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DOE OSTI2026

On the Statistical Uncertainty of Monte Carlo-Calculated Scattering Sensitivities

Sensitivity coefficients calculated with Monte Carlo codes are widely used for nuclear data uncertainty quantification in the modeling and simulation of complex 3D reactor systems. This study systematically compares sensitivity coefficients and associated statistical uncertainties for the multiplication factor and fuel temperature reactivity across multiple Monte Carlo codes (SCALE/KENO, SCALE/Shift, MCNP, and Serpent) using simple models representing light-water reactors and advanced reactor concepts. For multiplication factor sensitivities, statistical uncertainties are generally acceptable, although scattering sensitivities show significantly larger statistical uncertainties than, for example, fission and capture reactions. Fuel temperature reactivity sensitivities show significantly larger statistical uncertainties across all reactions. Elastic scattering sensitivities are the most problematic: all Monte Carlo codes fail to resolve energy-dependent coefficients, and they produce dramatically different energy-collapsed values. Critically, the use of these sensitivity coefficients in nuclear data uncertainty propagation leads to reduced statistical uncertainties in individual uncertainty contributions. This can lead to the masking of unusable sensitivity coefficients and producing misleading uncertainty results. The findings of this study show that new or enhanced methods are needed to improve Monte Carlo elastic scattering sensitivity calculations. Additionally, this study shows the relevance of verifying sensitivity coefficients through direct perturbation calculations for individual nuclide reactions, instead of only for total cross sections as commonly done.

Bostelmann, Rike [ORNL] (ORCID:0000000165968088)↗

NASA NTRS2001

Our Sun IV: The Standard Model and Helioseismology: Consequences of Uncertainties in Input Physics and in Observed Solar Parameters

Helioseismic frequency observations provide an extremely accurate window into the solar interior; frequencies from the Michaelson Doppler Imager (MDI) on the Solar and Heliospheric Observatory (SOHO) spacecraft, enable the adiabatic sound speed and adiabatic index to be inferred with an accuracy of a few parts in 10(exp 4) and the density with an accuracy of a few parts in 10(exp 3). This has become a Serious challenge to theoretical models of the Sun. Therefore, we have undertaken a self-consistent, systematic study of the sources of uncertainties in the standard solar models. We found that the largest effect on the interior structure arises from the observational uncertainties in the photospheric abundances of the elements, which affect the sound speed profile at the level of 3 parts in 10(exp 3). The estimated 4% uncertainty in the OPAL opacities could lead to effects of 1 part in 10(exp 3); the approximately 5%, uncertainty in the basic pp nuclear reaction rate would have a similar effect, as would uncertainties of approximately 15% in the diffusion constants for the gravitational settling of helium. The approximately 50% uncertainties in diffusion constants for the heavier elements would have nearly as large an effect. Different observational methods for determining the solar radius yield results differing by as much as 7 parts in 10(exp 4); we found that this leads to uncertainties of a few parts in 10(exp 3) in the sound speed int the solar convective envelope, but has negligible effect on the interior. Our reference standard solar model yielded a convective envelope position of 0.7135 solar radius, in excellent agreement with the observed value of 0.713 +/- 0.001 solar radius and was significantly affected only by Z/X, the pp rate, and the uncertainties in helium diffusion constants. Our reference model also yielded envelope helium abundance of 0.2424, in good agreement with the approximate range of 0.24 to 0.25 inferred from helioseismic observations; only extreme Z/X values yielded envelope helium abundance outside this range. We found that other current uncertainties, namely, in the solar age and luminosity, in nuclear rates other than the pp reaction, in the low-temperature molecular opacities, and in the low-density equation of state, have no significant effect on the quantities that can be inferred from helioseismic observations. The predicted pre-main-sequence lithium depletion is uncertain by a factor of 2. The predicted neutrino capture rate is uncertain by approximately 30% for the Cl-27 experiment and by approximately 3% for Ga-71 experiments, while the B-8 neutrino flux is uncertain by approximately 30%.

Boothroyd, Arnold I.↗

NASA NTRS1995

Credible Computations: Standard and Uncertainty

The discipline of computational fluid dynamics (CFD) is at a crossroad. Most of the significant advances related to computational methods have taken place. The emphasis is now shifting from methods to results. Significant efforts are made in applying CFD to solve design problems. The value of CFD results in design depends on the credibility of computed results for the intended use. The process of establishing credibility requires a standard so that there is a consistency and uniformity in this process and in the interpretation of its outcome. The key element for establishing the credibility is the quantification of uncertainty. This paper presents salient features of a proposed standard and a procedure for determining the uncertainty. A customer of CFD products - computer codes and computed results - expects the following: A computer code in terms of its logic, numerics, and fluid dynamics and the results generated by this code are in compliance with specified requirements. This expectation is fulfilling by verification and validation of these requirements. The verification process assesses whether the problem is solved correctly and the validation process determines whether the right problem is solved. Standards for these processes are recommended. There is always some uncertainty, even if one uses validated models and verified computed results. The value of this uncertainty is important in the design process. This value is obtained by conducting a sensitivity-uncertainty analysis. Sensitivity analysis is generally defined as the procedure for determining the sensitivities of output parameters to input parameters. This analysis is a necessary step in the uncertainty analysis, and the results of this analysis highlight which computed quantities and integrated quantities in computations need to be determined accurately and which quantities do not require such attention. Uncertainty analysis is generally defined as the analysis of the effect of the uncertainties involved in all stages of a process on the final responses. There are two approaches for conducting the uncertainty analysis: experimental and computational. These analyses and approaches are briefly described.

Mehta, Unmeel B.↗

NASA NTRS2002

Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

Green, Lawrence L.↗

NASA NTRS2005

Uncertainty Considerations for Ballistic Limit Equations

The overall risk for any spacecraft system is typically determined using a Probabilistic Risk Assessment (PRA). A PRA attempts to determine the overall risk associated with a particular mission by factoring in all known risks (and their corresponding uncertainties, if known) to the spacecraft during its mission. The threat to mission and human life posed by the mircro-meteoroid & orbital debris (MMOD) environment is one of the risks. NASA uses the BUMPER II program to provide point estimate predictions of MMOD risk for the Space Shuttle and the International Space Station. However, BUMPER II does not provide uncertainty bounds or confidence intervals for its predictions. With so many uncertainties believed to be present in the models used within BUMPER II, providing uncertainty bounds with BUMPER II results would appear to be essential to properly evaluating its predictions of MMOD risk. The uncertainties in BUMPER II come primarily from three areas: damage prediction/ballistic limit equations, environment models, and failure criteria definitions. In order to quantify the overall uncertainty bounds on MMOD risk predictions, the uncertainties in these three areas must be identified. In this paper, possible approaches through which uncertainty bounds can be developed for the various damage prediction and ballistic limit equations encoded within the shuttle and station versions of BUMPER II are presented and discussed. We begin the paper with a review of the current approaches used by NASA to perform a PRA for the Space Shuttle and the International Space Station, followed by a review of the results of a recent sensitivity analysis performed by NASA using the shuttle version of the BUMPER II code. Following a discussion of the various equations that are encoded in BUMPER II, we propose several possible approaches for establishing uncertainty bounds for the equations within BUMPER II. We conclude with an evaluation of these approaches and present a recommendation regarding which of them would be the most appropriate to follow.

Schonberg, W. P.↗

NASA NTRS2011

Detailed Uncertainty Analysis of the Ares I A106 Liftoff/Transition Database

The Ares I A106 Liftoff/Transition Force and Moment Aerodynamics Database describes the aerodynamics of the Ares I Crew Launch Vehicle (CLV) from the moment of liftoff through the transition from high to low total angles of attack at low subsonic Mach numbers. The database includes uncertainty estimates that were developed using a detailed uncertainty quantification procedure. The Ares I Aerodynamics Panel developed both the database and the uncertainties from wind tunnel test data acquired in the NASA Langley Research Center s 14- by 22-Foot Subsonic Wind Tunnel Test 591 using a 1.75 percent scale model of the Ares I and the tower assembly. The uncertainty modeling contains three primary uncertainty sources: experimental uncertainty, database modeling uncertainty, and database query interpolation uncertainty. The final database and uncertainty model represent a significant improvement in the quality of the aerodynamic predictions for this regime of flight over the estimates previously used by the Ares Project. The maximum possible aerodynamic force pushing the vehicle towards the launch tower assembly in a dispersed case using this database saw a 40 percent reduction from the worst-case scenario in previously released data for Ares I.

Hanke, Jeremy L.↗

NASA NTRS2013

Uncertainty Determination for Aeroheating in Uranus and Saturn Probe Entries by the Monte Carlo Method

The 2013-2022 Decaedal survey for planetary exploration has identified probe missions to Uranus and Saturn as high priorities. This work endeavors to examine the uncertainty for determining aeroheating in such entry environments. Representative entry trajectories are constructed using the TRAJ software. Flowfields at selected points on the trajectories are then computed using the Data Parallel Line Relaxation (DPLR) Computational Fluid Dynamics Code. A Monte Carlo study is performed on the DPLR input parameters to determine the uncertainty in the predicted aeroheating, and correlation coefficients are examined to identify which input parameters show the most influence on the uncertainty. A review of the present best practices for input parameters (e.g. transport coefficient and vibrational relaxation time) is also conducted. It is found that the 2(sigma) - uncertainty for heating on Uranus entry is no more than 2.1%, assuming an equilibrium catalytic wall, with the uncertainty being determined primarily by diffusion and H(sub 2) recombination rate within the boundary layer. However, if the wall is assumed to be partially or non-catalytic, this uncertainty may increase to as large as 18%. The catalytic wall model can contribute over 3x change in heat flux and a 20% variation in film coefficient. Therefore, coupled material response/fluid dynamic models are recommended for this problem. It was also found that much of this variability is artificially suppressed when a constant Schmidt number approach is implemented. Because the boundary layer is reacting, it is necessary to employ self-consistent effective binary diffusion to obtain a correct thermal transport solution. For Saturn entries, the 2(sigma) - uncertainty for convective heating was less than 3.7%. The major uncertainty driver was dependent on shock temperature/velocity, changing from boundary layer thermal conductivity to diffusivity and then to shock layer ionization rate as velocity increases. While radiative heating for Uranus entry was negligible, the nominal solution for Saturn computed up to 20% radiative heating at the highest velocity examined. The radiative heating followed a non-normal distribution, with up to a 3x variation in magnitude. This uncertainty is driven by the H(sub 2) dissociation rate, as H(sub 2) that persists in the hot non-equilibrium zone contributes significantly to radiation.

Palmer, Grant↗

NASA NTRS2014

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

CFD↗

NASA NTRS2014

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Fluid Dynamics↗

NASA NTRS2014

Uncertainty in Agricultural Impact Assessment

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

damage assessment↗

NASA NTRS2017

Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

Baurle, R. A.↗

NASA NTRS2017

Evaluation of the Uncertainty in JP-7 Kinetics Models Applied to Scramjets

One of the challenges of designing and flying a scramjet-powered vehicle is the difficulty of preflight testing. Ground tests at realistic flight conditions introduce several sources of uncertainty to the flow that must be addressed. For example, the scales of the available facilities limit the size of vehicles that can be tested and so performance metrics for larger flight vehicles must be extrapolated from ground tests at smaller scales. To create the correct flow enthalpy for higher Mach number flows, most tunnels use a heater that introduces vitiates into the flow. At these conditions, the effects of the vitiates on the combustion process is of particular interest to the engine designer, where the ground test results must be extrapolated to flight conditions. In this paper, the uncertainty of the cracked JP-7 chemical kinetics used in the modeling of a hydrocarbon-fueled scramjet was investigated. The factors that were identified as contributing to uncertainty in the combustion process were the level of flow vitiation, the uncertainty of the kinetic model coefficients and the variation of flow properties between ground testing and flight. The method employed was to run simulations of small, unit problems and identify which variables were the principal sources of uncertainty for the mixture temperature. Then using this resulting subset of all the variables, the effects of the uncertainty caused by the chemical kinetics on a representative scramjet flow-path for both vitiated (ground) and nonvitiated (flight) flows were investigated. The simulations showed that only a few of the kinetic rate equations contribute to the uncertainty in the unit problem results, and when applied to the representative scramjet flowpath, the resulting temperature variability was on the order of 100 K. Both the vitiated and clean air results showed very similar levels of uncertainty, and the difference between the mean properties were generally within the range of uncertainty predicted.

Norris, A. T.↗

NASA NTRS2018

Aircraft System Noise Prediction Uncertainty Quantification for a Hybrid Wing Body Subsonic Transport Concept

Aircraft system level noise prediction for advanced, unconventional concepts has undergone significant improvement over the past two decades. The prediction modeling uncertainty must be quantified so that potential benefits of unconventional configurations, which are outside of the range of empirical models, can be reliably assessed. This paper builds on previous work in an effort to improve estimates of element prediction uncertainties where the prediction methodology has been improved, or new experimental validation data are available, to provide an estimate of the system level uncertainty in the prediction process. In general, the uncertainty of the prediction will be strongly dependent on the aircraft configuration as well as which technologies are integrated. While the quantitative uncertainty values contained here are specific to the hybrid wing body design presented, the underlying process is the same regardless of configuration. A refined process for determining the uncertainty for each element of the noise prediction is detailed in this paper. The system level uncertainty in the prediction of the aircraft noise is determined at the three certification points, using a Monte Carlo method. Comparisons with previous work show a reduction of 1 EPNdB in the 95%coverage interval of the cumulative noise level. The largest impediment for continued reduction in uncertainty for the hybrid wing body concept is the need for improved modeling and validation experiments for fan noise, propulsion airframe aeroacoustic effects, and the Krueger flap, which comprise the bulk of the uncertainty in the cumulative certification noise level.

June, Jason C.↗

NASA NTRS2018

Trim Flight Conditions for a Low-Boom Aircraft Design Under Uncertainty

The purpose of this paper is to investigate the noise generation of a low-boom aircraft design in flight trim conditions under uncertainty. The deflection of control surfaces to maintain a trimmed flight state has the potential to change the perceived loudness at the ground. Furthermore, the uncertainties associated with the control surface deflections can complicate the overall uncertainty quantification. Incorporation of the uncertainties in the prediction of perceived sound levels during the design phase can lead to improved robustness. In this paper, a brief review of low-boom flight trim research is presented. Realistic flight trim conditions requiring control surface deflection are integrated into the current research efforts for uncertainty quantification and vehicle design. In addition, a generalized set of procedures for the characterization of uncertainties in flight trim conditions are introduced. In a case study of the application of these procedures, a 5 decibel average difference in the perceived level of loudness was found between clean (no deflections) and trimmed configurations. Also, uncertainties attributable to control surface deflection were found to account for, on average, over 50% of the total near field uncertainty. Uncertainty discretization methods implemented were able to give more insight into the overall global variances.

Phillips, Ben D↗

NASA NTRSDate not supplied

Uncertainty in Observational Estimates of the Aerosol Direct Radiative Effect and Forcing

Aerosols continue to be responsible for the largest uncertainty in determining the anthropogenic radiative forcing of the climate. To both reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE, the direct interaction with solar radiation by all aerosols) and to optimize the design of future observing systems, we build a framework for assessing uncertainty in aerosol DRE and the aerosol direct radiative forcing (DRF, the radiative effect of just anthropogenic aerosols, RF_ari). Shortwave aerosol radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels are used to compute a lower-bound on the systematic uncertainty in observational estimates of the aerosol DRE/DRF by making the optimistic assumption that global aerosol observations can be made with the accuracy found in the Aerosol Robotic Network (AERONET) sun photometer retrievals. The total uncertainty is shown to be dominated by contributions from the aerosol single scattering albedo uncertainty. These uncertainty estimates were compared to a literature survey of mostly satellite-based aerosol DRE/DRF values. Comparisons to previous studies reveal that most have significantly underestimated the aerosol DRE uncertainty. Past estimates of the aerosol DRF uncertainty are smaller (on average) than our optimistic observational estimates, including the aerosol DRF uncertainty given in the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5).

Tyler James Thorsen↗

NASA NTRSDate not supplied

Uncertainty in Observational Estimates of the Aerosol Direct Radiative Effect and Forcing

Tyler James Thorsen↗

DOE OSTI2021

An uncertainty quantification method relevant to material test reactors

Within material test reactor calculations, energy dependent flux and reaction rate uncertainties are typically not quantified when performing as-run analyses to determine the neutron field experienced by the experiment. When high fidelity Monte-Carlo codes are used in such analyses, straight forward methods to calculate output uncertainties are not available, instead expert opinion is used to postulate computational uncertainties. New methods to propagate uncertainties through these high fidelity simulations are available when sufficient computational power is available. A tool is developed here for sampling any part of an MCNP input from random distributions to determine output uncertainties based on those inputs. Another tool is developed to sample nuclear data cross-section in ACE format using multi-group nuclear data covariances. The Total Monte-Carlo Method and Gesellschaft für Anlagen-und Reaktorsicherheit method (GRS) are implemented and compared to one another as well as MCNP sensitivity and uncertainty calculations. The methods were applied to the Godiva critical sphere k-eigenvalue, the UAM pin-cell benchmark energy dependent flux and reaction rates, and the Advanced Test Reactor energy dependent flux within an experimental location. Furthermore, the two methods agree well, with GRS allowing for an order of magnitude speedup for reaction rate uncertainty calculations and several orders of magnitude for eigenvalue uncertainty calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Uncertainty quantification (UQ) in nuclear reactors for transients is directly linked with safety assessment through the cross-sections uncertainties, provided as a covariance matrix, which are propagated through the reactor system to output of interest pertaining to reactor safety, such as peak temperatures in fuel/clad/coolant. Using a two-step approach, uncertainties are first quantified and propagated from basic input variables (such as reaction cross-sections) to intermediate quantities (such as reactivity feedback coefficients) through lattice level calculations. Uncertainties of intermediate quantities (from the first step) are then propagated through the system transient calculations, in the second step, to obtain uncertainties on reactor safety output parameters of interest. The scope of this work consists of Uncertainty Quantification & Propagation of nuclear data uncertainties that are highly correlated through unprotected transient overpower and unprotected loss of flow to assess their impact on core safety parameters. This two-step approach in the presence of covariance renders the sensitivity analysis very challenging. In fact, usually the sensitivity analysis is restricted to each step, which limits its application since the sensitivities between the system output quantities and the basic input variables are difficult to obtain. Here, in this work, we address this issue by proposing a simple, general methodology to combine the sensitivity indices obtained in each step by assuming the model behavior being linear. For the first step Generalized Perturbation theory based indices are used while in the second step the recently studied Johnson indices. The uncertainty quantification and sensitivity methodologies discussed here are demonstrated on a generic LFR design which is based on the 500 MWth demonstration Lead-cooled fast reactor (DLFR) using oxide fuel, developed by Westinghouse Electric Company (WEC).

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