Parameter Uncertainty Analysis Research Articles

Comparative life cycle assessment of landfill gas utilization in South Korea with parametric uncertainties

Landfill gas, a major contributor to global CH4 emissions, which have a much greater impact on global warming than CO2 does, is inevitable as a waste derivative. Upcycling landfill gas into value-added products is a powerful strategy to reduce greenhouse gas emissions and to use materials in a circular system. This study presents a full life cycle assessment of electricity produced using landfill gas at the Sudokwon landfill site, the largest case of landfill gas upcycling in South Korea. To ensure the representativeness of the results, we used statistical data to capture the technical and regional characteristics of the national market share as primary data. In addition, a parametric uncertainty analysis integrated with data quality assessment was conducted to reflect the temporal dynamics of landfills and enhance the reliability of this study. Our findings demonstrate that upcycling landfill gas into electricity is environmentally beneficial across sixteen impact categories compared with two baseline scenarios that do not involve landfill gas upcycling. The methodological approaches presented herein can address multifunctional challenges in waste treatment systems and improve the reliability of life cycle inventories.

Iber-PEST: Automatic calibration in fully distributed hydrological models based on the 2D shallow water equations

Calibrating physically based hydrological models manually is time-consuming and challenging. Automatic calibration tools have become prevalent in these models; however, its effective use requires not only a deep knowledge of the calibration procedure itself, but also understanding the structure of the model input and output files. In this study, we introduce Iber-PEST, a novel framework combining the parameter estimation and uncertainty analysis package, PEST, with Iber, a 2D model based on the shallow water equations. We demonstrate its capabilities by successfully calibrating eight storm events in northwestern Spain's basins, achieving promising results (mean NSE equal to 0.84). Furthermore, by applying the iterative ensemble smoother included in PEST, we demonstrate the feasibility to use the calibration data generated by Iber-PEST with alternative PEST algorithms not directly integrated in the Iber-PEST framework. This work successfully addresses the significant implementation barrier associated with the automatic calibration of Iber hydrological models with PEST.

Optimized flare gas recovery technology with consideration of life cycle assessment, reliability analyses, and uncertainity of parameters

One of the most important sources of carbon dioxide emissions is gas flaring from oil wells. In addition to having an adverse effect on the environment, burning gases with a high energy value causes significant economic losses. The present work evaluated the best allocation for flare gas recovery technologies between eleven technologies considering life cycle assessment, reliability, and uncertainty analysis of some economic parameters in Iran's oil field. By choosing three objective functions, i.e., the carbon dioxide emission reduction, return payback period, and global warming by life cycle assessment analysis, the best combination of system was selected from eleven technologies using the genetic (NSGAII) algorithm and decision-making technique (TOPSIS) simultaneously. It was shown that the technology's reliability affects the best solution. Correspondingly, the integration of an enhanced oil recovery plant and a natural gas liquid at the capital investment of 120 M$ and a benefit of 263 M$ annually, with a repayment period of nine months, CO2 reduction of 1314,579 tons per year, and global warming effect of 699,820 tone CO2 equivalent was introduced as the best technology.

Realistic estimation framework of radioactive release distributions into the environment during nuclear power plant accidents

Since the level 2 PSA of OPR-1000 was the requirement for regulatory purposes, Cs-137 release estimation was contained as the Nuclear Safety Act of ROK in which the Cs-137 release frequency exceeding 100 TBq was determined to happen less than 1.0E-6 per year after the Fukushima Daiichi Accident. However, Cs-137 release estimation from the conventional level 2 PSA of OPR-1000 provided uncertainty due to dominant accident sequence consideration. Thus, this study aimed to develop systematic methods through the overall framework to quantify realistic uncertainty concerns of radioactive material release using sensitivity and uncertainty analysis methods and apply them to OPR-1000. This framework helped to quantify confidential value for the Cs-137 release under the BEPU approach using both parametric and non-parametric methods to cover both realistic and conservative points. Uncertainty propagation analysis showed the unexpected uncertainty increase of Cs-137 release exceeding 100 TBq. The non-parametric uncertainty analysis provided higher conservative concerns for safety than the realistic concerns in terms of economics when compared with the parametric uncertainty analysis. Wilks’ uncertainty analysis showed the importance to consider conservative Cs-137 release in order to reach the higher safety need. Sensitivity analysis showed reasonable relationships between engineering safety parameters with the Cs-137 release.

Assessment of the Impact of Spatial Variability on Streamflow Predictions Using High-Resolution Modeling and Parameter Estimation: Case Study of Geumho River Catchment, South Korea

In this study, we analyzed the impact of model spatial resolution on streamflow predictions, focusing on high-resolution scenarios (<1 km) and flooding conditions at catchment scale. Simulation experiments were implemented for the Geumho River catchment in South Korea using Weather Research and the Forecasting Hydrological Modeling System (WRF-Hydro) with spatial resolutions of 100 m, 250 m, and 500 m. For the estimation of parameters, an automatic calibration tool based on the Model-Independent Parameter Estimation and Uncertainty Analysis (PEST) method was utilized. We assessed the hydrological predictions across different spatial resolutions considering calibrated parameters, calibration runtime, and accuracy of streamflow before and after calibration. For both Rainfall Events 1 and 2, significant improvements were observed after event-specific calibration in all resolutions. Particularly for 250 m resolution, NSE values of 0.8 or higher were demonstrated at lower gauging locations. Also, at a 250 m resolution, the changes in the calibrated parameter values (REFKDT) were minimized between Rainfall Events 1 and 2, implicating more effective calibration compared to the other resolutions. At resolutions of 100 m and 500 m, the optimal parameter values for the two events were distinctively different while more computational resources were required for calibration in Event 2 with drier antecedent conditions.

Comprehensive Assessment and Empirical Research on Green and Low-Carbon Technologies in the Steel Industry

The iron and steel industry is the leading industry supporting China’s industrial sector. Currently, there is less assessment work on green and low-carbon technologies for the iron and steel industry. This study clarifies the overall strategy of technology assessment by researching the relevant theories and methods of technology assessment. The study further establishes a scientific and reasonable comprehensive assessment index system of green and low-carbon technologies for the iron and steel industry from the aspects of technology index, economy and promotion, and application, including factors such as 11 indexes, the amount of energy saving, carbon dioxide emission reduction, and the resource recovery rate by utilising analytical and comprehensive methods and combining with the characteristics of the technologies. By analysing and comparing the advantages and disadvantages of the commonly used assessment methods, the entropy weighting method, grey correlation analysis method, and TOPSIS (technique for order preference by similarity to an ideal solution) method are combined and optimised to construct a comprehensive assessment model. The Latin hypercube sampling method is also introduced to analyse the technical parameters in combination with the evaluation model. Finally, fourteen iron and steel green and low-carbon technologies were selected for case assessment and uncertainty analysis of technical parameters, and it was found that the comprehensive assessment result of gas combined cycle power generation technology was optimal. After determining the weights of each assessment indicator through the entropy weighting method, it is concluded that the technical performance indicator > economic indicator > promotional indicator. A comparative analysis of the results under the three preference decisions concludes that technical performance is the main obstacle to improving the comprehensive assessment score of the technology, followed by the economics of the technology. Finally, the uncertainty analysis of the technical parameters shows that the fluctuation of the technical parameters not only affects the performance of the technology, but also affects the weights of the indicators and the comprehensive evaluation results of the technology.

Parameter Uncertainty and Sensitivity Analysis for Nutrient Modelling in a Forested Catchment Using the Sequential Uncertainty Fitting (SUFI-2) Algorithm in SWAT-CUP

Parameter Uncertainty and Sensitivity Analysis for Nutrient Modelling in a Forested Catchment Using the Sequential Uncertainty Fitting (SUFI-2) Algorithm in SWAT-CUP

Parameter sensitivity and uncertainty of a one-dimensional morphodynamic model in the Lower Yellow River

One-dimensional morphodynamic models are effective tools to conduct flood risk assessment and sediment management of alluvial rivers. In order to gain better understanding of morphodynamic models and improve the simulation efficiency and accuracy, a comprehensive investigation into parameter sensitivity and uncertainty has been conducted for a one-dimensional morphodynamic model. This model was calibrated using the measured flow and sediment data in the Lower Yellow River (LYR). The variance-based Sobol' and GLUE (Generalized Likelihood Uncertainty Estimation) tools were applied to quantify the parameter sensitivity and uncertainty to the simulation of different hydrological indicators in the meandering reach, with various model response surfaces selected. Results show that the importance of input parameters varied with different model outputs. The simulated water level and discharge hydrographs were more sensitive to the roughness coefficient at middle and high flows, with high total- and first-order sensitivity indices to the NSEs and RMSEs, while the simulated sediment concentration was more sensitive to the input parameters related to sediment transport capacity (K and M) and recovery coefficient (bd). Notably, the pairwise interactions between K and different roughness coefficients were significant in the simulated sediment concentration hydrographs. Spatio-temporal variability in the effect of input parameters is also revealed. The most contributing factor was screened as K for the maximum sediment concentration in the uppermost and lowermost reaches of the meandering reach, which turned to be bd in the middle reach. The most sensitive parameter to the simulated sediment concentration was screened as K in the initial period and then turned to be the exponent of recovery coefficient in case of erosion (be), which was bd in the last period over the simulation period. An analysis of parameter uncertainty indicates that the morphodynamic model can obtain satisfactory accuracy with most observed data concentrating in the 95% confidence interval, and the uncertainty of simulation outputs increased downstream. Improved understanding of parameter sensitivity and uncertainty in morphodynamic modelling can provide the scientific guidance for factor prioritization in the future model calibration.

Massively Parallel Monte Carlo Sampling for Xinanjiang Hydrological Model Parameter Optimization Using CPU-GPU Computer Cluster

The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. The MCS method is an internally concurrent algorithm that can be parallelized. It has the potential to execute on massively parallel hardware systems such as multi-node computer clusters equipped with multiple CPUs and GPUs, which are known as heterogeneous hardware systems. To take advantage of this, we parallelize the algorithm and implement it on a multi-node computer cluster that hosts multiple INTEL multi-core CPUs and NVIDIA many-core GPUs by using C++ programming language combined with the MPI, OpenMP, and CUDA parallel programming libraries. The parallel parameter optimization method is coupled with the Xinanjiang hydrological model to test the acceleration efficiency when tackling real-world applications that have a very high computational burden. Numerical experiments indicate, on the one hand, that the computational efficiency of the massively parallel parameter optimization method is significantly improved compared to single-core CPU code, and the multi-GPU code achieves the fastest speed. On the other hand, the scalability property of the proposed method is also satisfactory. In addition, the correctness of the proposed method is also tested using sensitivity and uncertainty analysis of the model parameters. Study results indicate good acceleration efficiency and reliable correctness of the proposed parallel optimization methods, which demonstrates excellent prospects in practical applications.

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population.

Global analysis of stochastic and parametric uncertainty in nonlinear dynamical systems: adaptative phase-space discretization strategy, with application to Helmholtz oscillator

Global analysis of stochastic and parametric uncertainty in nonlinear dynamical systems: adaptative phase-space discretization strategy, with application to Helmholtz oscillator

Upfront DPYD Genotype-Guided Treatment for Fluoropyrimidine-Based Chemotherapy in Advanced and Metastatic Colorectal Cancer: A Cost-Effectiveness Analysis

ObjectivesFluoropyrimidines are the most widely used chemotherapy drugs for advanced and metastatic colorectal cancer (CRC). Individuals with certain DPYD gene variants are exposed to an increased risk of severe fluoropyrimidine-related toxicities. This study aimed to evaluate the cost-effectiveness of preemptive DPYD genotyping to guide fluoropyrimidine therapy in patients with advanced or metastatic CRC. MethodsOverall survival of DPYD wild-type patients who received a standard dose and variant carriers treated with a reduced dose were analyzed by parametric survival models. A decision tree and a partitioned survival analysis model with a lifetime horizon were designed, taking the Iranian healthcare perspective. Input parameters were extracted from the literature or expert opinion. To address parameter uncertainty, scenario and sensitivity analyses were also performed. ResultsCompared with no screening, the genotype-guided treatment strategy was cost-saving ($41.7). Nevertheless, due to a possible reduction in the survival of patients receiving reduced-dose regimens, it was associated with fewer quality-adjusted life-years (9.45 vs 9.28). In sensitivity analyses, the prevalence of DPYD variants had the most significant impact on the incremental cost-effectiveness ratio. The genotyping strategy would remain cost-saving, as long as the genotyping cost is < $49 per test. In a scenario in which we assumed equal efficacy for the 2 strategies, genotyping was the dominant strategy, associated with less costs (∼$1) and more quality-adjusted life-years (0.1292). ConclusionsDPYD genotyping to guide fluoropyrimidine treatment in patients with advanced or metastatic CRC is cost-saving from the perspective of the Iranian health system.

Modeling for dependent competing failure processes of subsea pipelines considering parameter uncertainty based on dynamic Bayesian network

Leakages in subsea pipelines can have serious environmental and economic consequences, making it essential to develop reliable evaluation methods to prevent failures. Up to now, no comprehensive assessment methods focusing on parameter uncertainty of lifetime distribution functions in the dependent competing failure of subsea pipelines has been developed. This paper proposes a novel method for evaluating the reliability of subsea pipelines with dependent competing failure processes and parameter uncertainty, using dynamic Bayesian network (DBN). The independent variables and dependent variables of physical models are converted into parent nodes and child nodes of DBNs, respectively. Uncertain parameters of the degradation failure and sudden failure equations are denoted by root nodes and assigned to probability distributions in the DBN model. The interaction between degradation failure and sudden failure of subsea pipelines is studied, and two DBNs of competing failure based on variable degradation increment and variable degradation rate are established. Mutual information analysis finds out the important parameters affecting the reliability of subsea pipelines. The parameters in degradation failure model have little effects on the failure of subsea pipelines at first, but their impact is increasing over time. According to 3-sigma rule, three different situations are defined to perform uncertainty analysis of parameters in the developed DBN model. It shows that increasing the values of uncertain parameters, the reliability of the subsea pipelines would have obvious decrease earlier.

Uncertainty analysis for prediction of elastic properties of unidirectional bamboo fiber-reinforced composites

This paper presents a virtual framework to obtain the elastic response of unidirectional (UD) continuous bamboo fiber-reinforced composites based on the micromechanical modeling approach. The virtual framework involves the development of an algorithm for the generation of stochastic Representative Volume Elements (RVEs), taking into account the micro-structural uncertainty and variability of the bio-based bamboo fibers and their composites. The developed algorithm and model are validated with experimental characterizations and theoretical models. Different statistical micro-structural realizations with random fiber packing, fiber geometries, and variable RVE sizes are compared to investigate the effects of these parameters on the accuracy of the prediction of the elastic properties of bamboo fiber-reinforced composites. All models and loading scenarios are applied automatically through Python scripting in the Abaqus finite element (FE) solver. The uncertainty analysis of input parameters is carried out through parametric studies. This virtual micromechanical framework can be further linked in the future to macroscale models of bamboo or other natural fiber composites for multiscale prediction of the behavior of biocomposite structures under in-service loading.

A Monte Carlo-based Workflow for Geochemical Anomaly Identification Under Uncertainty and Global Sensitivity Analysis of Model Parameters

A Monte Carlo-based Workflow for Geochemical Anomaly Identification Under Uncertainty and Global Sensitivity Analysis of Model Parameters

Thermal performance enhancement on a box-type solar cooker using a triangular fin over a conventional cooking pot

The thermal performance of existing box-type solar cookers is limited and to improve the performance many research works reported on modification of solar cooker sub-systems i.e., cylindrical fin over the cooking pot, extra air duct, integrating with a photovoltaic panel, incorporation of heat storage materials and multiple reflectors. This paper presents a novel study of a triangular-finned cooking pot to increase the convective heat transfer rate and solar irradiance over the cooking pot. The thermal network diagram is presented and thermal losses are measured using MATLAB. The thermal visualization is presented with the help of COMSOL Multiphysics 5.5 and uncertainty analysis of different performance parameters is completed with Engineering Equation Solver (EES). The simulation results found thermal losses occur from the top, bottom and side are 4.26, 0.77 and 0.64 W/m2 respectively. The experimentally observed result indicates the cooking time is reduced from 94–95 min to 85–86 min during the boiling test of 1.5 kg water for the triangular finned cooking pot. The first figure of merit is 0.139 and the second figure of merit is 0.563 respectively for the same loading conditions. The cooking power is increased from 68.71 W to 77.06 W. The thermal efficiency is increased from 36.16 % to 40.55 %. The uncertainty analysis emphasizes the value of the performance parameter lies within an acceptable range. From thermal visualization, It is concluded that wall edges are less affected by heat flow and the correct placement of the cooking pot is necessary for uniform heating.

Comprehensive analysis of model parameter uncertainty influence on evaluation of HVAC operation to mitigate indoor virus: A case study for an office building in a cold and dry climate

Simulation-based studies of HVAC operation to mitigate indoor virus have been conducted to understand tradeoffs between indoor air quality (IAQ) and energy consumption. However, the influence of model parameter uncertainty in these studies has not been systematically quantified, which is critical when providing guidance to building operators. To address this gap, we identify 20 model parameters for a typical medium office building system in a cold and dry climate that can influence IAQ and energy consumption for indoor virus scenarios. Next, the distributions of the parameter values are estimated from literature and a set of simulation samples for three representative days is generated that simultaneously sample all of the parameters from their distributions. Two HVAC virus mitigation strategies are studied: increased filtration using MERV 13 filters and increased ventilation, via supply of 100% outdoor air into buildings. The model parameter uncertainty leads to significant variability in the results, particularly for the IAQ because of the highly uncertain virus generation rate. Use of 100% outdoor air can be beneficial for some uncertain scenarios on the hot day, but shows less IAQ improvement and/or significant energy increases on the other days. The virus removal efficiency and pressure drop of the HVAC filter, fan efficiency, and internal heat gain are the most important parameters to determining the tradeoffs of the two strategies. The results demonstrate how this model parameter uncertainty analysis methodology can provide practical guidance to building operators.

Environmental Footprints of the Catalytic Pyrolysis of Pine Needles through Integration of a Nickel-Decorated Chemically Modified Biochar Catalyst

The current study presents the life cycle analysis (LCA) of a biochar-catalyzed pyrolysis-based biorefinery system. Noncondensable gases (NCGs), produced as a coproduct, have been recycled back to the system, while biochar produced was employed for catalyst preparation, to visualize a biorefinery model. Results showed the considerable environmental impact of the process, in particular of the catalyst and energy sources utilized during the process. Matching up with the LCA metrics, both single parameter and Monte Carlo uncertainty analyses also revealed the high sensitivity of the obtained impacts for the impregnating chemical (i.e., ZnCl2, H3PO4, and NaOH) employed during catalyst preparation. Among different processes, the Ni/BC-H3PO4 catalyzed process with NCG recycling has emerged as the best possible case with the lowest environmental emissions (GWP ≈ 0.109 kg CO2 equivalent) and low cumulative energy demand (∼2.47 MJ) together with high process efficacy and productivity. Furthermore, sustaining process energy needs with varieties of different sources advocates for renewable sources (more specifically hydropower) over nonrenewable sources of energy. The study highlights the hotspots of the current technology, envisaging ways of reducing emissions through clean/renewable energy sources as well as utilizing less impact-causing intermediate materials, and thus, acting like a building stone in creating a base toward the vision of achieving net zero emissions.

Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

In the hilly region of the Western Himalayas, forest fires play a crucial role in forest destruction and biodiversity loss. Therefore, addressing the problem of forest fires is an immediate priority to mitigate the impact of forest fires. Therefore, the objective of this research is to assess and map the susceptibility zones of forest fire in the hilly state of Uttarakhand using geospatial techniques and machine learning algorithms (MLA) and deep Learning based sensitivity and uncertainty analysis of the forest fire ignition parameters. Thirteen forest fire ignition parameters were used to map forest fire susceptibility. Further, we used five distinct MLAs, including artificial neural networks (ANN), random forest (RF), logistic regression (LR), support vector machine (SVM) and an ensemble machine learning model. The generated models were validated using the receiver operating characteristic (ROC). The result shows that the ''very high'' and ''high'' zones for forest fires cover almost 55% of the total area of Uttarakhand. Validation of the models used indicated that all models produced most significant results. The ensemble machine learning had the highest accuracy (AUC = 0.977), followed by RF (0.973) and ANN (0.972). In addition, a Deep Neural Networks (DNN) based sensitivity approach is proposed to perform sensitivity and uncertainty analysis of the ignition parameters. For this, we have selected the best predictive model (Ensemble Machine Learning) to evaluate the sensitivity and uncertainty analysis. The sensitivity analysis shows that evapotranspiration has the highly influencing parameter to forest fires, followed by annual rainfall, wind speed, distance to agriculture, distance to tourist spots, distance to built-up and distance to road. However, aspect, temperature and distance to religious centers have less influence on susceptibility to forest fires in this study area. This study is unique because it provides new insights into the construction of an ensemble machine learning model and the integration of DNN-based sensitivity and uncertainty for robust and scientific modelling of forest fire susceptibility. The contribution of parameters demonstrates a novel approach in understanding the impact of a single parameter sample on forest fire susceptibility, which will help in developing prevention strategies. A similar approach may be used in other parts of the world with similar natural and anthropogenic conditions for mapping forest fire susceptibility.

Parametric MELCOR 2.2 sensitivity and uncertainty study with a focus on aerosols based on Phébus test FPT1

The Phébus test FPT-1 was carried out to investigate the fission product release, transportation, and distribution in the reactor coolant system and the simulated containment during a severe accident. The test was divided into three phases: the core degradation, aerosol transport, and containment phenomena. In this work, a parametric sensitivity and uncertainty analysis of the two first phases, i.e., core degradation and aerosol transport, is conducted with the focus on aerosol parameters.The study was done in a frame of WP4 of the European MUSA project whose main objective is to develop a methodology for severe accident uncertainty analysis.Due to the wide scope of the MUSA project, this study focuses not only on the UQ analysis but also on the parametric dependency and data interpretation. The main subject was investigating the uncertainty of the MELCOR 2.2 aerosol models and the relation between the chosen parameters as well as evaluating the usability of essential analytical tools available for such analyses.The calculations were carried out using the integral severe accident code MELCOR2.2 and the generic input deck of the FPT-1 experiment. The uncertainty quantification tool DAKOTA was used as a statistical tool. It was coupled with MELCOR within the SNAP environment.Fifteen uncertain parameters related with the aerosol release and transport were investigated. Two figures of merit were chosen to be the amount of deposition of Cs in the reactor coolant circuit, and the mass of air-borne aerosol in the containment. Preliminary analysis showed a significant correlation between the aerosol dynamic and agglomeration shape factors, density and minimum aerosol diameter with both FOMs. In addition to the standard uncertainty analysis, five MELCOR/DAKOTA UQ sensitivity calculations with modified parameters were performed and analyzed. Additionally, the impact of the parameters was investigated by excluding the most correlated ones from the analysis. The results confirm the strong correlation of shape factors and the importance of their ranges for overall UQ results.The authors show that by coupling the chosen analytical tools together, it is possible to carry out uncertainty quantification of severe accident analysis.Based on the study performed, the authors underline the need for further investigation of parameter distributions, the effect of the chosen number of samples on the results, and a global time-dependent UQ analysis.