Uncertainty Analysis Methodology Research Articles

ACE Surrogate Model–Based Uncertainty and Sensitivity Analysis Methods for Severe Accident Codes

This paper explores the alternating conditional expectation (ACE) algorithm-based surrogate model to advance the state-of-practice in uncertainty and sensitivity analysis methodologies for severe accident codes. For engineering purposes, the ACE algorithm has been used as an alternative means to find the optimal functional forms of the multiple input variables and response variables of interest. Analysis results here demonstrate that compared with the reference cases the proposed surrogate model provides much higher performance in terms of the coefficient of determination (R2) and normalized root mean square error (NRMSE), thus giving more robust insights into the relationship and correlation between the input parameters and figures of merit (FOMs) of interest. Relevant results and insights are summarized in terms of points of interest.

Optimizing Space Telescopes’ Thermal Performance through Uncertainty Analysis: Identification of Critical Parameters and Shaping Test Strategy Development

The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope developed by the Instituto de Astrofísica de Canarias (IAC), this paper reports an uncertainty analysis on a thermal model aimed at improving cost savings in the future testing phases. The primary objective was to identify critical parameters impacting thermal performance and reduce overdesign. Employing the Statistical Error Analysis (SEA) method across several operational scenarios, the research identifies key factors, including the Earth’s infrared temperature and albedo, and the spacecraft’s attitude and environmental conditions, as the variables with major influences on the system’s thermal performance. Ultimately, the findings suggest that uncertainty-based analysis is a potent tool for guiding thermal control system design in space platforms, promoting efficiency and reliability. This methodology not only provides a framework for optimizing thermal design and testing in space missions but also ensures that instruments like the VINIS telescope maintain optimal operating temperatures in diverse space environments, thereby increasing mission robustness and enabling precise resource allocation.

Proposal of uncertainty analysis methodology for L1PRA using Markov state-transition model

ABSTRACT Following the severe accident at the Fukushima Daiichi Nuclear Power Plant in 2011, the revised nuclear safety regulation in Japan requires continuous safety improvement and states that PRA methods that reflect the latest knowledge should be used in activities related to continuous safety improvement. In this context, the construction of PRA models for the digital RPS (DRPS) has been addressed as an important issue within the Working Group on Risk Assessment (WGRISK) of the OECD/NEA, and several studies have been conducted. And there are challenges in aligning them with the conventional probabilistic risk assessment methodology. In a previous study, the authors developed a simultaneous differential equation describing the relationship between state transitions and state probabilities based on Markov state transition diagrams to calculate them numerically. However, the analytical method for uncertainty analysis commonly used in conventional PRA evaluations is not explicitly presented. The purpose of this study is to provide a methodology for more accurate evaluation of core damage frequency in nuclear power plants equipped with digital RPS, taking into account the uncertainties, and to contribute to the continuous improvement of safety.

Intrusive and non-intrusive uncertainty quantification methodologies for pyrolysis modeling

In this work we discuss and compare multiple uncertainty analysis methodologies for pyrolysis modeling. Using Arrhenius equation as the pyrolysis model and kinetic parameters for PMMA material as uncertain variables, different approaches for stochastic uncertainty quantification and their computational effort are analyzed. First non-intrusive (NI) methods are compared through convergence analysis where the deterministic model can be used as is to ascertain the statistical moments, followed by an intrusive polynomial chaos (IPC) approach which requires reformulation of the deterministic model, but solution of the model only once. The applied NI methods include Monte Carlo based simulations, regression and projection based non-intrusive polynomial chaos (NIPC) methods. The uncertainty analysis and convergence comparison is performed for three materials including the effects of overlapping reactions and dependent parameters. In general for different uncertain parameters and materials, IPC gave accurate statistical information in a single model run compared to NIPC projection requiring runs ranging from tens to hundreds and NIPC regression requiring runs in range of thousands to generate converged statistics. However, in case of only a few uncertain parameters, presence of non-polynomial functions in the model and small-scale simulations, projection based NIPC outshines IPC in terms of simplicity and applicability.

Mathematical Toolbox for Joint Structural and Electrochemical Analysis of Lithium-Ion Battery Cells

Increasing interest in the automotive field for electrification has led to an increased demand for lithium-ion batteries (LIB). LIB remain the most popular and widely applied energy storage technology. The microstructure of the positive electrode of LIBs governs its electrochemical performance. For this reason, it is quite imperative to determine an optimal electrode microstructure design to achieve the best possible energy properties. Mechanistic simulation models can be a powerful tool in analyzing the effect of microstructural parameters on the battery cell performance. Not only do they negate costs for necessary materials, but they also allow analysis and optimization while avoiding time-consuming experiments.Doyle-Fuller-Newman (DFN) models, being pseudo-2-dimensional (P2D), are computationally less expensive than their higher dimension counterparts; however, they often apply the Bruggeman relation to compute effective parameters such as the electrode conductivity of the electrode. Despite its simple implementation (i. e. a correction factor to reflect effective parameters), the Bruggeman relation delivers inaccurate results. [1, 2]In this work [1], we coupled a DFN model with the structure surrogate model from [2], excluding the Bruggeman relation. The model predicts the effective transport parameters and thus the battery performance more accurately. With this coupled model, we investigated the effect of the cathode mass loading and density on the achievable volumetric discharge energy density. [1] Furthermore, we developed a numerical optimization tool, which can predict the optimal structural parameters for maximum energy density. Our findings suggested different optimal designs for different current densities; whereas higher mass loading was preferred at lower C-rates, it was less favorable at higher C-rates. The concentration profiles of lithium-ions across the cathode thickness at the end of discharge suggest transport limitations to be the main reason behind this. As a further step, we carried out an uncertainty analysis to determine the robustness of the optimal designs to structural fluctuations with respect to their energy density.Our findings provide a deeper understanding into the influence of structural parameters on the discharge performance of lithium-ion battery cells, using only simulative methods. Additionally, our methodology of numerical optimization and subsequent uncertainty analysis allows for a quick and simple method for determining optimal structural parameters and their robustness against fluctuations. This approach can assist to produce battery cell electrodes with an optimized design in industry as well as help simulative and experimental researchers to further investigate optimal electrode design.[1] Karaki et al., Energy Technology 2022 [2] Laue et al., E. Acta 2019 Figure 1

Determination of the coefficient of thermal expansion of ultra-low-expansion glass using anultrasonic immersion testing method.

The coefficient of thermal expansion (CTE) of ultra-low-expansion (ULE) glass is critical to the development of precision optical systems. Herein, an ultrasonic immersion pulse-reflection method is proposed to characterize the CTE of ULE glass. The ultrasonic longitudinal wave velocity of ULE-glass samples with significantly different CTE values was measured using a correlation algorithm combined with moving-average filtering, which can achieve 0.2m/s precision with a contribution to the ultrasonic CTE measurement uncertainty of 0.47ppb/°C. Furthermore, the established ultrasonic CTE measurement model predicted the 5°C-35°C mean CTE with a root-mean-square error of 0.9ppb/°C. Notably, a complete uncertainty analysis methodology was established in this paper, which can provide directional guidance for the subsequent development of higher-performance measurement devices and the improvement of relevant signal processing procedures.

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.

Effects of fuel relocation on fuel performance and evaluation of safety margin to 10CFR50.46c ECCS acceptance criteria in APR1400 plant

Characteristics of fuel relocation and its effect on safety margin in a loss-of-coolant accident (LOCA) safety analysis have been analyzed. Packing fraction model developed by Quantum Technology was validated and slight modification was conducted based on the formation of high burnup structure model. FAMILY computer code that is an integrated code with FRAPTRAN and MARS-KS is used for the evaluation. The peak cladding temperature (PCT) and peak local oxidation (PLO) of the APR1400 nuclear power plant with the PLUS7 fuel during LOCA were evaluated by a best-estimate plus uncertainty analysis methodology up to 60 MWd/kgU. Fuel relocation resulted in an increase in 95 %/95 % probability/confidence level of the reflood PCT (95/95 reflood PCT) and the PLO (95/95 PLO) until the fuel burnup of ∼ 50 MWd/kgU. Maximum increase of 95/95 reflood PCT was about 130 K at 30 MWd/kgU burnup. This results in the minimum safety margin of the reflood PCT at that burnup with respect to the U.S. NRC proposed 10CFR50.46c PCT limit. Maximum increase of 95/95 PLO was 3.9 percent point at 30 MWd/kgU. However, minimum safety margin was observed at different burnups such as 40 MWd/kgU and 60 MWD/kgU. Regarding the cladding burst characteristics, fuel relocation may lead to an increase in the burst temperature and burst strain of the cladding, and it also induces an earlier failure of cladding.

Uncertainty analyses of neutron noise simulations in a Zero-Power reactor

A comprehensive uncertainty analysis methodology has been established for the modeling of stationary neutron flux oscillations induced by fuel rods vibration in a zero-power reactor. The methodology includes uncertainty propagation and sensitivity analysis. The target event is based on an actual experimental campaign at the CROCUS zero-power reactor and corresponds to the simultaneous oscillation of 18 metallic uranium fuel rods in the periphery of the core. Both the uncertainty propagation and the sensitivity analysis commonly use a large part of the entire analysis process, from the selection of uncertain parameters to the actual code simulations. Applying a random sampling-based approach, the input parameters are sampled N times from their distribution information and used as inputs for N noise simulations using CORE SIM +. The quantity of interest (QoI) is the amplitude of the Auto-Power Spectral Density at various detector locations, which is normalized by the amplitude of the Cross-Power Spectral Density of the reference detector. Their uncertainties are determined following the 4th order Wilks’ formula for two-sided limits. Through the determination of correlations among QoI at the installed detector locations, it is demonstrated that the neutron noise near the area of oscillating fuel rods (noise source) have different behavior compared to the neutron noise further away from the noise source. The following sensitivity analyses are carried out using multiple correlation coefficients within grouped parameters. As expected from the QoI correlations, the QoIs at two different locations (near and far from the noise source) are influenced by different input parameters. Near the noise source, the QoI uncertainty is driven by the uncertainties in the position of the noise source, while the uncertainties in the nuclear data for U-235 and U-238 are the leading contributors further away from the source. This paper provides general information on how to perform the uncertainty analyses for neutron noise simulations, as well as quantitative estimates of the computational uncertainty required for the validation of the computer programs under development for the simulation of neutron noise.

Cold Leg LBLOCA uncertainty analysis using TRACE/DAKOTA coupling

Safety analyses for nuclear power plants were carried out in the past using a conservative approach. With the increase of the phenomenological knowledge, through experimental data, and computational power, it became possible to adopt best estimate thermal- hydraulic system codes to perform deterministic safety analyses. However, some uncertainties are still present in the models, correlations, initial and boundary conditions, etc. Therefore, it is fundamental to quantify the uncertainty of calculation. This approach is called “Best Estimate Plus Uncertainty” (BEPU). Among the available uncertainty analysis methodologies, the probabilistic method to propagate input uncertainty is widely adopted. In the present study, an uncertainty analysis of a cold leg large break loss of coolant accident in a generic PWR-900 MWe has been developed and it has been carried out coupling the best estimate thermal- hydraulic system code TRACE and the uncertainty quantification tool DAKOTA in the SNAP environment/architecture.

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

Gradient-Informed Design Optimization of Select Nuclear Systems

This paper presents a gradient-informed design optimization of nuclear reactor core components based on neutronics objectives with both continuous and discrete materials. The main argument in favor of using gradient-informed design optimization is that it scales well with increasing dimensionality of the design space. First, a challenge problem with 121 free parameters is solved with a gradient-informed method and then with a genetic algorithm. Then, a challenge problem to optimize the flux profile of a simplified assembly with eight axial zones is solved. Both challenge problems are solved using directly calculated derivatives from Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) in the SCALE package. This work also demonstrates how a discrete optimization problem—selection of materials for 121 voxels—can be lifted into a continuous problem with mixed materials. In the continuous space, adjoint-based gradients are well-defined, and gradient descent is applicable. Then, a forcing function is introduced that with the selection of an appropriately sized hyperparameter can be used to guide the optimized continuous solution back into a discrete solution. This paper presents an account of the challenges that were faced when applying a gradient-informed optimization algorithm using a Monte Carlo calculation to estimate the gradient information and compares a gradient descent optimization method to a genetic algorithm optimization of the same geometry. Overall, this work demonstrates the potential use of adjoint-based gradient calculations in design optimization of nuclear systems.

Methodology for a combined uncertainty analysis and data quality rating of existing graphite datasets in context of battery LCAs

Battery raw materials have been identified as one of the main contributors to the overall environmental impact of batteries, however the primary data related to their production remains scarce and is subjected to high uncertainty. In particular, the commercial life cycle inventory datasets related to refining and processing of graphite as an anode material for lithium ion batteries are subjected to in-transparency and are difficult to be traced back. The existing Life Cycle Assessment (LCA) studies on traction batteries make use of these old and partly incomplete graphite datasets without consideration of underlying uncertainties and their effects on the overall environmental impacts. This paper addresses this issue by providing a comprehensive overview of the conventional graphite production processes, a summary of existing graphite datasets, along with their sources and presenting the underlying reasons for the major deviations. Subsequently, the quality of nine different Life Cycle Inventory (LCI) graphite datasets is discussed based on evaluation performed according to EPA data quality guidelines, whereby data gaps are identified and datasets are ranked according to an established data quality rating (DQR) methodology. Furthermore, the paper provides a common base for LCA results of existing graphite production datasets which were remodeled and assessed in Brightway2 (BW2) using background data from a ecoinvent. The results of DQR and LCA reveal major discrepancies in the datasets for the production of battery grade graphite. A Monte Carlo (MC) analysis is also provided to identify uncertainties stemming from technospheric flows, generating result bandwidths. A subsequent case study on state of the art lithium ion battery cell is also presented revealing that the choice of a certain graphite dataset could have a significant impact on the overall LCA results of a battery. The study highlights the need of the graphite datasets to receive more attention in the future.

Weierstrass function methodology for uncertainty analysis of random media criticality with spectrum range control

Randomized Weierstrass function (RWF) has been under development for evaluating the uncertainty of random media criticality due to the material mixture in disorder. In this work, the modelling capability of RWF is refined so that the spectrum range can be controlled by specifying its lower and upper ends of the frequency domain variable. As a result, it becomes possible to make fair criticality comparison among replicas of random media under inverse power law power spectra. Technically, the infinite sum of trigonometric terms in RWF is formally extended to cover the arbitrarily low frequency domain and then truncated to finite terms for the sole purpose of spectrum range control. This means that the refined RWF is free of the issue of convergence towards a reference fractal characteristic of Weierstrass function and thus termed Incomplete Randomized Weierstrass function (IRWF). As a demonstration, a three-dimensional version of IRWF is applied to the mixture of three fuels with different burnups in a sufficiently water-moderated environment. Monte Carlo criticality calculations are carried out to evaluate the uncertainty of neutron effective multiplication factor (keff) due to the indeterminacy of mixture formation.

Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I

In recent years, large efforts have been devoted to Light Water Reactor (LWR) Uncertainty Quantification (UQ). In 2006, the LWR Uncertainty Analysis in Modeling (UAM) benchmark was launched with an aim to investigate the uncertainty propagation in all modeling stages of the LWRs and guide uncertainty and sensitivity analysis methodology development. This article summarizes the benchmark activities for the standalone neutronics phase (Phase I), which includes three main exercises: Exercise I-1: “Cell Physics,” Exercise I-2: “Lattice Physics,” and Exercise I-3: “Core Physics.” A comparative analysis of the Phase I results is performed in this article for all the considered LWRs types: Three Mile Island – 1 Pressurized Water Reactor (PWR), Peach Bottom – 2 Boiling Water Reactor (BWR), Kozloduy – 6 Water - Water Energetic Reactor (VVER) and a Generation-III reactor. It was found, for all major exercises, that the predicted uncertainty of the system eigenvalue is highly dependent on the choice of the covariance libraries used in the UQ process and is less sensitive to the solution method, nuclear data library and UQ method. For all four reactor types, the observed relative standard deviation across all exercises is approximately 0.5% for the UO2 fuel. In the pin cell and lattice calculations with MOX fuel this uncertainty increases to 1%. The main reason is the larger Pu-239 nu-bar uncertainty compared to the U-235 nu-bar. The largest contributors to the eigenvalue uncertainties are the U-235 nu-bar and the U-238 capture in the UO2 fuel and the Pu-239 nu-bar in the MOX fuel. In the assembly lattice exercises, higher uncertainties are predicted for the fast group than the thermal group constants with differences up to one order of magnitude. This is attributed to the larger uncertainties of most cross-sections at high energies. The obtained correlation matrices share some common major trends but also exhibit strong differences in case by case comparisons indicating an impact of the selected neutronics modeling and nuclear data library. In the core exercises, the predicted relative standard deviation of the radial and axial power, for most of the cores, is below 10%. An exception is the radial power profile of the Generation-III core, when a mixture of UOX/MOX assemblies is considered. Finally, it is important to note that the bias in most of the studies is significant and up to the same order of the estimated uncertainty. This indicates a need for better quantification of the bias/variance through more code to code and code to experiments comparisons.

Application of a BEPU-based code assessment to the ATLAS upper head SB-LOCA test

The prevailing state of knowledge of two-phase flow in complex systems, as in the nuclear field, usually leads a certain degree of ad hoc calibration of computational models and the consequent inevitable subjectivity in analyses and assessments. The UPC-ANT Uncertainty Analysis methodology for code assessment attempts to maintain scientifically desired features of models and analyses, by crediting BEPU analysis and centering the assessment criteria on T-H phenomenology rather than on safety criteria, as it is the usual practice. An SB-LOCA in the upper head is utilized as a case study, and global, chronological, FOMs and phenomenological analyses are carried out and illustrated briefly. Overall, it was concluded that the developed RELAP5 model reproduces all the relevant T-H phenomena to the investigated scenario, either partially or totally, and the BEPU methodology is generally adequate to perturb the underlying T-H phenomena.

Global uncertainty and sensitivity analysis of MELCOR and TRACE critical flow models against MARVIKEN tests

Uncertainties of numerical predictions play important role in assessment of safety margins in nuclear reactors. Critical Flow models are used in the different types of Nuclear Safety Analysis but these are especially important in Loss of Coolant Accidents (LOCA) analyses.In this paper validation of the input decks is performed of all of the Marviken critical flow tests. Next uncertainties of the prediction of these models implemented into TRACE and MELCOR computer codes are analysed and discussed. Results of the global uncertainty analysis are shown in terms of trends of uncertainty values with respect to the pressure, nozzle length, water level, discharge coefficient and hydraulic diameter.Application of the global sensitivity analysis methods allowed ranking of the sources of uncertainties over large spectrum of experimental conditions. This was achieved thanks to usage of Bayesian Integrated Uncertainty and Sensitivity Analysis (BIGUSA) methodology. The analysis revealed that the uncertainties of critical mass flow predictions are predominantly resulting from uncertainties of the hydraulic diameter in both codes. In the TRACE code the hydraulic diameter, nozzle length and discharge coefficient were dominating factors. For MELCOR dominating factors were hydraulic diameter, discharge coefficient and pressure.

A novel method for fault tree uncertainty analysis using error propagation methods

AbstractFault tree analysis (FTA) is a widely used methodology in the process industries. FTA is used for the development of failure mechanisms, computation of failure frequencies and the determination of the probability of failure on demand of safety systems. Much of the data used in a FTA study are uncertain. For example, the failure rate of a pump is often not known with great precision. Likewise the failure rates of instrumentation are often known only within some defined limits. The common practice, used by analysts in the quantification of a fault tree, is to use the most likely or best guess as to the needed failure rate data. The use of best guess values as data inputs to the quantification of a fault tree creates uncertainty in the computed results. This paper presents a general methodology for the determination of the impact of uncertainty on the results of a fault tree study. The general methodology is based on the mathematics of propagation of error and variance contribution analysis. For a fault tree that contains 10 event minimal cut sets, the methodology will compute the variance in the probability (or frequency) of the top event with a maximum error of 5%. For five event minimal cut sets the error in variance of the top event probability (or frequency) will be no more than 2%. An example is presented to illustrate the application of the fault tree uncertainty analysis methodology to a real world problem.

Application of Expanded Event Trees combined with uncertainty analysis methodologies

During the last decades, the safety analysis of nuclear power plants, have been shifting from conservative models and hypotheses to Best Estimate. Within this trend, the different safety analyses can be categorized depending on their associated conservatisms. The approach that includes Expanded Event Trees together with Best Estimate model and conditions plus uncertainty avoiding conservatisms in system availability can be referred as Extended BEPU (EBEPU). In this sense, an Extended BEPU safety analysis relies on Expanded Event Trees and Best Estimate models to study an accidental sequence.In the present paper, an EBEPU study is performed, using a loss of coolant accident in a pressurized water reactor modeled with the TRACE code. A 5% core power uprate is used as an example of safety margin EBEPU analysis. The paper presents parametric and non-parametric uncertainty approaches. Observing the results from all event tree sequences, it is seen that the branches that contribute the most to the Core Damage frequency are successful branches, with all safety systems available, and some Core Damage branches have a possibility of success.

Assessment of Critical Experiment Benchmark Applicability to a Large-Capacity HALEU Transportation Package Concept

This work presents an assessment of the applicability of existing benchmark critical experiments to the criticality safety code validation for a large-capacity high-assay low-enriched uranium (HALEU) transportation package concept. Numerous next-generation nuclear reactor designs require HALEU fuel, which is characterized by an enrichment between 5 and 20 wt% 235U. The U.S. Department of Energy (DOE) has proposed to recover and downblend highly enriched uranium from DOE-owned used nuclear fuel to accelerate the demonstration of commercially viable microreactor technologies. One element of the infrastructure needed to demonstrate HALEU-fueled reactors is the ability to safely transport enriched product to be used for fuel fabrication. There is uncertainty as to whether existing critical benchmark experiment data are sufficient to support criticality safety code validation for HALEU transportation applications. The anticipated chemical form of the HALEU in the proposed transportation concept is UO2 with 20 wt% 235U/U. The concept uses a combination of an existing transportation packaging design and a novel basket design, including borated aluminum flux traps. The basket provides space for 18 reusable, stainless steel canisters that contain the HALEU. In 10 CFR 71, normal conditions of transport (NCTs) and hypothetical accident conditions (HACs) are defined for fissile material transportation packages. NCT and HAC KENO-VI models of the transportation package were developed using the Standardized Computer Analyses for Licensing Evaluation (SCALE) 6.2.3 computer code package, and optimum moderation conditions were determined using the SCALE SAMPLER sequence. The SCALE Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) sequences were then used to compare the neutronic characteristics of 1584 International Criticality Safety Benchmark Evaluation Project benchmark critical experiments with the NCT and HAC HALEU transportation models. The TSUNAMI integral correlation coefficient ck was the criterion used to rank neutronic similarity. Thirty-four experiments were identified as similar (ck ≥ 0.9) to the NCT model, and 55 experiments were identified as similar to the HAC model. Hundreds of experiments were also identified as at least marginally similar (ck ≥ 0.8) to both models. The results indicate that additional critical experiments are unlikely to be needed to support HALEU transportation criticality safety analyses for package concepts similar to the concept package analyzed.