Using CMOS Sensors in a Cellphone for Gamma Detection and Classification

The CMOS camera found in many cellphones is sensitive to ionized electrons. Gamma rays penetrate into the phone and produce ionized electrons that are then detected by the camera. Thermal noise and other noise needs to be removed on the phone, which requires an algorithm that has relatively low memory and computational requirements. The continuous high-delta algorithm described fits those requirements. Only a small fraction of the energy of even the electron is deposited in the camera sensor, so direct methods of measuring the energy cannot be used. The fraction of groups of lit up pixels that are lines is correlated with the energy of the gamma rays. This correlation under certain conditions allows limited low resolution energy resolution to be performed

Determining Camera Gain in Room Temperature Cameras

James R. Janesick provides a method for determining the amplification of a CCD or CMOS camera when only access to the raw images is provided. However, the equation that is provided ignores the contribution of dark current. For CCD or CMOS cameras that are cooled well below room temperature, this is not a problem, however, the technique needs adjustment for use with room temperature cameras. This article describes the adjustment made to the equation, and a test of this method

Application of a Java-based, univel geometry, neutral particle Monte Carlo code to the searchlight problem

A univel geometry, neutral particle Monte Carlo transport code, written entirely in the Java programming language, is under development for medical radiotherapy applications. The code uses ENDF-VI based continuous energy cross section data in a flexible XML format. Full neutron-photon coupling, including detailed photon production and photonuclear reactions, is included. Charged particle equilibrium is assumed within the patient model so that detailed transport of electrons produced by photon interactions may be neglected. External beam and internal distributed source descriptions for mixed neutron-photon sources are allowed. Flux and dose tallies are performed on a univel basis. A four-tap, shift-register-sequence random number generator is used. Initial verification and validation testing of the basic neutron transport routines is underway. The searchlight problem was chosen as a suitable first application because of the simplicity of the physical model. Results show excellent agreement with analytic solutions. Computation times for similar numbers of histories are comparable to other neutron MC codes written in C and FORTRAN

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

PEBBLES: A COMPUTER CODE FOR MODELING PACKING, FLOW AND RECIRCULATIONOF PEBBLES IN A PEBBLE BED REACTOR

A comprehensive, high fidelity model for pebble flow has been developed and embodied in the PEBBLES computer code. In this paper, a description of the physical artifacts included in the model is presented and some results from using the computer code for predicting the features of pebble flow and packing in a realistic pebble bed reactor design are shown. The sensitivity of models to various physical parameters is also discussed

Development Status of the PEBBLES Code for Pebble Mechanics: Improved Physical Models and Speed-up

PEBBLES is a code for simulating the motion of all the pebbles in a pebble bed reactor. Since pebble bed reactors are packed randomly and not precisely placed, the location of the fuel elements in the reactor is not deterministically known. Instead, when determining operating parameters the motion of the pebbles can be simulated and stochastic locations can be found. The PEBBLES code can output information relevant for other simulations of the pebble bed reactors such as the positions of the pebbles in the reactor, packing fraction change in an earthquake, and velocity profiles created by recirculation. The goal for this level three milestone was to speedup the PEBBLES code through implementation on massively parallel computer. Work on this goal has resulted in speeding up both the single processor version and creation of a new parallel version of PEBBLES. Both the single processor version and the parallel running capability of the PEBBLES code have improved since the fiscal year start. The hybrid MPI/OpenMP PEBBLES version was created this year to run on the increasingly common cluster hardware profile that combines nodes with multiple processors that share memory and a cluster of nodes that are networked together. The OpenMP portions use the Open Multi-Processing shared memory parallel processing model to split the task across processors in a single node that shares memory. The Message Passing Interface (MPI) portion uses messages to communicate between different nodes over a network. The following are wall clock speed up for simulating an NGNP-600 sized reactor. The single processor version runs 1.5 times faster compared to the single processor version at the beginning of the fiscal year. This speedup is primarily due to the improved static friction model described in the report. When running on 64 processors, the new MPI/OpenMP hybrid version has a wall clock speed up of 22 times compared to the current single processor version. When using 88 processors, a speed up of 23 times is achieved. This speedup and other improvements of PEBBLES combine to make PEBBLES more capable and more useful for simulation of a pebble bed reactor. This report details the implementation and effects of the speedup work done over the course of the fiscal year

METHODS FOR MODELING THE PACKING OF FUEL ELEMENTS IN PEBBLE BED REACTORS

Two methods for the modeling of the packing of pebbles in the pebble bed reactors are presented and compared. The first method is based on random generation of potential centers for the pebbles, followed by rejection of points that are not compatible with the geometric constraint of no (or limited) pebbles overlap. The second method models the actual physical packing process, accounting for the dynamic of pebbles as they are dropped onto the pebble bed and as they settle therein. A simplification in the latter model is the assumption of a starting point with very dilute packing followed by settling. The results from the two models are compared and the properties of the second model and the dependence of its results on many of the modeling parameters are presented. The first model (with no overlap allowed) has been implemented into a code to compute Dancoff factors. The second model will soon be implemented into that same code and will also be used to model flow of pebbles in a reactor and core densification in the simulation of earthquakes. Both methods reproduce experimental values well, with the latter displaying a high level of fidelity

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

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