Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

2021 ◽  
Author(s):  
Emmanuel Boafo ◽  
Emmanuel Numapau Gyamfi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Hydrogen Generation ◽  
Input Parameter ◽  
Distribution Functions ◽  
Physical Models ◽  
Severe Accident ◽  
Accident Analysis ◽  
Melt Flow Rate ◽  
Input Parameters

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with DAKOTA, an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 NPP accident scenario, which was modelled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters; COR_SC 1141(Melt flow rate per unit width at breakthrough candling) , COR_ZP (Porosity of fuel debris beds) and COR_EDR (Characteristic debris size in core region) contributed most significantly to the uncertainty in hydrogen generation.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

Uncertainty Quantification of Aeroacoustic Power Sources in Corrugated Pipes

2015 ◽  
Author(s):  
Maharudrayya Swamy ◽  
Pejman Shoeibi Omrani ◽  
Nestor Gonzalez Diez
Keyword(s):  
Uncertainty Quantification ◽  
Input Parameter ◽  
Distribution Functions ◽  
Geometrical Parameters ◽  
Acoustic Source ◽  
Parameter Uncertainties ◽  
Source Power ◽  
Power Sources ◽  
Edge Radius ◽  
Onset Velocity

Gas transport in corrugated pipes often exhibit whistling behavior, due to periodic flow-induced pulsations generated in the pipe cavities. These aero-acoustic sources are strongly dependent on the geometrical dimensions and features of the cavities. As a result, uncertainties in the exact shape and geometry play a significant role in determining the singing behavior of corrugated pipes. While predictive modelling for idealized periodic structures is well established, this paper focusses on the sensitivity analysis and uncertainty quantification (UQ) of uncertain geometrical parameters using probabilistic models. The two most influential geometrical parameters varied within this study are the cavity width and downstream edge radius. Computational Fluid Dynamics (CFD) analysis was used to characterize the acoustic source. Stochastic collocation method was used for propagation of input parameter uncertainties. The analysis was performed with both full tensor product grid and sparse grid based on level-2 Clenshaw-Curtis points. The results show that uncertainties in the width and downstream edge radius of the cavity have an effect on the acoustic source power, peak Strouhal number and consequently the whistling onset velocity. Based on the assumed input parameters distribution functions, the confidence levels for the prediction of onset velocity were calculated. Finally, the results show the importance of performing uncertainty analysis to get more insights in the source of errors and consequently leading to a more robust design or risk-management oriented decision.

scholarly journals Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

2019 ◽  
Vol 37 (4-6) ◽  
pp. 377-433
Author(s):  
Tatenda Nyazika ◽  
Maude Jimenez ◽  
Fabienne Samyn ◽  
Serge Bourbigot
Keyword(s):  
Sensitivity Analysis ◽  
Material Properties ◽  
Input Parameter ◽  
Fire Behavior ◽  
Sensitivity Study ◽  
Optimization Techniques ◽  
Decomposition Reactions ◽  
Input Parameters ◽  
Gradient Based ◽  
Complete Set

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

scholarly journals Analysis of Inputs Parameters Used to Estimate Enteric Methane Emission Factors Applying a Tier 2 Model: Case Study of Native Cattle in Senegal

2021 ◽  
Author(s):  
Séga Ndao
Keyword(s):  
Sensitivity Analysis ◽  
Methane Emission ◽  
Input Parameter ◽  
Emission Factors ◽  
Future Research ◽  
Tier 2 ◽  
Lactating Cows ◽  
Enteric Methane ◽  
Input Parameters ◽  
Native Cattle

In the context of the Paris Agreement, and considering the importance of methane emissions from cattle in West Africa, application of a Tier 2 method to estimate enteric methane emission factors is clearly pertinent. The current study has two purposes. Firstly, it aims to detect how much each input parameter contributes to the overall uncertainty of enteric methane emission factors for cattle. Secondly, it aims to identify which input parameters require additional research efforts for strengthening the evidence base, thus reducing the uncertainty of methane enteric emission factors. Uncertainty and sensitivity analysis methodologies were applied to input parameters in the calculation of enteric methane emission factors for lactating cows and adult male Senegalese native cattle using the IPCC Tier 2 model. The results show that the IPCC default input parameters, such as the coefficient for calculating net energy for maintenance (Cfi), digestible energy (DE) and the methane conversion rate (Ym) are the first, second and third most important input parameters, respectively, in terms of their contribution to uncertainty of the enteric methane emission factor. Sensitivity analysis demonstrated that future research in Senegal should prioritize the development of Ym, Cfi and DE in order to estimate enteric methane emission factors more accurately and to reduce the uncertainty of the national agricultural greenhouse gas 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.

Comparison of CORA and MELCOR Core Degradation Simulation and MELCOR Oxidation Model

2014 ◽  
Author(s):  
Jun Wang ◽  
Michael L. Corradini ◽  
Wen Fu ◽  
Troy Haskin ◽  
Wenxi Tian ◽  
...  
Keyword(s):  
Test Data ◽  
Flow Rate ◽  
Hydrogen Generation ◽  
Severe Accident ◽  
Generation Rate ◽  
Accident Analysis ◽  
University Of Wisconsin ◽  
Calculation Results ◽  
Oxidation Model ◽  
The University

MELCOR is widely used and sufficiently trusted for severe accident analysis. However, the occurrence of Fukushima has increased the focus on severe accident codes and their use. A MELCOR core degradation calculation was conducted at the University of Wisconsin – Madison. The calculation results were checked by comparing with a past CORA experiment. MELCOR calculation results included the flow rate of argon and steam, the generation rate of hydrogen. Through this work, the performance of MELCOR COR package was reviewed in detail. This paper compares the hydrogen generation rates predicted by MELCOR to the CORA test data. While agreement is reasonable it could be improved. Additionally, the MELCOR zirconium oxidation model was analyzed.

scholarly journals Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 416
Author(s):  
Branwen Snelling ◽  
Stephen Neethling ◽  
Kevin Horsburgh ◽  
Gareth Collins ◽  
Matthew Piggott
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Hazard Analysis ◽  
Input Parameter ◽  
Slope Angle ◽  
Rheological Parameters ◽  
Wave Characteristics ◽  
Run Up ◽  
Sph Simulation

Simulations of landslide generated waves (LGWs) are prone to high levels of uncertainty. Here we present a probabilistic sensitivity analysis of an LGW model. The LGW model was realised through a smooth particle hydrodynamics (SPH) simulator, which is capable of modelling fluids with complex rheologies and includes flexible boundary conditions. This LGW model has parameters defining the landslide, including its rheology, that contribute to uncertainty in the simulated wave characteristics. Given the computational expense of this simulator, we made use of the extensive uncertainty quantification functionality of the Dakota toolkit to train a Gaussian process emulator (GPE) using a dataset derived from SPH simulations. Using the emulator we conducted a variance-based decomposition to quantify how much each input parameter to the SPH simulation contributed to the uncertainty in the simulated wave characteristics. Our results indicate that the landslide’s volume and initial submergence depth contribute the most to uncertainty in the wave characteristics, while the landslide rheological parameters have a much smaller influence. When estimated run-up is used as the indicator for LGW hazard, the slope angle of the shore being inundated is shown to be an additional influential parameter. This study facilitates probabilistic hazard analysis of LGWs, because it reveals which source characteristics contribute most to uncertainty in terms of how hazardous a wave will be, thereby allowing computational resources to be focused on better understanding that uncertainty.

Validation Status of MAAP5 Core Melt Progression Model

2014 ◽  
Author(s):  
Chan Y. Paik ◽  
Paul McMinn ◽  
Christopher Henry ◽  
Wison Luangdilok
Keyword(s):  
Hydrogen Generation ◽  
Computer Code ◽  
Severe Accident ◽  
Accident Analysis ◽  
Primary System ◽  
Analysis Program ◽  
Fuel Rod ◽  
Progression Model ◽  
Hydrogen Mass ◽  
Quench Test

The Modular Accident Analysis Program (MAAP) is a computer code that is used for integrated severe accident analysis. The latest MAAP5 version was validated against the PHEBUS-PF FPT0 and FPT1 tests performed at CEA/IPSN, PBF-SFD Test 1–4 performed at INEL, and QUENCH Test 06 performed at FZK. PHEBUS FPT0, PHEBUS FPT1, and PBF-SFD Test 1–4 are in-pile experiments where a test bundle was housed in the center of a reactor. Comparisons were focused on fuel and shroud temperature histories, and hydrogen generation histories. For the PHEBUS tests, primary system and containment responses were also compared. In general, fuel and shroud temperatures are well predicted by MAAP5. Overall hydrogen mass generated is also well predicted except that MAAP5 over-predicts the total hydrogen mass generated for the FPT1 test. The hydrogen generation at the time of the peak oxidation phase for the FPT0 test is under-predicted while the hydrogen generation for the FPT1 test is over-predicted. In general, MAAP tends to over-predict mass relocations from the upper part of the bundle due to fuel rod collapses by the end of separate effects test transients.

scholarly journals Sensitivity Analysis of Common Input Parameters in Tools for Modeling Energy in Homes

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
Sheikh Tijan Tabban ◽  
Nelson Fumo
Keyword(s):  
Sensitivity Analysis ◽  
Energy Consumption ◽  
Reference Model ◽  
Residential Buildings ◽  
Input Parameter ◽  
Heating Equipment ◽  
Water Heating ◽  
Common Input ◽  
Heating And Cooling ◽  
Input Parameters

Energy models of buildings can be developed and used for analysis of energy consumption. A model offers the opportunity to simulate a building under specific conditions for analysis of energy efficiency measures or optimum design. Due to the great amount of information needed to develop an energy model of a building, the number of inputs can be reduced by making variable the most relevant input parameters and making the others to take common or standard values. In this study, an analysis of input parameters required by computational tools to estimate energy consumption in homes was done in two stages. In the first stage, common input parameters were identified for three software and three webtools based on the criteria that the input parameter should be common for at least two software and at least one webtool. In the second stage, a sensitivity analysis was performed on the inputs identified in the first stage. The software BEopt, developed by the National Renewable Energy Laboratory, was used as the source of typical input parameters to be compared, and to perform the simulations for the sensitivity analysis. The base or reference model to perform simulations for the sensitivity analysis corresponds to a model developed with information from a research house located on the campus of the University of Texas at Tyler and default inputs for the BEopt B-10 reference benchmark. Results show that besides the location, and consequently the weather, common parameters are building orientation, air leakage, space conditioning settings, space conditioning schedule, water heating equipment, and terrain. Among these parameters, the sensitivity analysis identified the largest variations in energy consumption for variations on space conditioning schedule (heating and cooling setpoints), followed by the type of water heating equipment. KEYWORDS: Residential Buildings; Energy Consumption; Energy Analysis; Input Parameters; Building Simulation; Source Energy

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