scholarly journals Improved Feasibility of Joint Uncertainty and Sensitivity Analyses Performance for Complex Scenario of Accidental Radioactivity Release into the Calm Atmosphere

2021 ◽  
Author(s):  
Petr Pecha ◽  
Miroslav Kárný
Keyword(s):  
Sensitivity Analysis ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Latin Hypercube Sampling ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Second Stage ◽  
Non Gaussian ◽  
Novel Concept ◽  
Complex Scenario

Abstract During several hours of the calm meteorological situation, a relatively significant level of radioactivity can be accumulated around the source. At the second stage, the calm situation is assumed to terminate and convective movement of the air induced by wind immediately starts. Random realisations of the input atmospheric dispersion model parameters for the CALM scenario are generated using LHS (Latin Hypercube Sampling) scheme. The resultant complex random radiological trajectories passing through both calm and convective stages of the release scenario represent inevitable prerequisite for prospective uncertainty analysis (UA) and sensitivity analysis (SA). Novel concept of Approximate Based (AB) solution approximates non-Gaussian sum of individual puffs at the end of the calm period by only one Gaussian “superpuff” distribution. Substantial acceleration of generation of sufficiently large number of random realisation makes further UA and SA feasible. Both procedures come from common mapping of the pairs of matrix random dependant output fields and vector of random input parameters realisations. Examples of 2-D random trajectories of deposited 137Cs are presented. Global sensitivity analysis utilising random sampling methods is outlined.

scholarly journals Global sensitivity analysis of parameter uncertainty in landscape evolution models

2018 ◽  
Vol 11 (12) ◽  
pp. 4873-4888 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Landscape Evolution ◽  
Relative Sensitivity ◽  
Function Approach ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Function ◽  
Sediment Yields ◽  
Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

Modeling bronchial circulation with application to soluble gas exchange: description and sensitivity analysis

1998 ◽  
Vol 84 (6) ◽  
pp. 2070-2088 ◽  
Author(s):  
Thien D. Bui ◽  
Donald Dabdub ◽  
Steven C. George
Keyword(s):  
Sensitivity Analysis ◽  
Gas Exchange ◽  
Pulmonary Circulation ◽  
Blood Flow Rate ◽  
Latin Hypercube Sampling ◽  
Inert Gases ◽  
Phase Iii ◽  
Model Parameters ◽  
Bronchial Circulation ◽  
Wide Range

The steady-state exchange of inert gases across an in situ canine trachea has recently been shown to be limited equally by diffusion and perfusion over a wide range (0.01–350) of blood solubilities (βblood; ml ⋅ ml−1 ⋅ atm−1). Hence, we hypothesize that the exchange of ethanol (βblood = 1,756 at 37°C) in the airways depends on the blood flow rate from the bronchial circulation. To test this hypothesis, the dynamics of the bronchial circulation were incorporated into an existing model that describes the simultaneous exchange of heat, water, and a soluble gas in the airways. A detailed sensitivity analysis of key model parameters was performed by using the method of Latin hypercube sampling. The model accurately predicted a previously reported experimental exhalation profile of ethanol ( R 2= 0.991) as well as the end-exhalation airstream temperature (34.6°C). The model predicts that 27, 29, and 44% of exhaled ethanol in a single exhalation are derived from the tissues of the mucosa and submucosa, the bronchial circulation, and the tissue exterior to the submucosa (which would include the pulmonary circulation), respectively. Although the concentration of ethanol in the bronchial capillary decreased during inspiration, the three key model outputs (end-exhaled ethanol concentration, the slope of phase III, and end-exhaled temperature) were all statistically insensitive ( P > 0.05) to the parameters describing the bronchial circulation. In contrast, the model outputs were all sensitive ( P < 0.05) to the thickness of tissue separating the core body conditions from the bronchial smooth muscle. We conclude that both the bronchial circulation and the pulmonary circulation impact soluble gas exchange when the entire conducting airway tree is considered.

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

2020 ◽  
Vol 148 (7) ◽  
pp. 2997-3014
Author(s):  
Caren Marzban ◽  
Robert Tardif ◽  
Scott Sandgathe
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Structure ◽  
Latin Hypercube Sampling ◽  
Forecast Model ◽  
The United States ◽  
Model Parameters ◽  
Analysis Methodology ◽  
Trigger Function ◽  
Multivariate Multiple Regression

Abstract A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

scholarly journals Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration

2020 ◽  
Vol 13 (10) ◽  
pp. 4691-4712
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton–ecosystem model (OPEM), implemented in the University of Victoria Earth System Climate Model (UVic-ESCM), using a Latin hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3-, PO43-, O2, and surface chlorophyll a concentrations. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3- inventory. Global O2 varies by a factor of 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.

scholarly journals Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4884
Author(s):  
Piotr Darnowski ◽  
Piotr Mazgaj ◽  
Mateusz Włostowski
Keyword(s):  
Sensitivity Analysis ◽  
Hydrogen Generation ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Latin Hypercube Sampling ◽  
Severe Accident ◽  
Sensitivity Analyses ◽  
Pressurized Water ◽  
Water Reactor ◽  
Best Estimate

In this study, uncertainty and sensitivity analyses were performed with MELCOR 2.2.18 to study the hydrogen generation (figure-of-merit (FoM)) during the in-vessel phase of a severe accident in a light water reactor. The focus of this work was laid on a large generation-III pressurized water reactor (PWR) and a double-ended hot leg (HL) large break loss of coolant accident (LB-LOCA) without a safety injection (SI). The FPT-1 Phebus integral experiment emulating LOCA was studied, where the experiment outcomes were applied for the plant scale modelling. The best estimate calculations were supplemented with an uncertainty analysis (UA) based on 400 input-decks and Latin hypercube sampling (LHS). Additionally, the sensitivity analysis (SA) utilizing the linear regression and linear and rank correlation coefficients was performed. The study was prepared with a new open-source MELCOR sensitivity and uncertainty tool (MelSUA), which was supplemented with this work. The FPT-1 best-estimate model results were within the 10% experimental uncertainty band for the final FoM. It was shown that the hydrogen generation uncertainties in PWR were similar to the FPT-1, with the 95% percentile being covered inside a ~50% band and the 50% percentile inside a ~25% band around the FoM median. Two different power profiles for PWR were compared, indicating its impact on the uncertainty but also on the sensitivity results. Despite a similar setup, different uncertainty parameters impacted FoM, showing the difference between scales but also a significant impact of boundary conditions on the sensitivity analysis.

scholarly journals Groundwater pollution risk using a modified Latin hypercube sampling

2006 ◽  
Vol 8 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Husam Baalousha
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Pollution ◽  
Probabilistic Models ◽  
Latin Hypercube Sampling ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Deterministic Models ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

scholarly journals Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

2020 ◽  
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory.

Sign in / Sign up

Export Citation Format

Share Document