Uncertainty Analysis Techniques Research Articles

Application of uncertainty and sensitivity analysis in dose assessment during a postulated LBLOCA for VVER-1000 nuclear reactor

A key element in the process of accident analysis for the event of a severe nuclear emergency such as the large break loss of coolant accident (LBLOCA) is to properly quantify safety margins using reliable tools. Assessing the consequences of radioactive material release and the equivalant dose after the LBLOCA can provide clarity about the accident. Reliable safety analysis methods for nuclear power plants (NPPs) can be used to obtain relatively realistic outcomes considering the complex uncertainties of NPP events. In this study, an appropriate technique for sensitivity and uncertainty analysis is employed to develop a methodology for performing dose assessment within a postulated LBLOCA including uncertainties. Considering the approved reliability over the years and safety aspects, VVER reactor is among the most widely used nuclear reactors, which is selected as the case study in this article. The main problem that the study aims to solve is to illustrate whether the inordinate deviation of the input parameters from their reference values lead to violation of the allowed TEDE value by conducting uncertainty analysis. For performing calculations of total effective dose equivalent (TEDE), the environmental contamination with radioactive materials has been simulated using HotSpot code and the obtained results were compared with the solutions of JRODOS program. To obtain the most influential parameters on the obtained TEDE values in different distances from the NPP, an appropriate sensitivity analysis has been carried out, following which the Wilks’ statistical method is used to generate 59 data samples with uncertainty propagation and 95 % confidence. The resulting uncertainty bands demonstrate that the maximum TEDE values of calculated data lie within about 7 to 15 mSv in the postulated LBLOCA scenario, which are below the threshold limit of 50 mSv recommended by International Atomic Energy Agency.

Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models

ABSTRACT The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West, Florida (United States). The model types examined were a commonly used quasi-empirical linear regression model and a decision tree-based Categorical Boosting (CatBoost) machine learning (ML) model. Image types examined were (four) cloud-free Sentinel-2 images and a maximum blue band (Band 2) value image composite of the four Sentinel-2 images. Initial models fitted were based on band reflectances alone. Geographical adaptivity was added by including UTM coordinates and refitting the models. Major findings were: 1) The ML/CatBoost models provided substantially better depth estimates than the quasi-empirical models. 2) The geographically adaptive models outperformed the non-geographically adaptive models. 3) The ML/CatBoost models that included non-visible spectral bands including infra-red improved SDB accuracy compared to ML/CatBoost and quasi-empirical models based only on visible spectral bands. 4) Accuracies from ML/CatBoost models were comparable across all individual images and the composite suggesting that CatBoost models eliminate or at least minimize the need to find “the best” cloud-free image nor is it necessary to create a composite image. 5) Localized SDB inaccuracy was spatially random. 6) Significant spatial hotspots where SDB accuracy was consistently higher or lower across all images and models were present. Results suggest that image selection is less important for global and local SDB accuracy than using ML models that detect hidden interactions and non-linear relationships among pixel reflectance and geographic location. The spatially random local deviation from global accuracy suggests a weak ability to infer local accuracy from neighboring accuracies. This lack of spatial autocorrelation among errors is potentially problematic for the use of SDB maps for navigation since error at any location is generally inferred from known uncertainties at neighboring locations. Rigorous and robust uncertainty analysis is necessary in any effort to improve SDB, and the uncertainty analysis techniques employed that characterize SDB uncertainty in both statistical and geographical space could be an important part of quality assurance and continuous improvement.

A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+

Abstract. Parameter sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of the estimation of parameters and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate an uncertainty and sensitivity analysis technique for the holistic Soil and Water Assessment Tool (SWAT+) model coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. The main calculated groundwater inflows and outflows include boundary exchange, pumping, saturation excess flow, groundwater–surface water exchange, recharge, groundwater–lake exchange and tile drainage outflow. We present the method for four watersheds located in different areas of the United States for 16 years (2000–2015), emphasizing regions of extensive tile drainage (Winnebago River, Minnesota, Iowa), intensive surface–groundwater interactions (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas) and mountain snowmelt (Arkansas Headwaters, Colorado). The main parameters of the coupled SWAT+gwflow model are estimated utilizing the parameter estimation software PEST. The monthly streamflow of holistic SWAT+gwflow is evaluated based on the Nash–Sutcliffe efficiency index (NSE), percentage bias (PBIAS), determination coefficient (R2) and Kling–Gupta efficiency coefficient (KGE), whereas groundwater head is evaluated using mean absolute error (MAE). The Morris method is employed to identify the key parameters influencing hydrological fluxes. Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for uncertainty quantification (UQ) and parameter estimation (PE) and to decrease the computational cost owing to the large number of parameters. Depending on the watershed, key identified selected parameters include aquifer specific yield, aquifer hydraulic conductivity, recharge delay, streambed thickness, streambed hydraulic conductivity, area of groundwater inflow to tile, depth of tiles below ground surface, hydraulic conductivity of the drain perimeter, river depth (for groundwater flow processes), runoff curve number (for surface runoff processes), plant uptake compensation factor, soil evaporation compensation factor (for potential and actual evapotranspiration processes), soil available water capacity and percolation coefficient (for soil water processes). The presence of gwflow parameters permits the recognition of all key parameters in the surface and/or subsurface flow processes, with results substantially differing if the base SWAT+ models are utilized.

Sustainable Retrofit of Existing Buildings: Impact Assessment of Residual Fluorocarbons through Uncertainty and Sensitivity Analyses

Fluorocarbons are an important category of greenhouse gas emissions, and currently, their use is prohibited due to their significant contribution to the global ozone depletion potential (ODP). During this century, they will continue to emit greenhouse gases into the environment since they are present in the thermal insulation foam and HVAC systems in existing buildings; however, proper disposal of these banks of CFCs/HFCs from existing buildings can limit their effects on the environment. However, there are no studies that have investigated quantifying the achievable environmental savings in this case. In this study, a comparative life cycle assessment (LCA) is conducted to evaluate, for the first time in the literature, the environmental savings achievable through the removal and disposal of CFC/HFC banks from buildings including damage-related emissions. To cope with the scarcity of data, uncertainty and sensitivity analysis techniques are applied. The results show that, for the selected archetype building, the largest annual emissions of CFCs/HFCs come from the external thermal insulation of the envelope. The removal of this material can lead to an additional significant reduction in the GWP (up to 569 kgCO2eq/m2) and the ODP (up to 117 × 10−3 kgCFC-11eq/m2), i.e., higher than that achievable by reducing energy consumption through energy retrofit measures (276 and 0, respectively). Thus, CFC/HFC banks should not be neglected in LCA studies of existing buildings due to their possible significant impact on a building’s ecoprofile.

Decision Support System for Sustainable Exploitation of the Eocene Aquifer in the West Bank, Palestine

Groundwater is a crucial resource for water supply and irrigation in many parts of the world, especially in the Middle East. The Eocene aquifer, located in the northern part of the West Bank, Palestine, is threatened by unsustainable groundwater abstractions and on-ground pollution. Analysis and management of this aquifer are challenging because of limited data availability. This research contributes to the long-term sustainability of the aquifer by model-based design of future abstraction strategies considered within an uncertainty analysis framework. The methodology employed started with development of a single-layer steady-state MODFLOW groundwater model of the area, followed by uncertainty analysis of model parameters using Monte Carlo simulations. The same model was afterwards coupled with a Successive Linear Programming (SLP) optimization algorithm, implemented in the Groundwater Management tool (GWM) of the United States Geological Survey (USGS). The purpose of optimization was deriving five optimal abstraction strategies, each aiming to maximize groundwater abstraction, subject to different constraints regarding groundwater depletion. Given the uncertainty of model parameters, the sensitivity and reliability of these optimal strategies were then tested. Sensitivity was checked for two optimal strategies by performing re-optimization with different values of uncertain model parameters (one at a time). Reliability of the five strategies was tested by analyzing the extent of constraints’ violation for each strategy when varying the uncertain parameters using Monte Carlo simulations. Finally, the model was used for determining capture zones of wells for the five optimal abstraction strategies, land-use in these capture zones, and the associated estimates of on-ground nitrogen loading. The developed strategies were then deployed in a web-based decision support application (named Groundwater Decision Support System—GDSS), together with other relevant information. Users can analyze results of different optimal strategies in terms of groundwater level variations and total water balance results, and test consequences of uncertain parameters. Capture zones of wells for different abstraction strategies, together with land-use and on-ground nitrogen loading in these capture zones, are also presented. Results show that critical uncertain parameters are recharge, hydraulic conductivity, and conductance at key boundary condition locations. Optimal abstraction strategies results indicate that an increase in total abstractions could be between 5% and 20% from the current level (estimated at about 56 × 106 m3/year, which is about 74% of estimated annual recharge). The uncertain parameters, however, are impacting the sensitivity and the reliability of the optimal strategies to variable degrees. Recharge and hydraulic conductivity are the most critical uncertain parameters regarding sensitivity of the optimal strategies, while reliability is also impacted by the level of abstraction proposed in a given strategy (number, locations, and abstraction rates of new wells). The main novelty and contribution of this research is in combining modelling, uncertainty analysis, and optimization techniques in a comprehensive decision support system for the area of the Eocene aquifer, characterized with limited data availability.

Groundwater Management and Allocation Models: A Review

Effective groundwater management and allocation are essential from economic and social points of view due to increasing high-quality water demands. This study presents a review and bibliometric analysis of the popular techniques in groundwater management and allocation models, which have not yet been captured in the literature, as our knowledge allows. To this extent, the literature on this state-of-the-art is categorized based on four primary sectors intervening in efficient groundwater management. The first sector discusses the simulation and surrogate models as the central groundwater predictive models, wherein quantitative and qualitative groundwater models are scrutinized. The second section is dedicated to applying different classic and smart optimization models, followed by a summary of state-of-the-art works on applying accurate and heuristic optimization models in groundwater management. Third, uncertainty analysis techniques in conjunction with groundwater modeling are studied as analytical tools, approximation methods, and simulation methods to identify the most exciting subject fields. The fourth section reviews decision-making models coupled with groundwater models as multi-criteria decision-making, social choice, and game-theory models. Finally, a summary of this review and goals for future studies are presented. Additionally, several new ideas are recognized, advising scholars to find critical gaps in the field.

A Comparative Study of Meta-Modeling for Response Estimation of Stochastic Nonlinear MDOF Systems Using MIMO-NARX Models

Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative for a large number of MCS. The previous study has demonstrated that the multi-input multi-output nonlinear autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representations of deterministic nonlinear multi-degree-of-freedom (MDOF) structural dynamic systems. Model order reduction (MOR) is executed to eliminate insignificant modes to reduce the computational burden due to too many degrees of freedom. In this study, the MIMO-NARX strategy is integrated with different meta-modeling techniques for uncertainty analysis. Different meta-models including Kriging, polynomial chaos expansion (PCE), and arbitrary polynomial chaos (APC) are used to surrogate the NARX coefficients for system uncertainties. A nine-DOF structure is used as an MDOF dynamic system to evaluate different meta-models for the MIMO-NARX. Good fitness of statistical responses is observed between the MCS results of the original system and all surrogated MIMO-NARX predictions. It is demonstrated that the APC-NARX model with the advantage of being data-driven is the most efficient and accurate tool for uncertainty quantification of nonlinear structural dynamics.

Uncertainty and sensitivity analysis applied on commercial tariff with off-peak tariff rider: a case study

Commercial Tariff with Off-Peak Tariff Rider (C1 OPTR) is one type of time-based electricity tariff. The C1 OPTR charges electricity consumers with different electricity rates instead of a flat rate tariff. This paper investigates the C1 OPTR tariff adopted recently by Universiti Teknologi MARA Cawangan Pulau Pinang (UiTMCPP) from its previous flat rate tariff. The investigation involves applying the uncertainty and sensitivity analysis to the average load factor (ALF) model of the UiTMCPP. The ALF model consists of two major factors, namely kilowatt-hour (kWh) and maximum demand (kW). The analysis aims to identify the most contributing factor between the kWh and kW to the uncertainty of the ALF in a systematic way using Monte Carlo simulation. The factor identified is important for improvement by UiTMCPP to ensure that the suitable target ALF can be easily achieved. Based on Sobol uncertainty and sensitivity analysis technique, 60,000 samples for the respective kWh and kW have been generated and executed to produce the output of the ALF model. The result of the uncertainty analysis shows that the ALF output is uncertain between 0.195 and 0.343. Furthermore, the applied sensitivity analysis discovers that the kW is the most contributing factor to the ALF output uncertainty, with the sensitivity index indicating 0.8853.

Further development of the RDRA method for the optimal acquisition of data in process integration retrofit projects

Process Integration techniques are powerful in analysing the energy usage of industrial plants and designing retrofit solutions for increasing their energy efficiency. However they are far from constituting the industrial practice. One primary cause for this is the severe time consumption they entail, especially in acquiring the necessary process data. A recently developed method, the Required Data Reduction Analysis (RDRA), aims at reducing this time consumption. It makes use of uncertainty and sensitivity analysis techniques, together with optimisation tools, in order to systematically identify: (i) a reduced number of process parameters, whose information need to be acquired with high detail, and (ii) the maximum acceptable uncertainty of each of them.The paper presents recent additions to the method, expanding its capabilities and addressing two open questions highlighted in previous work. In particular: (i) three sensitivity analysis techniques are compared, based on computational effort and precision, (ii) a novel algorithm for minimising the number of parameters needing detailed data acquisition is proposed and (iii) the impact of the uncertainties characterisation on the outcome of the analysis is assessed.The results of the analysis considering four different case studies provided evidence that the Standardised Regression Coefficient method for sensitivity analysis should be preferred in the Required Data Reduction Analysis, and that the uncertainty characterisation did not jeopardise the results. Moreover, the proposed minimisation algorithm proved to be effective and fast in reducing the number of parameters. A reduction of 5 to 13 parameters out of 19 to 22 was achieved on the four case studies by requiring 14000 to 22000 model simulations.

Evaluation of time-dependent strengths of californium neutron sources by decay of 252Cf, 250Cf, and 248Cm: Uncertainties by Monte Carlo method

A method for estimating source strength measurements of californium neutron sources is presented, based on the model of 252Cf, 250Cf, and 248Cm decay. This is combined with the Monte Carlo method (MCM) of propagating uncertainties. Californium sources were categorized into two types: Sort–A are those with most input quantities known while Sort–B are sources with only the mass at a certain reference date known. For Sort–A, the spread of all input quantities was estimated with Gaussian distribution and the deterministic 1st order GUM uncertainty propagation is applied to validate the MCM results. While, for Sort–B with inputs that have non-Gaussian distributions, only MCM is applied to evaluate uncertainties. Results show that for californium sources that are 25 y or older, a simple 252Cf decay correction is imprecise due to the contribution of 250Cf and 248Cm. The MCM was also shown to be a robust technique for uncertainty analysis that provides results for both Gaussian and non-Gaussian distributions. Moreover, the time-dependence of the contributors in the source strength and the corresponding uncertainties are presented. When exceedingly low uncertainties are not required, the calculation techniques presented in this work may serve as an alternative to actual measurements, which tend to be expensive.

Probabilistic Assessment of Extended Detention Basins: Role of Model Parameter Uncertainty

AbstractThis study investigates the role of parameter uncertainty on evaluation of stormwater control measures. Specifically, several formal and informal Bayesian uncertainty analysis techniques ar...

Design and simulation-based validation of life cycle assessment and life cycle cost of an air conditioning system

The use of air conditioning (AC) systems is widespread in Saudi Arabia. The primary energy source in this region are electric power stations running on fossil fuels, which have harmful environmental impact. A side effects of the increasing use of ACs is the emission of hydrofluorocarbons, which contribute to ozone depletion. The remanufacturing of ACs has been reported as an environmentally friendly alternative due to waste reduction and minimal use of raw materials. This study compares the energy intensities of manufacturing and remanufacturing ACs in terms of environmental and resource economics. This comparison was carried out using an environmental life cycle assessment. Furthermore, a life cycle cost (LCC) analysis was also carried out to determine the most cost-effective option in different sectors from the user's perspective. The results were validated using an uncertainty analysis technique. These findings were used to estimate the manufacturing/remanufacturing energy across multiple use cycles.

Application of similarity analysis method in zero-power experimental validation

New advanced reactors require zero-power experiments to validate the correctness of design. In order to ensure sufficient validation strength, certain similarity of physical properties between zero-power experimental reactor and the target reactor is required. Based on sensitivity and uncertainty analysis techniques, three indexes for evaluating the physical similarity between reactor systems were introduced. Based on the Monte Carlo transport program MONK, a coupled similarity analysis program MCS was compiled. The results of MCS were compared with TSUNAMI’s and the two programs showed good agreement. Finally, the similarity between the CEFR in MOX fuel loading design and the BFS-119-2 zero-power experiment scheme given by IPPE was calculated by MCS. The results showed that the two systems were very similar to each other, and the physical properties of the CEFR-MOX loading design could be validate by the zero-power experiment.

The dynamic benefit compensation in a multi-reservoir system based on importance analysis

The multi-reservoir system has remarkable advantages that mostly come from benefit compensation among reservoirs. However, no in-depth studies on dynamic benefit compensation in a multi-reservoir system have been conducted. This study presents a comprehensive framework to quantitatively evaluate reliability of the multi-reservoir system, which is integrated with techniques of importance analysis, compensation mechanism, and uncertainty analysis. Two simplified series-parallel connections of the multiple reservoirs and three major reservoirs are introduced to demonstrate the application of the proposed framework. The importance-based system reliability analysis and benefit compensation strategy are then given. The importance level can be served as an evaluation indicator for providing answers to a set of questions with respect to which connection mode should be used, how many members should be integrated; and how long the operational time should be ensured. Results show that a low member’s failure rate would result in a low importance level and then to an insignificant effect on system reliability. The effect induced by the connection mode would be most significant than that induced by the failure rate. An optimal compensation proportions for the three major reservoirs would be achieved according to the member’s importance level and characteristic index with an overall satisfaction degree of 0.7380.

Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals

Utilizing the readily available, inexpensive, remotely-sensed satellite data products in combination with Markov Chain methods for estimating water levels and discharge anywhere along the vast river networks around the world is one of the most interesting and promising fields in hydrology. This study presents two new extensions of Markov Chain (MC) methods, namely the Online-Markov Chains (O-MC) and Extreme Online-Markov Chains (EO-MC) methods to improve the prediction accuracy. The O-MC method has the advantage of the online implementation of the correct variable states, and the EO-MC method has the advantage of online updating the Markov Matrix (MM) along with the online implementation of the correct variable states. The new O-MC and the EO-MC methods were evaluated using short-term satellites signal predictions for six different case study rivers. The Monte Carlo uncertainty analysis was used to measure the reliability of the new MC-based methods. Each model was developed 1000 times to calculate two indices, namely the 95 Percent Predicted Uncertainties (95PPU) and average distance factor (d-factor). The performances of MC and two extensions of O-MC and EO-MC were also examined for cases where we lack the training data. The Training Percent (TrPr) of the entire dataset gradually decreased from 90% to 1%, and the performance of the models in producing accurate future signals in the non-observed dataset is calculated. The Input Variable Imitation (IVI) problem was considered for the MC-based methods, and the results were compared with the Linear Regression (LR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM), and Radial Basis Function (RBF) regression methods. The results showed that the performance of EO-MC and O-MC are better than the simple MC method. In addition, it is concluded that EO-MC and O-MC have very similar performance in the uncertainty analysis and both methods are robust techniques. The main advantage of EO-MC compared with the O-MC method is highlighted when the number of training samples are very low. Finally, considering the IVI problem, the new O-MC and EO-MC methods significantly outperformed the LR, MLP, ELM, and RBF methods.

The Role of Dimensionality in Understanding Granuloma Formation.

Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions.

Evaluation of an efficient calibration protocol for use in radiochromic film dosimetry for photon beams

This study aimed to investigate the adequacy of the response quantities and the calibration functions utilized in Gafchromic™ film dosimetry. EBT3 Gafchromic™ film pieces were irradiated to the deemed dose values between 0 and 3 Gy with the beam quality of 6 MV. Response quantities (net change in transmittance (net∆T) and net change in optic density (net∆OD)) were calculated. To determine whether the response quantities and different function forms were acceptable, they were tested using the dose uncertainty analysis techniques. An accurate and precise calibration protocol suggesting a new logarithmic functional form and its superior performance with use of netΔT was demonstrated.

Investigation of mechanical error in four-bar mechanism under the effects of link tolerance

Many planar manipulators are composed of a basic four-bar mechanism. The output delivered by the mechanism deviates from desired one due to factors like link tolerances. The performance needs to be characterized for satisfactory application. This article presents investigation of mechanical error in four-bar mechanism with revolute joints (4R) under the influence of link tolerance. The classical partial derivative formulation (PDA) in uncertainty analysis technique is primarily used for estimating the mechanical error. The error estimation is carried out with proposed modification in PDA formulation. A geometric approach is also developed to estimate the mechanical error for 4R configuration. The mechanical error obtained through PDA is verified using geometric approach. The generalized formulation is demonstrated, and comparative estimation is presented. The methods and conclusions proposed herewith are adaptable for other planar configurations.

Uncertainty analysis method based on fuzzy random variables and time window selection

The uncertainty analysis has been conceived as a necessary step in the engineering research in various fields. The purpose of this article is to introduce the uncertainty analysis technique into the process of statistical modelling. Based on the theory of fuzzy random variables, aleatory and epistemic uncertainties in the statistical modelling process can be modeled by fuzzy random variables, which are interpreted as random variables with fuzzy statistical properties. An important aspect of modelling uncertainties lies in the appropriate selection of time window, which is used for the inclusion of data. In order to avoid the arbitrary choice of time window, a simple method for time window selection is proposed by analyzing the uncertainty and the stability of statistic data. Finally, a case study is carried out on the accident statistics. The results show that the proposed methods are effective and can provide more information for decision-making.

Sensitivity/uncertainty analyses comparing LR-0 reactor experiments containing FLiBe salt with models for molten-salt-cooled and molten-salt-fueled reactors

Critical experiments using an insertion zone with FLiBe salt performed at the LR-0 reactor at Research Centre Řež (RC Řež) have been compared to application models for solid-fueled, fluoride-salt-cooled high-temperature reactor (FHR) and liquid-fueled molten salt reactor (MSR) concepts using sensitivity and uncertainty (S/U) analysis techniques. These experiments support FHR and MSR advanced reactor concepts by informing on neutron spectral effects and nuclear data uncertainties related to fluoride salts. The FLiBe salt in the LR-0 experiments is from the Oak Ridge National Laboratory Molten Salt Reactor Experiment and is enriched to greater than 99.99% 7Li. This work is part of a broader collaboration between the United States and the Czech Republic on MSR and FHR technology. Results from the S/U analyses for FHR and MSR models indicated the most significant potential source of eigenvalue bias due to nuclear data within the FLiBe salt is radiative capture in 7Li. Other smaller but potentially significant contributions come from 19F, 6Li, and other 7Li reactions. Similarity comparisons of the RC Řež LR-0 experiments and FHR and MSR application models indicate that the LR-0 FLiBe experiments could be useful as candidate benchmarks for low-enriched uranium–fueled application models. However, sensitivity differences observed included both spectral effects, due to the LR-0 reactor being moderated by water instead of graphite and fluoride salt, and sensitivity magnitude effects driven by the amount of FLiBe inserted and its location. New experiments with refined designs to increase the volume and importance of salt in the system could provide improved data. Results also demonstrate that salt systems using thorium and/or 233U fuel will require additional experiments with relevant driver fuels. Increased contributions from the graphite moderator and 19F and covariance between reactions such as 233U fission and 233U radiative capture indicate higher uncertainty contributions from the salt and significant differences in contributions from the fuel species due to underlying uncertainties in their nuclear data.