Uncertainty Propagation Research Articles

7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment.

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation. Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement. The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors. The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease.

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Model-based standardization using multiple imputation

BackgroundWhen studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictions over the target covariate distribution, and generate a covariate-adjusted estimate of the marginal treatment effect.MethodsThe standard approach to model-based standardization involves maximum-likelihood estimation and use of the non-parametric bootstrap. We introduce a novel, general-purpose, model-based standardization method based on multiple imputation that is easily applicable when the outcome model is a generalized linear model. We term our proposed approach multiple imputation marginalization (MIM). MIM consists of two main stages: the generation of synthetic datasets and their analysis. MIM accommodates a Bayesian statistical framework, which naturally allows for the principled propagation of uncertainty, integrates the analysis into a probabilistic framework, and allows for the incorporation of prior evidence.ResultsWe conduct a simulation study to benchmark the finite-sample performance of MIM in conjunction with a parametric outcome model. The simulations provide proof-of-principle in scenarios with binary outcomes, continuous-valued covariates, a logistic outcome model and the marginal log odds ratio as the target effect measure. When parametric modeling assumptions hold, MIM yields unbiased estimation in the target covariate distribution, valid coverage rates, and similar precision and efficiency than the standard approach to model-based standardization.ConclusionWe demonstrate that multiple imputation can be used to marginalize over a target covariate distribution, providing appropriate inference with a correctly specified parametric outcome model and offering statistical performance comparable to that of the standard approach to model-based standardization.

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. We utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

Stochastic analysis of plant available water estimates and soil water balance components simulated by a hydrological model

AbstractThe uncertainty in soil hydraulic parameters is often not taken into account in process‐based hydrological modeling. Performing runs with 104 stochastic parameter realizations, we evaluated the propagation of uncertainty in the Van Genuchten–Mualem (VGM) parameters into estimates of the threshold values of soil water content used to calculate the total and readily available water, and on the long‐term (30 years) simulations of evaporation, transpiration, bottom flux, and runoff by the SWAP hydrological model. The simulated scenarios included weather data from a location in southeast Brazil and seven soils from the same region cropped with maize, comprising a wide range of texture classes. The results showed that uncertainties in VGM parameters affect the estimates of total and readily available water. Water balance components obtained by a deterministic simulation with average VGM parameters did not always agree with the average or median of stochastic simulations, and stochastic simulations including parameter uncertainties should be preferred. Variations in yearly rainfall characteristics were more important for bottom flux and evaporation, while transpiration and runoff were more strongly influenced by the variations in soil hydraulic properties.

Investigations of Flow Boiling in Mini-Channels: Heat Transfer Calculations with Temperature Uncertainty Analyses

The article aims to explore boiling heat transfer in mini-channels with a rectangular cross-section using various fluids (HFE-649, HFE-7000, HFE-7100, and HFE-7200). Temperature measurements were conducted using infrared thermography for the heated wall and K-type thermocouples for the working fluid. The 2D mathematical model for heat transfer in the test section was proposed. Local heat transfer coefficients between the heated wall and the working fluid were determined from the Robin condition. The problem was solved by means of the finite element method (FEM) with Trefftz functions. The values of the heat transfer coefficient that were obtained were compared with the results calculated from Newton’s law of cooling. The average relative differences between the obtained results did not exceed 4%. The study included uncertainty analyses for temperature measurements with K- and T-type thermocouples. Expanded uncertainties were calculated using the uncertainty propagation and Monte Carlo methods. Precisely determining the uncertainties in contact temperature measurements is crucial to ensure accurate temperature data for subsequent heat transfer calculations. The results of the heat transfer investigations were compared in terms of fluid temperature, heat transfer coefficients, and boiling curves. HFE-7200 consistently exhibited the highest fluid temperature and temperature differences at boiling incipience, while HFE-7000 demonstrated the highest heat transfer coefficients. HFE-649 showed the lowest heat transfer coefficients. The boiling curves exhibited a typical shape, with a notable occurrence of ‘nucleation hysteresis phenomena’. Upon the analysis of two-phase flow patterns, bubbly and bubbly-slug structures were observed.

Statistical analysis of parameters and adsorption isotherm models.

The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.

Application of the Polynomial Chaos Expansion to the Uncertainty Propagation in Fault Transients in Nuclear Fusion Reactors: DTT TF Fast Current Discharge

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications.

Uncertainty Propagation in a Simplified Integrated Thermal Management System

An integrated generic plate model that describes single-phase transport combining heat acquisition and rejection regimes is investigated. Transient processes and input uncertainty propagation impacting temperature variations in this simplified, dimensionless integrated thermal management system are discussed. The heat acquisition plate is surface-heated on one side, with the heat being conducted into a liquid. The heated liquid is assumed to be adiabatically advected to the heat rejection plate. Convection boundary conditions are considered for both plates. The range of mean values of dimensionless input parameters, such as the Biot and Prandtl numbers, is determined by the choice of a particular plate metal and flowing liquid, and their stochastic nature is described by the Gaussian distributions. Adiabatic solutions for the dimensionless temperature variations in the plates are obtained for different temperatures and Reynolds numbers. As is shown, even an assumed small standard deviation for the input parameters may result in a drastic increase in the stochastic spread in the temperature variation in the heat rejection plate. Unlike the temperature variation across the acquisition plate, the temperature variation profile for the heat rejection plate is sensitive to the temperature of the incoming liquid and heat input at the acquisition plate.

Modeling rainfall-induced landslide using the concept of local factor of safety: Uncertainty propagation and sensitivity analysis

Rainfall-induced landslides (RILS) are commonly investigated using hydro-mechanical models with the concept of local factor of safety. The inputs of these models are usually prone to uncertainties. An uncertainty analysis is required to investigate how uncertainties propagate through the model and impact the predictions. An appropriate strategy for uncertainty propagation analysis is suggested in this work to deal with nonlinearity and high dimensionality of RILS problems. It proceeds by performing a sensitivity analysis in two steps. A screening technique is first applied to eliminate insignificant parameters. Then, a global sensitivity analysis is performed to rank the parameters by order of importance. The Sobol indices are used as sensitivity metrics. The polynomial chaos expansion is used to compute the Sobol indices. The proposed strategy is first applied to a hypothetical benchmark and then to a more realistic configuration where prior knowledge of parameters and type of soil are considered. The results show that, when prior knowledge of soil is available, the most important parameters are the coefficient of cohesion, friction angle and air entry pressure head, respectively. The results also show that 10% uncertainty on these parameters leads to about 20% uncertainty on the prediction of slope stability.

Characterisation of neutron fields at the n-lab, a fast neutron facility at the University of Cape Town

The n-lab is a fast neutron facility based in the Department of Physics, University of Cape Town, offering collimated neutron beams produced by an MP 320 deuterium–tritium sealed tube neutron generator, and a 220 GBq americium–beryllium radioisotopic source. Characterisations of the spatial and energy distributions of the fast neutron beams were performed using an EJ-301 organic liquid scintillator detector and digital data acquisition system. Neutron energy spectra were obtained through unfolding analyses with MAXED, and a Monte Carlo approach to the propagation of uncertainties was implemented. Measurements of fluence and neutron emission rates were determined through the neutron activation of copper foils and subsequently used to validate the scaling of the unfolded neutron energy spectra.

Comparison and evaluation of resolved resonance region covariance representations

ABSTRACT The covariance matrices for the evaluated resolved resonance region parameters are typically processed into a grouped covariance representation for use in uncertainty propagation studies. An alternative method for performing uncertainty propagation would be to use the resonance parameters covariance matrix to sample many realizations of resonance parameters, and then perform a simulation with each sample, employing the total Monte Carlo method. In this paper, we demonstrate that these two techniques can yield drastically different estimations of the uncertainty on k eff . Furthermore, we provide reasoning as to why the grouped covariance approximation is a nonphysical approximation for the resolved resonance region, and how sampling resonance parameters is able to capture variations in the structure of the point-wise cross sections. Our recommendation is that the community move away from the grouped covariance approximation and towards a continuous energy representation, which could be generated by sampling resonance parameters, and ensure that the resonance parameter covariance matrices in evaluated libraries yield realistic grouped cross section uncertainties, regardless of group structure.

Opinion: The importance of historical and paleoclimate aerosol radiative effects

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

Low-Order Automatic Domain Splitting Approach for Nonlinear Uncertainty Mapping

This paper introduces a novel method for the automatic detection and handling of nonlinearities in a generic transformation. A nonlinearity index that exploits second-order Taylor expansions and polynomial bounding techniques is first introduced to estimate the Jacobian variation of a nonlinear transformation. This index is then embedded into a low-order automatic domain splitting algorithm that accurately describes the mapping of an initial uncertainty set through a generic nonlinear transformation by splitting the domain whenever nonlinearities grow above a predefined threshold. The algorithm is illustrated in the critical case of orbital uncertainty propagation, and it is coupled with a tailored merging process that limits the growth of the domains in time by recombining them when nonlinearities decrease. The low-order automatic domain splitting algorithm is then combined with Gaussian mixture models to accurately describe the propagation of a probability density function.

A simple approach to estimating the nutrient and carbon storage benefits of restoring submerged aquatic vegetation, applied to Vallisneria americana in the Caloosahatchee Estuary, Florida, USA

Carbon and nutrient storage are important ecosystem services of submerged aquatic vegetation (SAV) and may be enhanced by SAV restoration. This study demonstrates an approach to quantifying the nutrient and carbon storage potential of SAV restoration, focusing on the SAV Vallisneria americana in the oligohaline reaches of Florida's Caloosahatchee River Estuary (CRE). The variables of habitat area, plant size, plant density, and tissue nutrient stoichiometry are considered, and estimates are made both for storage in living tissue and for deposition in sediments. System-specific parameter values are obtained from a combination of abundance surveys, historical accounts of abundance and distribution, and field and mesocosm studies of tissue stoichiometry responses to nutrient addition. These are integrated with literature values for sediment deposition rates of C, N, and P in other SAV systems to estimate carbon and nutrient storage for current conditions and various restoration scenarios. Calculations indicate that under a restoration scenario assuming a return to the abundance documented in 1998–1999, V. americana tissues could act as a substantial sink for macro-elements in the CRE, representing 28.4 mt-C, 2.6 mt-N, and 0.16 mt-P, and depositing 897 mt-C y−1, 68.5 mt-N y−1, and 3.87 mt-P y−1 in meadow sediments. However, at current low shoot densities and small shoot sizes, these benefits are two to three orders of magnitude less. In addition to the large difference between the restoration and current-conditions scenarios, propagation of uncertainty around parameter estimates within each scenario leads to wide ranges of uncertainty around model outputs. More system-specific empirical studies would help constrain parameter estimates and improve the model. Overall, these findings emphasize the sensitivity of C, N, and P storage and deposition rates to SAV habitat conditions, and the importance of reversing declines in SAV density through restoration, and other conservation measures.

Novel numerical method for uncertainty analysis of coupled vibro-acoustic problem considering thermal stress

Uncertainty is pervasive and exerts a profound influence in engineering practice. Uncertainty quantification is a primary task and of paramount importance in uncertainty analysis. To deal with both dependent and independent uncertain parameters in engineering problems, an improved multidimensional parallelepiped model (IMPM) is subsequently developed. Compared with traditional methods that entirely based on statistical features, more accurate uncertainty quantification results can be obtained by means of IMPM. To enhance the efficiency of uncertainty propagation analysis under IMPM, a radial basis function neural network (RBFNN) surrogate model is constructed to replace the original time-consuming finite element simulation model. Eventually, the effectiveness of proposed models and numerical methods is validated through an engineering example involving the coupled vibro-acoustic system considering thermal stress of a hypersonic vehicle.

Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience

We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems.

Reinforced concrete wall buildings with force‐limiting connections: Modeling effects and uncertainty propagation

AbstractThis paper assesses the effects of (1) the gravity load‐resisting system (GLRS) modeling approach, (2) the seismic force‐resisting system (SFRS) modeling approach, and (3) the uncertainty of the model parameters of the constitutive law of the longitudinal reinforcing steel of the SFRS on the seismic responses of a 12‐story reinforced concrete wall building with force‐limiting connections. This is achieved by conducting nonlinear numerical earthquake simulations. The seismic responses of the building models with force‐limiting connections using two GLRS modeling approaches, (1) a moment frame system and (2) a pin‐base lean‐on‐column system, are compared. The seismic responses of the building models with conventional connections and force‐limiting connections, respectively, using two SFRS modeling approaches, (1) a distributed‐plasticity modeling approach and (2) a lumped‐plasticity modeling approach, are compared. A joint probability density function for the ASTM‐A615 Grade 60 steel available in the literature is used to conduct an uncertainty propagation analysis through Monte Carlo simulation. The uncertainty in the steel model parameters is propagated to the seismic responses of the building models with conventional connections and force‐limiting connections, respectively. The distributions of the mean values of the peak structural responses of the building models are studied. The effects of the GLRS modeling approach on the seismic responses are not significant in the context of seismic performance‐based design and assessment of buildings with force‐limiting connections. The effects of the SFRS modeling approach and the uncertainty in the steel model parameters on the floor total acceleration and force responses are reduced by including force‐limiting connections.

Bayesian calibration of quasi-static field distortions in HARMONI

ABSTRACT The high angular resolution monolithic optical and near-infrared Integral field spectrograph is the first light visible and near-infrared integral field spectrograph for the Extremely Large Telescope. To reach the diffraction limit of the telescope (≈ 10 mas) and maintain an optimal image quality over long exposures, an accurate measurement of geometrical distortions in the instrument’s guide star field is needed. Geometrical distortions affecting the guide stars map directly to pointing errors of the science field. The systematic contribution to the pointing error can be calibrated and removed by a corrective model. In this work, we propose a formulation of the corrective model that aims to calibrate the geometrical field distortions down to a given target residual, as well as reducing the time spent in calibrations. We also propose a calibration procedure that accounts for the uncertainties of the measurement process. We developed a tool named harmoni-pm to simulate the expected pointing error caused by geometrical distortions and validate the effectiveness of the proposed corrective model. We also relied on pseudo Zernike polynomials to formulate the model, and the Bayesian theoretical framework to study the propagation of uncertainties along the calibration process. Compared with the classical calibration algorithm, the Bayesian calibration algorithm was able to reduce the number of calibration points required to reach the same model residual. Additionally, we were able to optimize the hardware of the Geometrical Calibration Unit and reduce the time required to achieve the calibration goal.

Investigation of Uncertainty Propagation in the Resolved Resonance Range

This paper presents a comparative study using the first-order formula (also called “sandwich”) and the multivariate sampling methodologies for propagating uncertainty in the resolved resonance region. A distinctive aspect of this work is the generation of random cross sections by sampling Resonance Parameters (RPs) taking in consideration their correlations provided in nuclear data libraries via the Resonance Parameter Covariance Matrix (RPCM). SCOOBY (Sampling COvariance OBservatorY), a newly developed sampling tool, is presented in this paper. This tool relies on the GAIA-2 nuclear data processing code to read and correct the RPCM and generate random cross sections. The study compares the sandwich method that relies on sensitivity coefficients and covariance matrices, with the sampling method, which involves numerous Monte Carlo simulations with random cross sections. The comparison is tested on an ICSBEP benchmark, PU-MET-MIXED-002, chosen for its sensitivity to 239Pu cross sections. The results indicate that both methods quantify similar uncertainties, confirming the reliability of both the SCOOBY module and the GAIA-2 code. By using the PU-MET-MIXED-002 benchmark and sampling 239Pu resonance parameters, this work demonstrates the effectiveness of these methodologies in estimating uncertainty in criticality safety calculations. The findings highlight the robustness of these approaches in uncertainty propagation, suggesting the need for further research on additional benchmarks and expanded uncertainty propagation studies.