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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

14 SOLAR ENERGY↗
DOE OSTI2015

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

14 SOLAR ENERGY↗
DOE OSTI2017

AN INVESTIGATION OF CONSISTENT UNCERTAINTY PROPAGATION AND SENSITIVITY ANALYSIS IN A MULTIPHYSICS ENVIRONMENT

The framework for a LOCA toolkit for the U.S. light water reactors (LOTUS) is outlined. LOTUS allows for the coupling of the core design code VERA-CS with the fuel performance code FRAPCON such that uncertainties can be propagated consistently. An analysis of inherent model discrepancies, confirms that all differences are either negligible or the focus on immediate future work. Monte Carlo sampling is performed on 15 inputs for a depletion case of one fuel assembly. Both uncertainty quantification (UQ) and sensitivity analysis (SA) are performed on the figures of merit (FOM) of minimum departure from nucleate boiling ratio (MDNBR), maximum fuel centerline temperature (MFCT), and gap conductance at peak power (GCPP). Pearson correlation coefficient, Sobol sensitivity indices, and the density-based, delta moment independent measure are used as SA metrics. All measures yield consistent rankings of inputs matching past work.

10CFR50.46c↗
DOE OSTI2019

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities: Preprint

Increased penetration of distributed variable re-newable generation may cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But due to the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network is needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69 node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗
DOE OSTI2013

Quantitative uncertainty and sensitivity analysis of a PWR control rod ejection accident

The paper describes the results of the quantitative Uncertainty and Sensitivity (U/S) Analysis of a Rod Ejection Accident (REA) which is simulated by the coupled system code ATHLET-QUABOX/CUBBOX applying the GRS tool for U/S analysis SUSA/XSUSA. For the present study, a UOX/MOX mixed core loading based on a generic PWR is modeled. A control rod ejection is calculated for two reactor states: Hot Zero Power (HZP) and 30% of nominal power. The worst cases for the rod ejection are determined by steady-state neutronic simulations taking into account the maximum reactivity insertion in the system and the power peaking factor. For the U/S analysis 378 uncertain parameters are identified and quantified (thermal-hydraulic initial and boundary conditions, input parameters and variations of the two-group cross sections). Results for uncertainty and sensitivity analysis are presented for safety important global and local parameters. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Methodology, verification, and performance of the continuous-energy nuclear data sensitivity capability in MCNP6

A continuous-energy sensitivity coefficient capability has been introduced into MCNP6. The methods for generating energy-resolved and energy-integrated sensitivity profiles are discussed. Results from the verification exercises that were performed are given, and these show that MCNP6 compares favorably with analytic solutions, direct density perturbations, and comparisons to TSUNAMI-3D and MONK. Run-time and memory requirements are assessed for typical applications, and these are shown to be reasonable with modern computing resources. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

K-Eigenvalue sensitivities of secondary distributions of continuous-energy data

MCNP6 has the capability to produce energy-resolved sensitivity profiles for secondary distributions (fission {Chi} and scattering laws). Computing both unconstrained and constrained profiles are possible. Verification is performed with analytic test problems and a comparison to TSUNAMI-3D, and the comparisons show MCNP6 calculates correct or consistent results. Continuous-energy calculations are performed for three fast critical experiments: Jezebel, Flattop, and copper-reflected Zeus. The sensitivities to the secondary distributions (integrated over chosen energy ranges) are of similar magnitude to those of many of the cross sections, demonstrating the possibility that integral experiments are useful for assessing the fidelity of these data as well. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Identification of Mission Sensitivities with Mission Modeling from the One System Organization at Hanford - 13292

The Hanford site in southeast Washington contains approximately 207 million liters of radioactive and hazardous waste stored in 177 underground tanks. The U.S. Department of Energy's Office of River Protection is currently managing the Hanford waste treatment mission, which includes the storage, retrieval, treatment and disposal of the tank waste. Two recent studies, employing the modeling tools managed by the One System organization, have highlighted waste cleanup mission sensitivities. The Hanford Tank Waste Operations Simulator Sensitivity Study evaluated the impact that varying 21 different parameters had on the Hanford Tank Waste Operations Simulator model. It concluded that inaccuracies in the predicted phase partitioning of a few key components can result in significant changes in the waste treatment duration and in the amount of immobilized high-level waste that is produced. In addition, reducing the efficiency with which tank waste is retrieved and staged can increase mission duration. The 2012 WTP Tank Utilization Assessment concluded that flowsheet models need to include the latest low-activity waste glass algorithms or the waste treatment mission duration and the amount of low activity waste that is produced could be significantly underestimated. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Uncertainty quantification and sensitivity analysis applications to fuel performance modeling

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlighted the importance of the thermal conductivity of the fuel, the thermal conductivity of the cladding, the gap thickness, the fuel thermal expansion, and the system power factor as influential parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Uncertainty and Sensitivity Analysis of Reactivity Insertion Accidents using BISON Fuel Performance Code

The development of Accident Tolerant Fuels (ATF) has expanded into a considerable research program in the wake of the events at the Japanese Fukushima Daiichi nuclear power plant in 2011. The ATF goal is to insert a test assembly into a commercial reactor by 2022. To support this, testing of the ATF design under off-normal conditions is needed to assess the 'enhanced accident tolerance' capability of the fuel rod design over standard UO{sub 2}-Zircaloy concepts. Transient testing capabilities are necessary for off-normal testing of events such as loss of coolant accidents (LOCA) and reactivity insertion accidents (RIA). A transient testing program has been developed to support the testing of ATF designs. The Transient Reactor Test (TREAT) facility has been chosen as the site to perform the transient testing of the ATF designs. TREAT is a versatile test facility capable of subjecting fuel specimens to various off-normal conditions. Historic TREAT experiments were performed in a variety of experimental test vehicles including static environment capsules, once-through flowing systems, and also recirculating flowing systems. The planned TREAT experiments will be operated under PWR conditions. Therefore, a static capsule vehicle is under development to handle required temperatures and pressures. The static capsule, termed the Static Environment Rodlet Transient Test Apparatus (SERTTA), will be used for irradiation tests in TREAT to subject ATF fuel rodlets to a range of power pulse conditions and environments. To aid in the design and analysis of future tests in TREAT, the fuel performance code BISON is being used. Fuel performance codes capable of accident modeling have been developed for some time and they have shown an ability to reproduce experimental results with some degree of adequacy, but they still require extensive verification and validation. Future experiments in TREAT will add to a limited database of past transient tests performed in transient reactors to support LWR systems. These tests can be used for future verification and validation of fuel performance codes. Understanding the impact of the uncertainty of influential parameters is critical for not only analyzing the results of the experiment but also for modeling and validation of codes. For example, uncertainties associated with the initial manufactured state of the sample, instrument measurements, material property models, and planned experiment conditions can provide important insight into model performance, experimental data, and play an important role in experimental design. The objective of this work is to perform an uncertainty and sensitivity analysis on a simplified RIA case of a standard UO{sub 2}-Zircaloy sample using expected nominal conditions in the SERTTA capsule in TREAT. The BISON fuel performance code is used in conjunction with DAKOTA (Design Analysis Kit for Optimization and Tera-scale Applications) to perform the uncertainty and sensitivity analysis. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

The Effects of Different Lattice Codes on Results of Sensitivity and Uncertainty Analysis

According to the UAM ('Uncertainty Analysis in Modeling') benchmark, uncertainties in the LWR system calculations at all stages should be determined and quantified. As the neutronics calculations are prerequisite for the predictions of the reactor system, and the uncertainties introduced by the neutronics calculations would be propagated to the subsequent calculations, hence uncertainty analysis has been firstly focused on the neutronics phase to quantify the uncertainties of the reactor-physics predictions in UAM. For the lattice physics, the objective of uncertainty analysis is focused on uncertainty quantifications of the few-group constants; for the core physics, uncertainties of the core steady-state stand-alone neutronics calculations are focused on. Based on the previous researches, the nuclear-data uncertainty has been treated as one of the most significant source of uncertainty and received the most focus. In the previous works, the domestic code UNICORN has the capability of performing sensitivity analysis and uncertainty analysis using the direct numerical perturbation (DNP) method and statistical sampling (SS) method. In this paper, the capability of the UNICORN code has been improved. The domestic lattice code NECP-CACTI has been added in UNICORN to perform the lattice calculations, with DRAGON5.0 and NECP-CACTI available for lattice calculations. For DRAGON5.0 and NECP-CACTI, all the methods and technologies are shared in UNICORN. Therefore, the effects of different lattice codes on the results of sensitivity and uncertainty analysis have been researched and determined in this paper. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Sensitivity Analysis of Passive Heat Removal Capability of FHR Based on Efficient Surrogate Model and GenFlow

Code Scaling, Applicability and Uncertainty Evaluation Methodology (CSAU) is used for quantifying reactor safety margins and works as an alternative method for licensing nuclear power plant. In the framework of CSAU, sensitivity and uncertainty analysis (SA and UA) should be performed. However, for large-scale complicated thermal hydraulics analysis simulated by code (e.g. RELAP5, CFD and subchannel analysis, the sensitivity and uncertainty analysis is really time-consuming. Furthermore, sometimes it is not feasible to perform SA and UA. Specifically, the computation time for Monte Carlo sampling or advanced LHS sampling can be neglected, but the computation time for thermal hydraulics code makes up main part of time during SA and UA process. In order to accelerate the speed of SA and UA, thermal hydraulics code can be improved based on advanced mathematic algorithm or using parallel method. However, the development for new mathematic method needs long time and strict verification and validation process is needed for a new code, which usually takes a couple of years or even longer time to have an approved code that can be used in nuclear industry. An alternative way to improve the speed of SA and UA is using surrogate model. The surrogate model is derived from the input as well as output data of the original code, assuming the code as a black box. The most common method is using response surface method (RSM) as a surrogate for the code, the input parameters are then sampled from their assumed distribution and the corresponding value of the response surface function is calculated. However, a couple of input parameters need hundreds or even thousands of original code calculations. The large number of runs is an obvious disadvantage of this method. Besides, the second disadvantage is an appropriate response surface function must be selected (e.g. quadratic, cubic, quartic, with or without cross product terms, or using a non-polynomial form) to avoid the overfit and underfit problem. This process is time consuming and expensive. Polynomial chaos expansion method is a reasonably fast and attractive alternative, stochastic model. The chaos expansion provides an efficient high-order method of including non-linear effects in stochastic analysis. Usually, the information about the model parameters and their probability distribution is limited and the dependence between them is not known. However, if arbitrary polynomial chaos is used, instead of assuming probability distribution for input parameters which may introduces additional subjectivity into the analysis, the available information can directly and most purely be used in stochastic analysis based on arbitrary data distribution. Thus, it is not necessary to know the exact probability density functions to perform SA and UA based on arbitrary polynomial chaos (aPC). The aPC has been applied for uncertainty quantification in many fields (e.g. in solid mechanics, fluid flows, thermal sciences), and become widely used in nuclear industry recently. Mk1 PB-FHR is a small modular fluoride-salt-cooled, high-temperature reactors (FHRs) that is a concept design by University of California, Berkeley (UCB). It has inherently safe features like natural-circulation-driven systems (e.g. direct reactor auxiliary cooling system (DRACS)) to passively remove decay heat from the reactor core during design basis and beyond design. It is necessary to perform SA and UA, since the licensing process of this new kind of reactor is under way and there are many design parameters that have not been fixed. Current nuclear commercial codes, such as RELAP5, have mainly been developed for light water reactors, which require the use of comprehensive two-phase flow models. This capability is not required to simulate single-phase flow phenomena in FHRs, whose baseline coolant, flibe, remains liquid up to 1430 deg. C at atmospheric pressure. The FHR advanced natural circulation analysis (FANCY) code has been developed specifically for FHR technology modeling and validation work has been performed by using CIET data in UCB. The code has been renamed General Flow (GenFlow) because it has been further developed to have the capability to model complicated pipework system based on object-oriented concept for user friendly purpose. Besides, it can be used to simulate both the force circulation as well as natural circulation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

VUSAT - A VERA-CS Uncertainty and Sensitivity Analysis Toolkit

VERA-CS (the Virtual Environment for Reactor Applications, Core Simulator) is a high-fidelity multiphysics core simulator under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). VERA-CS includes three main components: MPACT for reactor physics and neutron transport, COBRA-TF for thermal-hydraulics, and ORIGEN for isotopic depletion. VERA-CS has been applied to an array of problems including core physics analysis, and the modeling for all of the fuel cycles of Watts Bar Nuclear Unit 1 (WBN1). MPACT is a 3D pin-resolved reactor transport code developed by Oak Ridge National Laboratory (ORNL) and the University of Michigan. COBRA-TF (CTF) is a three-field two-phase flow subchannel analysis code developed by ORNL and North Carolina State University. CTF solves the mass, momentum, and energy equations for the liquid, vapor, and droplet phases for each subchannel in the core and captures the axial flow within each channel as well as cross-flow between channels. Direct coupling between neutronics (MPACT) and thermal-hydraulics (CTF) in VERA-CS provides temperature and density feedback. CTF includes a number of models important for LWR safety and design analysis including flow regime dependent two-phase wall heat transfer, inter-phase heat transfer and drag, droplet breakup, and quench-front tracking. ORIGEN is developed by ORNL and released through the SCALE package. ORIGEN is directly integrated in MPACT through its API to calculate the changes in nuclides for every depletable cross-section region. Uncertainties exist in safety calculations with computer codes for nuclear reactor modeling and simulation, and they come from code limitations, scaling inaccuracies embedded in the experimental data, and uncertainties associated with the state of the reactor at the initiation of the transient. Consequently, for applications of high fidelity calculations using VERA-CS, proper uncertainty quantification (UQ) and sensitivity analysis (SA) of the computing models of reactor neutronics and thermal-hydraulics are important to comprehensively understand the results. New methods in UQ and SA have to be developed in multi-physics calculations using VERA-CS. Toward this end, a software package called VUSAT which stands for VERA-CS Uncertainty and Sensitivity Analysis Toolkit has been developed to facilitate the applications of VERA-CS within the context of best estimate plus uncertainty (BEPU) analysis framework. In this work, the VUSAT application to analyze departure from nucleate boiling (DNB) is presented.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Sensitivity Analysis of Successful Evacuee Proportion in Hypothetical Seismic Nuclear Emergency Situation

A magnitude 5.8 earthquake, which is the strongest earthquake ever detected in Korea, occurred in Gyeongju on 12 September, 2016. People were worried about not only the impact to their safety and quality of life as directly affected from the earthquake, but also the secondary impact from such a nuclear power plant (NPP) accident incurred by the earthquake. Public awareness is changing such that the risk of earthquakes is no longer a story of other countries. Even if the current situation in Korea is not regarded, it is important to assess the seismic hazard to an NPP and prepare for preventing the effects related with the earthquake impact. Developing a seismic evacuation model is necessary in a part of a level 3 probabilistic safety assessment (PSA) because it is one of the most affecting factors of public radiation exposure and health effects in an emergency phase. In a previous study, a sensitivity analysis of the evacuation speed in a hypothetical NPP accident by an earthquake has been carried out, and population doses resulting from various seismic evacuation scenarios associated with evacuation speed have been compared. In addition to the previous study, the proportion of evacuees who evacuate successfully with adequate speed has been considered and a sensitivity analysis has been performed in this study.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Sensitivity of a response to the composition of an (α,n) neutron source - 24235

We derive the sensitivity of a response to the composition of a homogeneously mixed (α,n) neutron source. We use the mathematical descriptions of the (α,n) neutron source rate density and spectrum that are used by SOURCES4C; specifically, a material's α-particle stopping power is given by the Bragg-Kleeman relationship. The (α,n) neutron source is computed for each combination of α particle emitter (source isotope) and (α,n) target isotope. In addition, each isotope in the material has an effect on the (α,n) neutron source because of its contribution to the α particle stopping power. The derivative of the total (α,n) source rate density with respect to each isotope density is exact, but the derivative of the energy spectrum is approximated using an appropriate spectrum. Numerical results using the total neutron leakage from the BeRP ball demonstrate the correctness and accuracy of the sensitivities. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Sensitivities and correlations of critical experiments due to uncertainties of system parameters and nuclear data - 14416

With increasing demand on accuracy of criticality calculations, correlations between benchmark experiments due to system parameter uncertainties become of interest in bias determination. The impact of different interpretations of system parameter uncertainties on the calculation of covariances and correlations of k{sub eff} is discussed in this article. Following the suggestion of the OECD/NEA EGUACSA Benchmark phase IV [1], 21 experiments from the ICSBEP international Handbook of Evaluated Criticality Safety Benchmarks [2] are used for this analysis (LEU-COMP-THERM series 07, exp. 01-04 and 39, exp. 01-17). The experiments share certain assembly and fuel features and were carried out in the same lab. Therefore, correlations in the calculation of k{sub eff} appear. Correlation coefficients between experiments due to nuclear data uncertainties are calculated with TSUNAMI [3] and show high values for most experiments. For the correlation analysis due to system parameter uncertainties mainly two scenarios are considered: In scenario A all uncertain parameters are varied mutually for all fuel rods, representing a systematic deviation of the actual experimental setup from the design value. This analysis is performed for all experiments listed above. In scenario E certain parameters like the outer cladding diameter are varied for each fuel rod individually, representing a random scattering of the varied parameter. Scenarios B, C and D represent intermediate scenarios between A and E, which are discussed shortly. Uncertainty and sensitivity calculations of k{sub eff} on the uncertain experimental parameters based on Monte Carlo sampling are presented and discussed. The calculations were performed with SUnCISTT (Sensitivities and Uncertainties in Criticality Inventory and Source Term Tool [4]) and SCALE 6.1.2 [3]. For the 21 EGUACSA proposed experiments the outer cladding diameter is the leading effect for the uncertainty of k{sub eff} for scenario A. In contrast, for scenario E the impact of the cladding diameter on k{sub eff} cancels out due to rod wise variations leaving the fuel diameter as the leading effect. The correlation analysis based on system parameter uncertainties indicates a significant dependence of the correlation coefficients on the interpretation of the uncertainties. It shows high values for scenario A, if the neutronic features are similar (EALF, H/U). For scenario E the overall correlations are significantly smaller and variations between experiments decrease. The impact of the determined correlations on bias calculations of application cases will be discussed by the authors in the accompanying paper 'Impact of correlated data in validation procedures'. (authors)

42 ENGINEERING↗