Joshua J. Cogliati

Pebble Bed Reactor Dust Production Model

The operation of pebble bed reactors, including fuel circulation, can generate graphite dust, which in turn could be a concern for internal components; and to the near field in the remote event of a break in the coolant circuits. The design of the reactor system must, therefore, take the dust into account and the operation must include contingencies for dust removal and for mitigation of potential releases. Such planning requires a proper assessment of the dust inventory. This paper presents a predictive model of dust generation in an operating pebble bed with recirculating fuel. In this preliminary work the production model is based on the use of the assumption of proportionality between the dust production and the normal force and distance traveled. The model developed in this work uses the slip distances and the inter-pebble forces computed by the authors’ PEBBLES. The code, based on the discrete element method, simulates the relevant static and kinetic friction interactions between the pebbles as well as the recirculation of the pebbles through the reactor vessel. The interaction between pebbles and walls of the reactor vat is treated using the same approach. The amount of dust produced is proportional to the wear coefficient for adhesive wear (taken from literature) and to the slip volume, the product of the contact area and the slip distance. The paper will compare the predicted volume with the measured production rates. The simulation tallies the dust production based on the location of creation. Two peak production zones from intra pebble forces are predicted within the bed. The first zone is located near the pebble inlet chute due to the speed of the dropping pebbles. The second peak zone occurs lower in the reactor with increased pebble contact force due to the weight of supported pebbles. This paper presents the first use of a Discrete Element Method simulation of pebble bed dust production.

BWR Station Blackout: A RISMC Analysis Using RAVEN and RELAP5-3D

Abstract The existing fleet of nuclear power plants is in the process of having its lifetime extended and having the power generated from these plants increased via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) pathway aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess the impact of power uprate of a boiling water reactor system during a station blackout accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN, which performs the stochastic analysis. Our analysis is performed by (a) sampling values from a set of parameters from the uncertainty space of interest, (b) simulating the system behavior for that specific set of parameter values, and (c) analyzing the outcomes from the set of simulation runs.

Pebble-bed pebble motion: Simulation and Applications

This dissertation presents a method for simulation of motion of the pebbles in a PBR. A new mechanical motion simulator, PEBBLES, efficiently simulates the key elements of motion of the pebbles in a PBR. This model simulates gravitational force and contact forces including kinetic and true static friction. Its used for a variety of tasks including simulation of the effect of earthquakes on a PBR, calculation of packing fractions, Dancoff factors, pebble wear and the pebble force on the walls. The simulator includes a new differential static friction model for the varied geometries of PBRs. A new static friction benchmark was devised via analytically solving the mechanics equations to determine the minimum pebble-to-pebble friction and pebble-to-surface friction for a five pebble pyramid. This pyramid check as well as a comparison to the Janssen formula was used to test the new static friction equations. Because larger pebble bed simulations involve hundreds of thousands of pebbles and long periods of time, PEBBLES runs on shared memory architectures and distributed memory architectures. For the shared memory architecture, the code uses a new O(n) lock-less parallel collision detection algorithm to determine which pebbles are likely to be in contact. The PEBBLES code provides new capabilities for understanding and optimizing PBRs. The PEBBLES code has provided the pebble motion data required to calculate the motion of pebbles during a simulated earthquake. The PEBBLES code provides the ability to determine the contact forces and the lengths of motion in contact. This information combined with the proper wear coefficients can be used to determine the dust production from mechanical wear. These new capabilities enhance the understanding of PBRs, and the capabilities of the code will allow future improvements in understanding.

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.

AGR-1 Depletion Benchmark

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Light Water Reactor Sustainability Program Status of Adaptive Surrogates within the RAVEN framework

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RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN has now increased in maturity enough for the Beta 1.0 release.

Software infrastructure progress in the RAVEN code

The milestones have been achieved. RAVEN has been migrated to Gitlab which adds new abilities for code review and management. Standalone RAVEN framework packages have been created for OSX and two Linux distributions.

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.

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.