Uncertainty propagation and parameter sensitivity analyses of relative permittivity models for use in soils

2013 ◽  
Author(s):  
Tyler J. McKee
Keyword(s):  
Relative Permittivity ◽  
Uncertainty Propagation ◽  
Parameter Sensitivity ◽  
Sensitivity Analyses

Sensitivity Analysis of Parameters for FPSO Model Updating

2020 ◽  
Author(s):  
Qihao Wu ◽  
Min Zhang ◽  
Tian’en Yang ◽  
Nuoya Xu ◽  
Junrong Wang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Model ◽  
Model Updating ◽  
Direct Method ◽  
Unit Length ◽  
Parameter Sensitivity ◽  
Irregular Waves ◽  
Sensitivity Analyses ◽  
Mooring Line ◽  
Restoring Force

Abstract This paper presents parameter sensitivity analysis for a FPSO numerical model updating. Generally, model test data are considered a better presentation of physical phenomena than its numerical counterpart. To minimize the discrepancy, model updating is of pragmatically importance. Model updating of a certain FPSO can be achieved by specific steps. In each step, the required properties of numerical model and test results are matched by means of tuning of the related parameters. To avoid inefficiency and physical meaning loss resulting from large modification of parameters which are insensitive to objective properties, parameter sensitivity analyses using the direct method are conducted in this paper. The investigated parameters mainly are the FPSO’s mooring line length, mooring line mass per unit length, mooring line cross-sectional area, fairlead position, FPSO hydrostatic stiffness, FPSO mass properties, linear and quadratic damping coefficients. According to the different stages of FPSO model updating, the objective functions are set to be the FPSO’s mooring line pretension, mooring system horizontal restoring force, the natural periods of the FPSO’s 6 degree of freedom motions and the standard deviation of motion response spectra under irregular waves.

scholarly journals A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

2020 ◽  
Vol 13 (2) ◽  
pp. 373-404 ◽  
Author(s):  
Andrew M. Sayer ◽  
Yves Govaerts ◽  
Pekka Kolmonen ◽  
Antti Lipponen ◽  
Marta Luffarelli ◽  
...  
Keyword(s):  
Uncertainty Propagation ◽  
Optimal Estimation ◽  
Sensitivity Analyses ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Formal Error ◽  
Uncertainty Estimates ◽  
Diagnostic Approaches

Abstract. Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

Parameter sensitivity analyses in seismic hazard

1996 ◽  
Vol 9 (4) ◽  
pp. 629-634
Author(s):  
Jian Wang ◽  
Meng-Tan Gao
Keyword(s):  
Seismic Hazard ◽  
Parameter Sensitivity ◽  
Sensitivity Analyses

scholarly journals Sub catchment Assessment of snowpack and snowmelt change by analyzing elevation bands and parameter sensitivity in the high Himalayas

2017 ◽  
Author(s):  
Vishal Singh ◽  
Manish Kumar Goyal ◽  
Rao Y. Surampalli ◽  
Francisco Munoz-Arriola
Keyword(s):  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Parameter Sensitivity ◽  
Snow Accumulation ◽  
Sensitivity Analyses ◽  
River Catchment ◽  
Hydrological Parameters ◽  
Streamflow Generation ◽  
Satluj River

Abstract. The present work proposes to improve estimates of how much streamflow is generated by snow in the watersheds of the steep Himalayas. Half of the earth’s glacial catchments in nonpolar areas are in the Himalayas, and they generate almost a third of the streamflows in India. In River catchments with glacier presence in the region, temporal variability in streamflow generation and the associated distribution of accumulated snow illustrate how changes in snowmelt and precipitation can affect water supplies to a growing population of 1.3 billion people. Estimations of snowpack and snowmelt in watersheds are critical for understanding streamflow generation and sources of catchments. However, estimating precipitation and snow accumulation is constrained by the difficulties complex terrain poses to data collection. The primary objective of this study is to assess the role of elevations in the computation of snowfall (snowpack) and snowmelt in sub-catchments. The study area is the Satluj River Catchment (up to Kasol gauge) with moderate (e.g., 526 m) to very high elevations (e.g., 7429 m) dominated by snow covers and glaciers. The Satluj River Catchment was divided into 14 sub-catchments. Snowpack and snowmelt variations in the sub-catchments in both historical and projected near-term (2011–2130) periods were analyzed using observed and Global Circulation Model (GCM) data sets. Both hydrological scenarios used elevation bands and parameter-sensitivity analyses built in the Soil Water Assessment Tool (SWAT) model. For model calibration/validation and parameter sensitivity analysis, an advanced optimization method — namely, Sequential Uncertainty Fitting (SUFI2) approach was used with multiple hydrological parameters. Among all parameters, the curve number (CN2) was found significantly sensitive for computations. The snowmelt hydrological parameters such as snowmelt factor maximum (SMFMX) and snow coverage (SNO50COV) significantly affected objective functions such as R2 and NSE during the model optimization process. The computed snowpack and snowmelt were found highly variable over the Himalayan sub-catchments as also reported by previous researchers in other regions. The magnitude of snowpack change consistently decreases across all the sub-catchments of the Satluj River Catchment (varying between 4 % and 42 %). The highest percentage of changes in snowpack was observed over high-elevation subcatchments.

scholarly journals Post-Closure Safety Calculations for the Disposal of Spent Nuclear Fuel in a Generic Horizontal Drillhole Repository

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2599 ◽  
Author(s):  
Stefan Finsterle ◽  
Richard A. Muller ◽  
John Grimsich ◽  
John Apps ◽  
Rod Baltzer
Keyword(s):  
Nuclear Fuel ◽  
Spent Nuclear Fuel ◽  
Uncertainty Propagation ◽  
Radioactive Decay ◽  
Transport Processes ◽  
Sensitivity Analyses ◽  
Annual Dose ◽  
Performance Metric ◽  
Safety Function ◽  
Wide Range

The post-closure performance of a generic horizontal drillhole repository for the disposal of spent nuclear fuel (SNF) is quantitatively evaluated using a physics-based numerical model that accounts for coupled thermal-hydrological flow and radionuclide transport processes. The model incorporates most subcomponents of the repository system, from individual waste canisters to the geological far field. The main performance metric is the maximum annual dose to an individual drinking potentially contaminated water taken from a well located above the center of the repository. Safety is evaluated for a wide range of conditions and alternative system evolutions, using deterministic simulations, sensitivity analyses, and a sampling-based uncertainty propagation analysis. These analyses show that the estimated maximum annual dose is low (on the order of 10−4 mSv yr−1, which is 1000 times smaller than a typical dose standard), and that the conclusions drawn from this dose estimate remain valid even if considerable changes are made to key assumptions and property values. The depth of the repository and the attributes of its configuration provide the main safety function of isolation from the accessible environment. Long-term confinement of radionuclides in the waste matrix and slow, diffusion-dominated transport leading to long migration times allow for radioactive decay to occur within the repository system. These preliminary calculations suggest that SNF can be safely disposed in an appropriately sited and carefully constructed and sealed horizontal drillhole repository.

Parameter Uncertainty Propagation Analysis for Urban Rainfall Runoff Modelling

1994 ◽  
Vol 29 (1-2) ◽  
pp. 145-154 ◽  
Author(s):  
Jianhua Lei ◽  
Wolfgang Schilling
Keyword(s):  
Parameter Uncertainty ◽  
Uncertainty Propagation ◽  
Parameter Sensitivity ◽  
Model Output ◽  
Rainfall Runoff ◽  
Order Analysis ◽  
First Order ◽  
Output Uncertainty ◽  
Uncertainty Propagation Analysis ◽  
Propagation Analysis

The paper proposes a strategy for model uncertainty propagation analysis. As an example, parameter uncertainty propagation analysis in the runoff block of the HYSTEM-EXTRAN model is carried out. The model is a modification of the well-known SWMM (Storm Water Management Model). Uncertainty propagation methods such as first-order analysis, sensitivity analysis, statistical linearization and Monte-Carlo analysis are discussed and applied. A pathway of parameter uncertainty propagation analysis is given based on validity, simplicity, and computational requirements. The pathway starts with sensitivity analysis which may help to reduce the dimensions of a multidimensional model by discarding insensitive parameters. This is to obtain a mathematically tractable uncertainty propagation problem for a complicated model. Then, the nonlinearity of the model must be quantified to check the validity of first-order analysis. If first-order analysis is not valid, and if components of model output uncertainty need to be known, the application of statistical linearization is the only analytical alternative. Monte-carlo analysis can always be applied and taken as a reference as long as the components of the model output uncertainty are not of interest. The parameter sensitivity is characterized by its sensitivity coefficient which is defined as the ratio of the coefficient of variance of a model output to the coefficient of variance of the model parameter itself. A nonlinear rainfall runoff model usually results in a variable parameter sensitivity. Hence, recommendations about parameter sensitivity cannot be generalized for a given rainfall-runoff model, but depend on the type and the range of the model output variable. It is shown that the type of probability density function describing the parameter uncertainty with known mean and variance has only a small effect on the results of the model output uncertainty.

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