DAKOTA reliability methods applied to RAVEN/RELAP-7.

This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR)

An approach based on Support Vector Machines and a K-D Tree search algorithm for identification of the failure domain and safest operating conditions in nuclear systems

The safety of a Nuclear Power Plant (NPP) is verified by analyzing the system responses under normal and accidental conditions. This is done by resorting to a Best-Estimate (BE) Thermal-Hydraulic (TH) code, whose outcomes are compared to given safety thresholds enforced by regulation. This allows identifying the limit-state function that separates the failure domain from the safe domain. In practice, the TH model response is affected by uncertainties (both epistemic and aleatory), which make the limit-state function and the failure domain probabilistic. The present paper sets forth an innovative approach to identify the failure domain together with the safest plant operating conditions. The approach relies on the use of Reduced Order Models (ROMs) and K-D Tree. The model failure boundary is approximated by Support Vector Machines (SVMs) and, then, projected onto the space of the controllable variables (i.e., the model inputs that can be manipulated by the plant operator, such as reactor control-rods position, feed-water flow-rate through the plant primary loops, accumulator water temperature and pressure, repair times, etc.). The farthest point from the failure boundary is, then, computed by means of a K-D Tree-based nearest neighbor algorithm; this point represents the combination of input values corresponding to the safest operating conditions. The approach is shown to give satisfactory results with reference to one analytical example and one real case study regarding the Peak Cladding Temperature (PCT) reached in a Boiling Water Reactor (BWR) during a Station-Black-Out (SBO), simulated using RELAP5-3D

Deployment and Overview of RAVEN capabilities for

Since the Beginning of 2012 Idaho National Labora

Implementation of Stochastic Polynomials Approach in the RAVEN Code

RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, has been tasked to provide the necessary software and algorithms to enable the application of the conceptual framework developed by the Risk Informed Safety Margin Characterization (RISMC) [1] path. RISMC is one of the paths defined under the Light Water Reactor Sustainability (LWRS) DOE program

Multi-Reactor Transmutation Analysis Utility (MRTAU,alpha1): Verification

Multi-Reactor Transmutation Utility (MRTAU) is a general depletion/decay algorithm under development at INL to support quick assessment of off-normal fuel cycle scenarios of similar nature to well studied reactor and fuel cycle concepts for which isotopic and cross-section data exists. MRTAU has been used in the past for scoping calculations to determine actinide composition evolution over the course of multiple recycles in Light Water Reactor Mixed Oxide and Sodium cooled Fast Reactor. In these applications, various actinide partitioning scenarios of interest were considered. The code has recently been expanded to include fission product generation, depletion and isotopic evolution over multiple recycles. The capability was added to investigate potential partial separations and/or limited recycling technologies such as Melt-Refining, AIROX, DUPIC or other fuel recycle technology where the recycled fuel stream is not completely decontaminated of fission products prior to being re-irradiated in a subsequent reactor pass. This report documents the code's solution methodology and algorithm as well as its solution accuracy compared to the SCALE6.0 software suite

RAVEN: Dynamic Event Tree Approach Level III Milestone

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented

REACTOR ANALYSIS AND VIRTUAL CONTROL ENVIRONMENT (RAVEN) FY12 REPORT

RAVEN is a complex software tool that will have tasks spanning from being the RELAP-7 user interface, to using RELAP-7 to perform Risk Informed Safety Characterization (RISMC), and to controlling RELAP-7 calculation execution. The goal of this document is to: 1. Highlight the functional requirements of the different tasks of RAVEN 2. Identify shared functions that could be aggregate in modules so to obtain a minimal software redundancy and maximize software utilization. RAVEN is in fact a software framework that will allow exploiting the following functionalities: • Derive and actuate the control logic required to: o Simulate the plant control system o Simulate the operator (procedure guided) actions o Perform Monte Carlo sampling of random distributed events o Perform event three based analysis • Provide a GUI to: o Input a plant description to RELAP-7 (component, control variable, control parameters) o Concurrent monitoring of Control Parameters o Concurrent alteration of control parameters • Provide Post Processing data mining capability based on o Dimensionality reduction o Cardinality reduction In this document it will be shown how an appropriate mathematical formulation of the control logic and probabilistic analysis leads to have most of the software infrastructure leveraged between the two main tasks. Further, this document will go through the development accomplished this year, including simulation results, and priorities for the next years developmen

PHISICS TOOLKIT: MULTI-REACTOR TRANSMUTATION ANALYSIS UTILITY - MRTAU

The principal idea of this paper is to present the new capabilities available in the PHISICS toolkit, connected with the implementation of the depletion code MRTAU, a generic depletion/ decay/burn-up code developed at the Idaho National Laboratory. It is programmed in a modular structure and modern FORTRAN 95/2003. The code tracks the time evolution of the isotopic concentration of a given material accounting for nuclear reaction happening in presence of neutron flux and also due to natural decay. MRTAU has two different methods to perform the depletion calculation, in order to let the user choose the best one respect his needs. Both the methodologies and some significant results are reported in this paper

A Flooding Induced Station Blackout Analysis for a Pressurized Water Reactor Using the RISMC Toolkit

In this paper we evaluate the impact of a power uprate on a pressurized water reactor (PWR) for a tsunami-induced flooding test case. This analysis is performed using the RISMC toolkit: the RELAP-7 and RAVEN codes. RELAP-7 is the new generation of system analysis codes that is responsible for simulating the thermal-hydraulic dynamics of PWR and boiling water reactor systems. RAVEN has two capabilities: to act as a controller of the RELAP-7 simulation (e.g., component/system activation) and to perform statistical analyses. In our case, the simulation of the flooding is performed by using an advanced smooth particle hydrodynamics code called NEUTRINO. The obtained results allow the user to investigate and quantify the impact of timing and sequencing of events on system safety. In addition, the impact of power uprate is determined in terms of both core damage probability and safety margins

Light Water Reactor Sustainability Program Support and Modeling for the Boiling Water Reactor Station Black Out Case Study Using RELAP and RAVEN

The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated. In order to evaluate the impact of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This report focuses, in particular, on the impact of power uprate on the safety of a boiled water reactor system. The case study considered is a loss of off-site power followed by the loss of diesel generators, i.e., a station black out (SBO) event. Analysis is performed by using a thermo-hydraulic code, i.e. RELAP-5, and a stochastic analysis tool currently under development at INL, i.e. RAVEN. Starting from the event tree models contained in SAPHIRE, we built the input file for RELAP-5 that models in great detail system dynamics under SBO conditions. We also interfaced RAVEN with RELAP-5 so that it would be possible to run multiple RELAP-5 simulation runs by changing specific keywords of the input file. We both employed classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. We also employed advanced data analysis and visualization tools that helped us to correlate simulation outcome such as maximum core temperature with a set of input uncertain parameters. Results obtained gave a detailed overview of the issues associated to power uprate for a SBO accident scenario. We were able to quantify how timing of safety related events were impacted by a higher reactor core power. Such insights can provide useful material to the decision makers to perform risk-infomed safety margins management