Propagation Of Input Uncertainties Research Articles

Uncertainty Propagation in a Simplified Integrated Thermal Management System

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

Uncertainty and Sensitivity Evaluation of the QUENCH-02 Experiment Simulation Using the ASYST Code

Abstract Uncertainty and sensitivity methods are increasingly used in safety analyzes of nuclear power plants to address the unreliability of input data, numerical models, and, in general, the lack of knowledge regarding certain physical phenomena, in determining safety margins and acceptance criteria. The ASYST code, developed as part of an international nuclear technology ASYST Development and Training Program (ADTP) managed by Innovative Systems Software (ISS), is used to perform an uncertainty analysis of the QUENCH-02 experiment conducted at the Karlsruhe Institute of Technology. The code uses a probabilistic methodology based on the propagation of input uncertainties. The QUENCH facility contains electrically heated PWR fuel rod simulators and the aim of the experiment is to examine hydrogen generation and the behavior of the fuel rod cladding during core reflood. For selected input parameters, such as steam/water flow, electrical power, and other relevant boundary conditions, it is necessary to define their probability density functions. Input databases are then prepared for individual calculations based on the selected confidence level and confidence interval. The number of performed calculations is 60, large enough to ensure at least 95% coverage of expected output results and uncertainty limits. The results of the calculations are compared with the experimental measurements. The Pearson correlation coefficient is used to obtain correlation between the input uncertain parameters and the output data. Sensitivity analyses cover the influence of variations in the heater's electrical power and the steam flow rate on the hydrogen production and the maximum cladding temperature.

Probing asymmetric uncertain effect on symmetric three-lobe journal bearing

The conventional design process of journal bearings is completely based on deterministic theoretical predictions. The deterministic approach ignores the probabilistic and random nature of input variables and consequently, poses an arbitrary tolerance or safety factor on the performance parameters. However, such arbitrary safety factors result in performance loss when the factor is very high or failure of the system when the factor is low. This paper presents a probabilistic approach for the design of a three-lobe journal bearing considering the uncertainty in geometric parameters such as clearance, preload, and offset factor. The Monte Carlo simulation (MCS) algorithm is used for the propagation of input uncertainties to the systems’ output. To increase the efficiency of computationally expensive MCS, the moving least square (MLS) method is used as a surrogate model. The training data of the surrogate model is obtained by solving the Reynolds equation, at selective design points, using the finite difference method. To explain the probabilistic response of the bearing for various applications and different operating conditions, the results are presented for long, short, and finite bearings at three different supply pressures considering three different eccentricity ratios. The results of the probabilistic analysis show a significant deviation of the system response from its deterministically predicted values and suggest safe design values for the reliable operation of a three-lobe bearing.

Vapor concentration and temperature field measurement of an evaporating sessile drop by tomographic laser absorption spectroscopy

Drop evaporation is a ubiquitous phenomenon that has been studied for over a century. However, the surrounding gas-phase field including the temperature and vapor concentration distribution is not sufficiently studied experimentally. In this paper, a sensor based on tunable laser absorption spectroscopy is designed to study the vapor-phase temperature and concentration distribution of evaporating sessile drops, and data processing method involving data pre-processing and tomographic reconstruction is proposed to realize high-precision, spatially resolved measurement, which was realized by scanning the mechanical galvanometer in the horizontal direction. With free-knot splines smoothing and “denucleated” onion-peeling algorithm, temperature and H2O concentration distributions surrounding the evaporated drop at three different substrate plate temperatures are observed. The concentration and temperature in close vicinity to the gas–liquid interface are reconstructed accurately despite the high-gradient changes. A spatial resolution of under 100 μm with a temporal resolution of 10 s has been realized. Quantitative depiction of the temperature and concentration fields shows evidence of convection and indicates that while the concentration level sharply peaks at the interface, temperature in the close vicinity to the drop shows flattening or even dipping trends. The in situ laser measurement results are validated against contact measurement, theoretical prediction with saturated vapor pressure, and model simulation of COMSOL. Uncertainties have been evaluated based on both repeated measurements and model prediction of input uncertainty propagation. Temperature and concentration measurement uncertainties are estimated to be <1.5% and <3.5%, respectively, even though all experiments were performed in open air with non-negligible buoyancy-induced convection.

An analytical model to quantify the impact of the propagation of uncertainty in knee joint angle computation

ABSTRACT Joint kinematics are typically described using Cardan angles or the attitude vector and its projection on the joint axes. Whichever the notation used, the uncertainties present in gait measurements affect the computed kinematics, especially for the knee joint. One notation – the attitude vector – enables the derivation of an analytical model of the propagation of uncertainty. Thus, the objective of this study was to derive this analytical model and assess the propagation of uncertainty in knee joint angle computation. Multi-session gait data acquired from one asymptomatic adult participant was used as reference data (experimental mean curve and standard deviations). Findings showed that an input uncertainty of 5° in the attitude vector and joint axes parameters matched experimental standard deviations. Taking each uncertainty independently, the cross-talk effect could result from uncertainty in the orientation of either the attitude vector (intrinsic variability) or the first joint axis (extrinsic variability). We concluded that the model successfully estimated the propagation of input uncertainties on joint angles and enabled an investigation of how that propagation occurred. The analytical model could be used to a priori estimate the standard deviations of experimental kinematics curves based on expected intrinsic and extrinsic uncertainties.

Assessing the effects of input uncertainties on the outputs of a freight demand model

Freight demand models typically use a series of sub-models that relate several inputs to model outputs, without considering the uncertainty of typical data sources used in the model development process. Hence, the uncertainty of freight demand model outputs is typically not characterized, making it difficult to understand the robustness of the model results, or how the rigour of the results might be improved with additional data. This paper proposes a formal five-step framework to analyze the effects and propagation of input uncertainty from datasets used during model development on the uncertainty of the outputs in a freight demand model driven by exogenous economic forecasts. The framework is applied to a Canadian commodity-based freight demand model, inspired by the Aggregate-Disaggregate-Aggregate (ADA) model, used to analyze the effects of the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP) on Canada's trade infrastructure. In this application of the framework, uncertainty for input datasets used to develop three sub-models is introduced and a set of outputs is simulated through repeated simulation. Descriptive statistics and rank error measures are used to access the uncertainty of the outputs. The results suggest that the case study model performs with adequate robustness in terms of aggregated outputs and for larger trade partners, while some specific disaggregate outputs and scenarios with smaller trade partners are less robust.

Uncertainty Study of the In-Vessel Phase of a Severe Accident in a Pressurized Water Reactor

A comprehensive uncertainty analysis in the event of a severe accident in a two-loop pressurized water reactor is conducted using an uncertainty package integrated in the ASYST code. The plant model is based on the nuclear power plant (NPP) Krško, a Westinghouse-type power plant. The station blackout scenario with a small break loss of coolant accident is analyzed, and all processes of the in-vessel phase are covered. A best estimate plus uncertainty (BEPU) methodology with probabilistic propagation of input uncertainty is used. The uncertain parameters are selected based on their impact on the safety criteria, the operation of the NPP safety systems and to describe uncertainties in the initial and boundary conditions. The number of required calculations is determined by the Wilks formula from the desired percentile and confidence level, and the values of the uncertain parameters are randomly sampled according to appropriate distribution functions. Results showing the thermal hydraulic behaviour of the primary system and the propagation of core degradation are presented for 124 successful calculations, which is the minimum number of required calculations to estimate a 95/95 tolerance limit at the 3rd order of the Wilks formula application. A statistical analysis of the dispersion of results is performed afterwards. Calculation of the influence measures shows a strong correlation between the decay heat and the representative output quantities, which are the mass of hydrogen produced during the oxidation and the height of molten material in the lower head. As the decay heat increases, an increase in the production of hydrogen and the amount of molten material is clearly observed. The correlation is weak for other input uncertain parameters representing physical phenomena, initial and boundary conditions. The influence of the order of the Wilks formula is investigated and it is found that increasing the number of calculations does not significantly change the bounding values or the distribution of results for this particular application.

Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion

Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.

BEPU analysis of a passive decay heat removal system with RELAP5/MOD3.3 and RELAP5-3D

Passive safety systems are currently implemented or under consideration in several advanced nuclear plants. In the Best-Estimate Plus Uncertainty (BEPU) approach it is fundamental to qualify Best-Estimate thermal-hydraulic system codes and models for the phenomena typical of passive systems and to quantify the uncertainty of calculations. In the present paper, through RELAP5/MOD3.3 and RELAP5-3D codes, thermal-hydraulic analyses were carried out and the obtained results compared against the experimental data collected in the experimental test facility PERSEO. For the present analysis, Test 7 Part 2, conducted at the nominal primary pressure of 7 MPa, has been selected. An accuracy evaluation has been performed on the reference calculation carried out with the two codes. For the accuracy evaluation both a qualitative and quantitative approach have been considered. The uncertainty of both calculations has been quantified by means of the probabilistic method of propagation of input uncertainties. Uncertain input parameters have been selected considering initial and boundary conditions, physical parameters and model uncertainties. The Uncertainty Analysis has been carried out through the RAVEN framework developed by Idaho National Laboratory. Additionally, regression and correlation indices have been used to characterize the importance of the selected uncertain input parameters for the selected Figure Of Merit. The effect of sampling methodologies on the results has also been evaluated.

Uncertainty study on atmospheric dispersion simulations using meteorological ensembles with a Monte Carlo approach, applied to the Fukushima nuclear accident

In emergency cases, when nuclear accidental releases take place, numerical models, developed by French Institute of Radiation Protection and Nuclear Safety (IRSN), are used to forecast the atmospheric dispersion of radionuclides. These models compute the quantity of radionuclides in the atmosphere, their deposited amount on the ground, and the subsequent gamma dose rate. Their results are used to make recommendations to protect the population in case of nuclear accident. However, the simulations are subject to considerable uncertainties. These uncertainties originate from different sources: input variables (weather forecasting, source term), physical parameters used in the models (turbulent diffusion, scavenging coefficient, deposition velocity, etc.) and model approximations (representativeness and numerical errors). This paper presents the propagation of input uncertainties through a Eulerian radionuclide transport model, ℓdX, applied to the Fukushima nuclear disaster. This uncertainty propagation involves perturbing the input variables and making numerous calls to the model. The perturbations should be broad enough to cover the possible range of variation of uncertain variables. Weather forecast ensembles are used to take into account meteorological uncertainties, and several source terms from the literature are included. The following step is to evaluate the spread of the outputs in order to draw insights about the subsequent uncertainties. In order to assess the quality of the ensemble of simulations, comparisons with radiological observations were carried out, using statistical indicators, both deterministic such as Root Mean Square Error (RMSE) or Figure of Merit in Space (FMS), and probabilistic indicators such as rank histograms, Brier score and Discrete Ranked Probability Scores (DRPS).

The Impact of Exceptional Points on the Reliability of Thermoacoustic Stability Analysis

Abstract Exceptional points can be found for specific sets of parameters in thermoacoustic systems. At an exceptional point, two eigenvalues and their corresponding eigenfunctions coalesce. Given that the sensitivity of these eigenvalues to parameter changes becomes infinite at the exceptional point, their occurrence may greatly affect the outcome and reliability of numerical stability analysis. We propose a new method to identify exceptional points in thermoacoustic systems. By iteratively updating the system parameters, two initially selected eigenvalues are shifted toward each other, ultimately colliding and generating the exceptional point. Using this algorithm, we were able to identify for the first time a physically meaningful exceptional point with positive growth rate in a thermoacoustic model. Furthermore, our analysis goes beyond previous studies inasmuch as we employ a more realistic flame transfer function to model flame dynamics. Building on these results, we analyze the effect of exceptional points on the reliability of thermoacoustic stability analysis. In the context of uncertainty quantification, we show that surrogate modeling is not reliable in the vicinity of an exceptional point, even when large sets of training samples are provided. The impact of exceptional points on the propagation of input uncertainties is demonstrated via Monte Carlo computations. The increased sensitivity associated with the exceptional point results in large variances for eigenvalue predictions, which needs to be taken into account for reliable stability analysis.

Unsteady RANS simulation of OECD-TAMU cold-leg mixing benchmark

Unsteady Reynolds-Average Navier-Stocks (URANS) simulation was conducted, in which the mixing of two immiscible fluids with different densities were measured using the multi-component material model for the OECD-TAMU cold-leg mixing benchmark. The turbulence models (i.e. realizable κ-ε, standard κ-ω and κ-ω SST) were validated against the particle image velocimetry (PIV) data and sensitivity analysis of uncertain input parameters were carried out. Turbulence models for the prediction of mean velocity components and root mean square velocity components, the mixing phenomena in the cold-leg were found in good agreement with the PIV data. However, the turbulence models unable to predict the close agreement with PIV data in the downcomer. Numerical uncertainty of two meshes was conducted for cold-leg and downcomer. It is observed that the numerical uncertainty band is within the range of experimental measurement data. Furthermore, The sensitivity of five uncertain input parameters (i.e. Schmidt number, density difference, heavy fluid viscosity, light fluid viscosity) was analyzed and their influence on the output parameters was noticed. Therefore, the turbulent Schmidt number and density difference, revealing significant effect on the out parameters. As a consequence, the major contribution of turbulent Schmidt number and density difference will be observed in the propagation of input uncertainty.

Enhancing the One-Dimensional SFR Thermal Stratification Model via Advanced Inverse Uncertainty Quantification Methods

Thermal stratification (TS) is a thermal-fluid phenomenon that can introduce large uncertainties to nuclear reactor safety. The stratified layers caused by TS can lead to temperature oscillations in the reactor core. They can also result in damages to both the reactor vessel and in-vessel components due to the growth of thermal fatigue cracks. More importantly, TS can impede the establishment of natural circulation, which is widely used for passive cooling and ensures the inherent safety of numerous reactor designs. A fast-running one-dimensional (1-D) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. The efficient 1-D model provided reasonable temperature predictions for the test conditions investigated, but nonnegligible discrepancies between the 1-D predictions and the experimental temperature measurements were observed. These discrepancies are attributed to the model uncertainties (also known as model bias or errors) in the 1-D model and the parameter uncertainties in the input parameters. In this study, we first recognized through a forward uncertainty analysis that the observed discrepancies between the computational predictions and the experimental temperature measurements could not be explained solely by input uncertainty propagation. We then performed an inverse uncertainty quantification (UQ) study to reduce the model uncertainties of the 1-D model using a modular Bayesian approach based on experimental data. Inverse UQ serves as a data assimilation process to simultaneously minimize the mismatches between the predictions and experimental measurements, while quantifying the associated parameter uncertainties. The solutions of the modular Bayesian approach were in the form of posterior probability density functions, which were explored by rigorous Markov Chain Monte Carlo sampling. Results showed that the quantified parameters obtained from the inverse UQ effectively improved the predictive capability of the 1-D TS model.

A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations

Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information (e.g., flow field, wall shear stress, and pressure distribution), which has become a paradigm in cardiovascular research and healthcare. Nonetheless, the predictive accuracy largely depends on precisely specified boundary conditions and model parameters, which, however, are usually uncertain (or unknown) in most patient-specific cases. Quantifying the uncertainties in model predictions due to input randomness can provide predictive confidence and is critical to promote the transition of CFD modeling in clinical applications. In the meantime, forward propagation of input uncertainties often involves numerous expensive CFD simulations, which is computationally prohibitive in most practical scenarios. This paper presents an efficient bi-fidelity surrogate modeling framework for uncertainty quantification (UQ) in cardiovascular simulations, by leveraging the accuracy of high-fidelity models and efficiency of low-fidelity models. Contrary to most data-fit surrogate models with several scalar quantities of interest, this work aims to provide high-resolution, full-field predictions (e.g., velocity and pressure fields). Moreover, a novel empirical error bound estimation approach is introduced to evaluate the performance of the surrogate a priori. The proposed framework is tested on a number of vascular flows with both standardized and patient-specific vessel geometries, and different combinations of high- and low-fidelity models are investigated. The results show that the bi-fidelity approach can achieve high predictive accuracy with a significant reduction of computational cost, exhibiting its merit and effectiveness. Particularly, the uncertainties from a high-dimensional input space can be accurately propagated to clinically relevant quantities of interest (e.g., wall shear stress) in the patient-specific case using only a limited number of high-fidelity simulations, suggesting a good potential in practical clinical applications.

Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

Inconsistent behavior of mechatronic applications is often related to uncertainties ingrained in the system. Stochastic models spawn an output distribution for any given deterministic input. A stochastic model can be obtained by associating a probability distribution to several uncertain model parameters and by propagating the parametric input uncertainty through the deterministic model. Typically a maximum likelihood identification procedure is used to estimate the parametric input uncertainty from observations. Such inverse identification procedures require to iterate the expensive forward input uncertainty propagation. In this paper we establish an efficient forward uncertainty propagation method by combining Polynomial Chaos and moment matching. The high-order statistical moments of the output distribution are estimated using the generalized Polynomial Chaos framework and by using the maximum entropy strategy a distribution can be associated to them. This strategy is numerically very attractive due to reduced forward sampling and deterministic nature of the propagation strategy. The strategy is integrated in the inverse uncertainty identification of a wet clutch system for which certain model parameters exhibit inconsistent behaviour and are therefore considered as stochastic. The number of required model simulations to achieve the same accuracy as the brute force methodologies is decreased by one order of magnitude. The probability model identified with the high order estimates resulted into a true log-likelihood increase of about 4% since the accuracy of the estimated output probability density function could be improved up to 47%.

The CASUALIDAD Method for Uncertainty Evaluation of Best-Estimate System Thermal-Hydraulic Calculations

Predictive Modeling Methodology constitutes an innovative approach to perform uncertainty analysis (UA) that reduces the subjective and user-defined ways to manage experimental data and derive uncertainty of input parameters that characterize the Propagation of Input Uncertainties (PIU) and/or Propagation of Output Accuracies (POA) methods. The Code with the capability of Adjoint Sensitivity and Uncertainty AnaLysis by Internal Data ADjustment and assimilation (CASUALIDAD) method can be developed as a fully deterministic method based on advanced mathematical tools to internally perform in the thermal-hydraulic system code the sensitivity analysis (SA) and the UA. The method is based upon powerful mathematical tools to perform the SA and upon the Data Adjustment and Assimilation methodology by which experimental observations are combined with code predictions and their respective errors through the application of the Bayes theorem and of the Principle of Maximum Likelihood to provide an improved estimate of the system state and of the associated uncertainty considering all input parameters that affect any prediction. The methodology has been structured in two main steps. The first step generates the database of improved estimations (IEs) starting from the available set of experimental data and related qualified calculations. The second step deals with the use of the selected (from the obtained database) set of IEs for the uncertainty evaluation of the predicted nuclear power plant transient scenario. The proposed methodology clearly interrelates in a consistent and robust framework the code validation issue with the evaluation of the uncertainty of code responses passing through the quantification of input uncertainty parameters of code models, thus constituting a step forward with respect to the subjectivity of the current methods based on PIU and/or POA.

Quantification of Input Uncertainty Propagation Through Models of Aluminum Alloy Direct Chill Casting

Insight into transport phenomena in complex solidification processes, such as direct chill (DC) casting, that cannot be found from experimental observation can be gained from numerical simulations. These predictions depend on material, process, and numerical parameters which contain inherit uncertainties due to experimental measurements or model assumptions. A fully transient numerical model of the direct chill casting process of Al-4.5 wt pct Cu was used to examine the propagation of input uncertainty to outputs of interest. The effect of microstructural model parameters, thermal boundary conditions, and material property input uncertainties were examined. Probability density functions were calculated based on these input uncertainties for metrics that characterize the ingot macrosegregation and sump depth. The macrosegregation-level predictions depend strongly on parameters that control the formation of the rigid mushy zone and shrinkage-driven flow. The heat release and transfer in the mushy zone are the dominant factors for determining the sump depth.

The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach

Abstract. The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

Abstract Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

Uncertainty and sensitivity analysis in reactivity-initiated accident fuel modeling: synthesis of organisation for economic co-operation and development (OECD)/nuclear energy agency (NEA) benchmark on reactivity-initiated accident codes phase-II

Abstract In the framework of OECD/NEA Working Group on Fuel Safety, a RIA fuel-rod-code Benchmark Phase I was organized in 2010–2013. It consisted of four experiments on highly irradiated fuel rodlets tested under different experimental conditions. This benchmark revealed the need to better understand the basic models incorporated in each code for realistic simulation of the complicated integral RIA tests with high burnup fuel rods. A second phase of the benchmark (Phase II) was thus launched early in 2014, which has been organized in two complementary activities: (1) comparison of the results of different simulations on simplified cases in order to provide additional bases for understanding the differences in modelling of the concerned phenomena; (2) assessment of the uncertainty of the results. The present paper provides a summary and conclusions of the second activity of the Benchmark Phase II, which is based on the input uncertainty propagation methodology. The main conclusion is that uncertainties cannot fully explain the difference between the code predictions. Finally, based on the RIA benchmark Phase-I and Phase-II conclusions, some recommendations are made.