based on discharge data, such as nutrient load estimations. It is important to understand how large the impact of discharge data uncertainty is on such analyses, since they are often used as the basis to take important environmental management decisions. In the Baltic Sea basin, nutrient load estimates from river mouths are a central information basis for managing and reducing eutrophication in the Baltic Sea. In this study we investigated rating curve uncertainty and its propagation to discharge data uncertainty and thereafter to uncertainty in the load of phosphorous and nitrogen for twelve Swedish river mouths. We estimated rating curve uncertainty using the Voting Point method, which accounts for random and epistemic errors in the stage-discharge relation and allows drawing multiple rating-curve realisations consistent with the total uncertainty. We sampled 40,000 rating curves, and for each sampled curve we calculated a discharge time series from 15-minute water level data for the period 2005-2014. Each discharge time series was then aggregated to daily scale and used to calculate the load of phosphorous and nitrogen from linearly interpolated monthly water samples, following the currently used methodology for load estimation. Finally the yearly load estimates were calculated and we thus obtained distributions with 40,000 load realisations per year - one for each rating curve. We analysed how the rating curve uncertainty propagated to the discharge time series at different temporal resolutions, and its impact on the yearly load estimates. Two shorter periods of daily water quality sampling around the spring flood peak allowed a comparison of load uncertainty magnitudes resulting from discharge data with those resulting from the monthly water quality sampling.

  10. Uncertainty in Partner Selection for Virtual Enterprises

    NASA Astrophysics Data System (ADS)

    Crispim, José; de Sousa, Jorge Pinho

    A virtual enterprise (VE) is a temporary organization that pools the core competencies of its member enterprises and exploits fast changing market opportunities. The success of such an organization is strongly dependent on its composition, and the selection of partners becomes therefore a crucial issue. This problem is particularly difficult because of the uncertainties related to information, market dynamics, customer expectations and technology speed up. In this paper we propose an integrated approach to rank alternative VE configurations in business environments with uncertainty, using an extension of the TOPSIS method for fuzzy data, improved through the use of a stochastic multiobjective tabu search meta-heuristic. Preliminary computational results clearly demonstrate the potential of this approach for practical application.

  11. Uncertainty quantification for porous media flows

    NASA Astrophysics Data System (ADS)

    Christie, Mike; Demyanov, Vasily; Erbas, Demet

    2006-09-01

    Uncertainty quantification is an increasingly important aspect of many areas of computational science, where the challenge is to make reliable predictions about the performance of complex physical systems in the absence of complete or reliable data. Predicting flows of oil and water through oil reservoirs is an example of a complex system where accuracy in prediction is needed primarily for financial reasons. Simulation of fluid flow in oil reservoirs is usually carried out using large commercially written finite difference simulators solving conservation equations describing the multi-phase flow through the porous reservoir rocks. This paper examines a Bayesian Framework for uncertainty quantification in porous media flows that uses a stochastic sampling algorithm to generate models that match observed data. Machine learning algorithms are used to speed up the identification of regions in parameter space where good matches to observed data can be found.

  12. Dealing with Uncertainties in Initial Orbit Determination

    NASA Technical Reports Server (NTRS)

    Armellin, Roberto; Di Lizia, Pierluigi; Zanetti, Renato

    2015-01-01

    A method to deal with uncertainties in initial orbit determination (IOD) is presented. This is based on the use of Taylor differential algebra (DA) to nonlinearly map the observation uncertainties from the observation space to the state space. When a minimum set of observations is available DA is used to expand the solution of the IOD problem in Taylor series with respect to measurement errors. When more observations are available high order inversion tools are exploited to obtain full state pseudo-observations at a common epoch. The mean and covariance of these pseudo-observations are nonlinearly computed by evaluating the expectation of high order Taylor polynomials. Finally, a linear scheme is employed to update the current knowledge of the orbit. Angles-only observations are considered and simplified Keplerian dynamics adopted to ease the explanation. Three test cases of orbit determination of artificial satellites in different orbital regimes are presented to discuss the feature and performances of the proposed methodology.

  13. Probabilistic simulation of uncertainties in thermal structures

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Shiao, Michael

    1990-01-01

    Development of probabilistic structural analysis methods for hot structures is a major activity at NASA-Lewis, and consists of five program elements: (1) probabilistic loads, (2) probabilistic finite element analysis, (3) probabilistic material behavior, (4) assessment of reliability and risk, and (5) probabilistic structural performance evaluation. Attention is given to quantification of the effects of uncertainties for several variables on High Pressure Fuel Turbopump blade temperature, pressure, and torque of the Space Shuttle Main Engine; the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; evaluation of the failure probability; reliability and risk-cost assessment; and an outline of an emerging approach for eventual hot structures certification. Collectively, the results demonstrate that the structural durability/reliability of hot structural components can be effectively evaluated in a formal probabilistic framework. In addition, the approach can be readily extended to computationally simulate certification of hot structures for aerospace environments.

  14. Uncertainty and Anticipation in Anxiety

    PubMed Central

    Grupe, Dan W.; Nitschke, Jack B.

    2014-01-01

    Uncertainty about a possible future threat disrupts our ability to avoid it or to mitigate its negative impact, and thus results in anxiety. Here, we focus the broad literature on the neurobiology of anxiety through the lens of uncertainty. We identify five processes essential for adaptive anticipatory responses to future threat uncertainty, and propose that alterations to the neural instantiation of these processes results in maladaptive responses to uncertainty in pathological anxiety. This framework has the potential to advance the classification, diagnosis, and treatment of clinical anxiety. PMID:23783199

  15. Back to the future: The Grassroots of Hydrological Uncertainty

    NASA Astrophysics Data System (ADS)

    Smith, K. A.

    2013-12-01

    Uncertainties are widespread within hydrological science, and as society is looking to models to provide answers as to how climate change may affect our future water resources, the performance of hydrological models should be evaluated. With uncertainties being introduced from input data, parameterisation, model structure, validation data, and ';unknown unknowns' it is easy to be pessimistic about model outputs. But uncertainties are an opportunity for scientific endeavour, not a threat. Investigation and suitable presentation of uncertainties, which results in a range of potential outcomes, provides more insight into model projections than just one answer. This paper aims to demonstrate the feasibility of conducting computationally demanding parameter uncertainty estimation experiments on global hydrological models (GHMs). Presently, individual GHMs tend to present their one, best projection, but this leads to spurious precision - a false impression of certainty - which can be misleading to decision makers. Whilst uncertainty estimation is firmly established in catchment hydrology, GHM uncertainty, and parameter uncertainty in particular, has remained largely overlooked. Model inter-comparison studies that investigate model structure uncertainty have been undertaken (e.g. ISI-MIP, EU-WATCH etc.), but these studies seem premature when the uncertainties within each individual model itself have not yet been considered. This study takes a few steps back, going down to one of the first introductions of assumptions in model development, the assignment of model parameter values. Making use of the University of Nottingham's High Performance Computer Cluster (HPC), the Mac-PDM.09 GHM has been subjected to rigorous uncertainty experiments. The Generalised Likelihood Uncertainty Estimation method (GLUE) with Latin Hypercube Sampling has been applied to a GHM for the first time, to produce 100,000 simultaneous parameter perturbations. The results of this ensemble of 100

  16. Are models, uncertainty, and dispute resolution compatible?

    NASA Astrophysics Data System (ADS)

    Anderson, J. D.; Wilson, J. L.

    2013-12-01

    Models and their uncertainty often move from an objective use in planning and decision making into the regulatory environment, then sometimes on to dispute resolution through litigation or other legal forums. Through this last transition whatever objectivity the models and uncertainty assessment may have once possessed becomes biased (or more biased) as each party chooses to exaggerate either the goodness of a model, or its worthlessness, depending on which view is in its best interest. If worthlessness is desired, then what was uncertain becomes unknown, or even unknowable. If goodness is desired, then precision and accuracy are often exaggerated and uncertainty, if it is explicitly recognized, encompasses only some parameters or conceptual issues, ignores others, and may minimize the uncertainty that it accounts for. In dispute resolution, how well is the adversarial process able to deal with these biases? The challenge is that they are often cloaked in computer graphics and animations that appear to lend realism to what could be mostly fancy, or even a manufactured outcome. While junk science can be challenged through appropriate motions in federal court, and in most state courts, it not unusual for biased or even incorrect modeling results, or conclusions based on incorrect results, to be permitted to be presented at trial. Courts allow opinions that are based on a "reasonable degree of scientific certainty," but when that 'certainty' is grossly exaggerated by an expert, one way or the other, how well do the courts determine that someone has stepped over the line? Trials are based on the adversary system of justice, so opposing and often irreconcilable views are commonly allowed, leaving it to the judge or jury to sort out the truth. Can advances in scientific theory and engineering practice, related to both modeling and uncertainty, help address this situation and better ensure that juries and judges see more objective modeling results, or at least see

  17. Simplified Propagation of Uncertainty in the Non-Keplerian Problem

    NASA Astrophysics Data System (ADS)

    Park, I.; Scheeres, D.

    2014-09-01

    A major topic in the field of space situational awareness is the accurate mapping of the uncertainty of an observed object; this has led to high precision modeling of orbital motion and their associated uncertainty propagation. A main purpose of our research is to explore how much precision is needed in describing the dynamical motion of a spacecraft to accurately map uncertainty. To do this, we define an analytical simplified dynamical system (SDS) and probe the relation between accuracy and precision in uncertainty propagation. The use of an analytical theory that is precise enough can have significant savings in computation time. In this research, our SDS is developed based on analytic solutions found by applying the Deprit-Hori Lie transformation theory. An analytic approach has an advantage in that it can give more insight into how the short-period variations influence the uncertainty propagation as well as provide more efficient computation. The SDS includes multiple perturbations caused by the oblate Earth, gravitational attraction of a third-body, and solar radiation pressure (SRP). An artificial satellite in a Medium-Earth-Orbit (MEO) is chosen to magnify perturbing effects due to the third-body and SRP relative to the Earth oblateness. A reference uncertainty is generated through Monte-Carlo simulations based on the full dynamical system. The accuracy of the SDS is verified through two statistical methods: 1) comparison of the central moments of the probability density function, 2) statistical energy test. Improvements in computational efficiency are investigated by comparing two factors: 1) number of function calls, 2) computation time. In summary, we derive approximate analytic solutions including multiple perturbations, and then define the SDS from them for a satellite in the MEO region. We verify the accuracy of the uncertainty propagation with the SDS by applying two statistical methods as well as show improvements in the computational efficiency of

  18. Assessing hydrologic prediction uncertainty resulting from soft land cover classification

    NASA Astrophysics Data System (ADS)

    Loosvelt, Lien; De Baets, Bernard; Pauwels, Valentijn R. N.; Verhoest, Niko E. C.

    2014-09-01

    For predictions in ungauged basins (PUB), environmental data is generally not available and needs to be inferred by indirect means. Existing technologies such as remote sensing are valuable tools for estimating the lacking data, as these technologies become more widely available and have a high areal coverage. However, indirect estimates of the environmental characteristics are prone to uncertainty. Hence, an improved understanding of the quality of the estimates and the development of methods for dealing with their associated uncertainty are essential to evolve towards accurate PUB. In this study, the impact of the uncertainty associated with the classification of land cover based on multi-temporal SPOT imagery, resulting from the use of the Random Forests classifier, on the predictions of the hydrologic model TOPLATS is investigated through a Monte Carlo simulation. The results show that the predictions of evapotranspiration, runoff and baseflow are hardly affected by the classification uncertainty when area-averaged predictions are intended, implying that uncertainty propagation is only advisable in case a spatial distribution of the predictions is relevant for decision making or is coupled to other spatially distributed models. Based on the resulting uncertainty map, guidelines for additional data collection are formulated in order to reduce the uncertainty for future model applications. Because a Monte Carlo-based uncertainty analysis is computationally very demanding, especially when complex models are involved, we developed a fast indicative uncertainty assessment method that allows for generating proxies of the Monte Carlo-based result in terms of the mean prediction and its associated uncertainty based on a single model evaluation. These proxies are shown to perform well and provide a good indication of the impact of classification uncertainty on the prediction result.

  19. Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling

    NASA Astrophysics Data System (ADS)

    Sonnenborg, T. O.; Seifert, D.; Refsgaard, J. C.

    2015-09-01

    Projections of climate change impact are associated with a cascade of uncertainties including in CO2 emission scenarios, climate models, downscaling and impact models. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference period (1991-2010) to the future period (2081-2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context-dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty due to the climate models is more important for groundwater hydraulic heads and stream flow.

  20. Climate model uncertainty vs. conceptual geological uncertainty in hydrological modeling

    NASA Astrophysics Data System (ADS)

    Sonnenborg, T. O.; Seifert, D.; Refsgaard, J. C.

    2015-04-01

    Projections of climate change impact are associated with a cascade of uncertainties including CO2 emission scenario, climate model, downscaling and impact model. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference period (1991-2010) to the future period (2081-2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty on the climate models is more important for groundwater hydraulic heads and stream flow.

  1. Optimizing Integrated Terminal Airspace Operations Under Uncertainty

    NASA Technical Reports Server (NTRS)

    Bosson, Christabelle; Xue, Min; Zelinski, Shannon

    2014-01-01

    In the terminal airspace, integrated departures and arrivals have the potential to increase operations efficiency. Recent research has developed geneticalgorithm- based schedulers for integrated arrival and departure operations under uncertainty. This paper presents an alternate method using a machine jobshop scheduling formulation to model the integrated airspace operations. A multistage stochastic programming approach is chosen to formulate the problem and candidate solutions are obtained by solving sample average approximation problems with finite sample size. Because approximate solutions are computed, the proposed algorithm incorporates the computation of statistical bounds to estimate the optimality of the candidate solutions. A proof-ofconcept study is conducted on a baseline implementation of a simple problem considering a fleet mix of 14 aircraft evolving in a model of the Los Angeles terminal airspace. A more thorough statistical analysis is also performed to evaluate the impact of the number of scenarios considered in the sampled problem. To handle extensive sampling computations, a multithreading technique is introduced.

  2. Soft computing and fuzzy logic

    SciTech Connect

    Zadeh, L.A.

    1994-12-31

    Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neuro-computing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind.

  3. 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.

  4. Quantifying and Reducing Curve-Fitting Uncertainty in Isc

    SciTech Connect

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-06-14

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  5. Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

    PubMed Central

    Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong

    2012-01-01

    This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications. PMID:23012509

  6. Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint

    SciTech Connect

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-09-28

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  7. Analysis of Infiltration Uncertainty

    SciTech Connect

    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

  8. Extended Forward Sensitivity Analysis for Uncertainty Quantification

    SciTech Connect

    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

  9. SCALE-6 Sensitivity/Uncertainty Methods and Covariance Data

    SciTech Connect

    Williams, Mark L; Rearden, Bradley T

    2008-01-01

    Computational methods and data used for sensitivity and uncertainty analysis within the SCALE nuclear analysis code system are presented. The methodology used to calculate sensitivity coefficients and similarity coefficients and to perform nuclear data adjustment is discussed. A description is provided of the SCALE-6 covariance library based on ENDF/B-VII and other nuclear data evaluations, supplemented by 'low-fidelity' approximate covariances. SCALE (Standardized Computer Analyses for Licensing Evaluation) is a modular code system developed by Oak Ridge National Laboratory (ORNL) to perform calculations for criticality safety, reactor physics, and radiation shielding applications. SCALE calculations typically use sequences that execute a predefined series of executable modules to compute particle fluxes and responses like the critical multiplication factor. SCALE also includes modules for sensitivity and uncertainty (S/U) analysis of calculated responses. The S/U codes in SCALE are collectively referred to as TSUNAMI (Tools for Sensitivity and UNcertainty Analysis Methodology Implementation). SCALE-6-scheduled for release in 2008-contains significant new capabilities, including important enhancements in S/U methods and data. The main functions of TSUNAMI are to (a) compute nuclear data sensitivity coefficients and response uncertainties, (b) establish similarity between benchmark experiments and design applications, and (c) reduce uncertainty in calculated responses by consolidating integral benchmark experiments. TSUNAMI includes easy-to-use graphical user interfaces for defining problem input and viewing three-dimensional (3D) geometries, as well as an integrated plotting package.

  10. Hydrology, society, change and uncertainty

    NASA Astrophysics Data System (ADS)

    Koutsoyiannis, Demetris

    2014-05-01

    Heraclitus, who predicated that "panta rhei", also proclaimed that "time is a child playing, throwing dice". Indeed, change and uncertainty are tightly connected. The type of change that can be predicted with accuracy is usually trivial. Also, decision making under certainty is mostly trivial. The current acceleration of change, due to unprecedented human achievements in technology, inevitably results in increased uncertainty. In turn, the increased uncertainty makes the society apprehensive about the future, insecure and credulous to a developing future-telling industry. Several scientific disciplines, including hydrology, tend to become part of this industry. The social demand for certainties, no matter if these are delusional, is combined by a misconception in the scientific community confusing science with uncertainty elimination. However, recognizing that uncertainty is inevitable and tightly connected with change will help to appreciate the positive sides of both. Hence, uncertainty becomes an important object to study, understand and model. Decision making under uncertainty, developing adaptability and resilience for an uncertain future, and using technology and engineering means for planned change to control the environment are important and feasible tasks, all of which will benefit from advancements in the Hydrology of Uncertainty.