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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗

NASA NTRS2005

Wireless Acoustic Measurement System

A prototype wireless acoustic measurement system (WAMS) is one of two main subsystems of the Acoustic Prediction/Measurement Tool, which comprises software, acoustic instrumentation, and electronic hardware combined to afford integrated capabilities for predicting and measuring noise emitted by rocket and jet engines. The other main subsystem is described in "Predicting Rocket or Jet Noise in Real Time" (SSC-00215-1), which appears elsewhere in this issue of NASA Tech Briefs. The WAMS includes analog acoustic measurement instrumentation and analog and digital electronic circuitry combined with computer wireless local-area networking to enable (1) measurement of sound-pressure levels at multiple locations in the sound field of an engine under test and (2) recording and processing of the measurement data. At each field location, the measurements are taken by a portable unit, denoted a field station. There are ten field stations, each of which can take two channels of measurements. Each field station is equipped with two instrumentation microphones, a micro-ATX computer, a wireless network adapter, an environmental enclosure, a directional radio antenna, and a battery power supply. The environmental enclosure shields the computer from weather and from extreme acoustically induced vibrations. The power supply is based on a marine-service lead-acid storage battery that has enough capacity to support operation for as long as 10 hours. A desktop computer serves as a control server for the WAMS. The server is connected to a wireless router for communication with the field stations via a wireless local-area network that complies with wireless-network standard 802.11b of the Institute of Electrical and Electronics Engineers. The router and the wireless network adapters are controlled by use of Linux-compatible driver software. The server runs custom Linux software for synchronizing the recording of measurement data in the field stations. The software includes a module that provides an intuitive graphical user interface through which an operator at the control server can control the operations of the field stations for calibration and for recording of measurement data. A test engineer positions and activates the WAMS. The WAMS automatically establishes the wireless network. Next, the engineer performs pretest calibrations. Then the engineer executes the test and measurement procedures. After the test, the raw measurement files are copied and transferred, through the wireless network, to a hard disk in the control server. Subsequently, the data are processed into 1/3-octave spectrograms.

Anderson, Paul D.↗

NASA NTRS2007

Wireless Acoustic Measurement System

A prototype wireless acoustic measurement system (WAMS) is one of two main subsystems of the Acoustic Prediction/ Measurement Tool, which comprises software, acoustic instrumentation, and electronic hardware combined to afford integrated capabilities for predicting and measuring noise emitted by rocket and jet engines. The other main subsystem is described in the article on page 8. The WAMS includes analog acoustic measurement instrumentation and analog and digital electronic circuitry combined with computer wireless local-area networking to enable (1) measurement of sound-pressure levels at multiple locations in the sound field of an engine under test and (2) recording and processing of the measurement data. At each field location, the measurements are taken by a portable unit, denoted a field station. There are ten field stations, each of which can take two channels of measurements. Each field station is equipped with two instrumentation microphones, a micro- ATX computer, a wireless network adapter, an environmental enclosure, a directional radio antenna, and a battery power supply. The environmental enclosure shields the computer from weather and from extreme acoustically induced vibrations. The power supply is based on a marine-service lead-acid storage battery that has enough capacity to support operation for as long as 10 hours. A desktop computer serves as a control server for the WAMS. The server is connected to a wireless router for communication with the field stations via a wireless local-area network that complies with wireless-network standard 802.11b of the Institute of Electrical and Electronics Engineers. The router and the wireless network adapters are controlled by use of Linux-compatible driver software. The server runs custom Linux software for synchronizing the recording of measurement data in the field stations. The software includes a module that provides an intuitive graphical user interface through which an operator at the control server can control the operations of the field stations for calibration and for recording of measurement data. A test engineer positions and activates the WAMS. The WAMS automatically establishes the wireless network. Next, the engineer performs pretest calibrations. Then the engineer executes the test and measurement procedures. After the test, the raw measurement files are copied and transferred, through the wireless network, to a hard disk in the control server. Subsequently, the data are processed into 1.3-octave spectrograms.

Anderson, Paul D.↗

DOE OSTI2024

Forensic Analysis of SOHO Router Binaries

Small Office/Home Office (SOHO) routers are used by millions of consumers across the United States, and are commensurately vulnerable. Forensic analysis of SOHO router firmware helps to understand and mitigate those vulnerabilities. This poster focused particularly on analysis of BusyBox executables, a software suite that provides several Unix utilities in a single file. Three main tools were used to analyze the binaries. BinWalk was used to extract the files, but also to build entropy graphs, extract Linux kernel images, and identify CPU architectures; WiiBin processed the binaries to find endianness, architecture, the percent compressed/encrypted, and compiler data; and @DisCo, a machine learning tool used to determine function similarity in disassembled binaries, analyzed similarities and determined versions of extracted BusyBox files from each router. These tools found that venders from all five routers utilized the same version of the BusyBox software across different firmware updates, demonstrating the importance of constant firmware scrutiny to protect against security vulnerabilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2012

TraceContract

TraceContract is an API (Application Programming Interface) for trace analysis. A trace is a sequence of events, and can, for example, be generated by a running program, instrumented appropriately to generate events. An event can be any data object. An example of a trace is a log file containing events that a programmer has found important to record during a program execution. Trace - Contract takes as input such a trace together with a specification formulated using the API and reports on any violations of the specification, potentially calling code (reactions) to be executed when violations are detected. The software is developed as an internal DSL (Domain Specific Language) in the Scala programming language. Scala is a relatively new programming language that is specifically convenient for defining such internal DSLs due to a number of language characteristics. This includes Scala s elegant combination of object-oriented and functional programming, a succinct notation, and an advanced type system. The DSL offers a combination of data-parameterized state machines and temporal logic, which is novel. As an extension of Scala, it is a very expressive and convenient log file analysis framework.

Kavelund, Klaus↗

NASA NTRS2012

MPST Software: MoonKommand

This software automatically processes Sally Ride Science (SRS) delivered MoonKAM camera control files (ccf) into uplink products for the GRAIL-A and GRAIL-B spacecraft as part of an education and public outreach (EPO) extension to the Grail Mission. Once properly validated and deemed safe for execution onboard the spacecraft, MoonKommand generates the command products via the Automated Sequence Processor (ASP) and generates uplink (.scmf) files for radiation to the Grail-A and/or Grail-B spacecraft. Any errors detected along the way are reported back to SRS via email. With Moon Kommand, SRS can control their EPO instrument as part of a fully automated process. Inputs are received from SRS as either image capture files (.ccficd) for new image requests, or downlink/delete files (.ccfdl) for requesting image downlink from the instrument and on-board memory management. The Moon - Kommand outputs are command and file-load (.scmf) files that will be uplinked by the Deep Space Network (DSN). Without MoonKommand software, uplink product generation for the MoonKAM instrument would be a manual process. The software is specific to the Moon - KAM instrument on the GRAIL mission. At the time of this writing, the GRAIL mission was making final preparations to begin the science phase, which was scheduled to continue until June 2012.

Kwok, John H.↗

NASA NTRS2013

Predicting Operator Execution Times Using CogTool

Researchers and developers of NextGen systems can use predictive human performance modeling tools as an initial approach to obtain skilled user performance times analytically, before system testing with users. This paper describes the CogTool models for a two pilot crew executing two different types of a datalink clearance acceptance tasks, and on two different simulation platforms. The CogTool time estimates for accepting and executing Required Time of Arrival and Interval Management clearances were compared to empirical data observed in video tapes and registered in simulation files. Results indicate no statistically significant difference between empirical data and the CogTool predictions. A population comparison test found no significant differences between the CogTool estimates and the empirical execution times for any of the four test conditions. We discuss modeling caveats and considerations for applying CogTool to crew performance modeling in advanced cockpit environments.

Santiago-Espada, Yamira↗

NASA NTRS2008

Runtime Verification of C Programs

We present in this paper a framework, RMOR, for monitoring the execution of C programs against state machines, expressed in a textual (nongraphical) format in files separate from the program. The state machine language has been inspired by a graphical state machine language RCAT recently developed at the Jet Propulsion Laboratory, as an alternative to using Linear Temporal Logic (LTL) for requirements capture. Transitions between states are labeled with abstract event names and Boolean expressions over such. The abstract events are connected to code fragments using an aspect-oriented pointcut language similar to ASPECTJ's or ASPECTC's pointcut language. The system is implemented in the C analysis and transformation package CIL, and is programmed in OCAML, the implementation language of CIL. The work is closely related to the notion of stateful aspects within aspect-oriented programming, where pointcut languages are extended with temporal assertions over the execution trace.

RMOR↗

DOE OSTI2021

Scalable augmented enumeration and metadata operations for large filesystems

Systems, apparatus, and methods are disclosed for performing scalable operations in a file system. Metadata entries in a namespace or directory tree are sharded across multiple file metadata servers. An augmented enumeration operation, such as listing a directory, is parallelized across the multiple file metadata servers, transparently to clients. Exemplary augmentation features can include filtering and sorting. Augmentation features can be executed concurrently with enumeration, prior to enumeration, after enumeration, or as a combination of these, and can utilize pre-built index structures or holding structures for intermediate results. Augmented enumeration operations can also include no-output operations such as changing file attributes or deleting a file, and cumulative operations such as counting total disk space usage. The parallelization is compatible with tree-level parallelization and storage-level parallelization. Disclosed technologies can be applied to other fields requiring scalable enumeration, such as database and network applications.

Grider, Gary A.↗

NASA NTRS1996

Air Traffic Complexity Measurement Environment (ACME): Software User's Guide

A user's guide for the Air Traffic Complexity Measurement Environment (ACME) software is presented. The ACME consists of two major components, a complexity analysis tool and user interface. The Complexity Analysis Tool (CAT) analyzes complexity off-line, producing data files which may be examined interactively via the Complexity Data Analysis Tool (CDAT). The Complexity Analysis Tool is composed of three independently executing processes that communicate via PVM (Parallel Virtual Machine) and Unix sockets. The Runtime Data Management and Control process (RUNDMC) extracts flight plan and track information from a SAR input file, and sends the information to GARP (Generate Aircraft Routes Process) and CAT (Complexity Analysis Task). GARP in turn generates aircraft trajectories, which are utilized by CAT to calculate sector complexity. CAT writes flight plan, track and complexity data to an output file, which can be examined interactively. The Complexity Data Analysis Tool (CDAT) provides an interactive graphic environment for examining the complexity data produced by the Complexity Analysis Tool (CAT). CDAT can also play back track data extracted from System Analysis Recording (SAR) tapes. The CDAT user interface consists of a primary window, a controls window, and miscellaneous pop-ups. Aircraft track and position data is displayed in the main viewing area of the primary window. The controls window contains miscellaneous control and display items. Complexity data is displayed in pop-up windows. CDAT plays back sector complexity and aircraft track and position data as a function of time. Controls are provided to start and stop playback, adjust the playback rate, and reposition the display to a specified time.

Source record↗

DOE OSTI2020

A Framework for the Analysis of Compiler Optimizations

Compilers transform program source code to machine executable code. During this transformation, they perform a number of compiler optimizations to improve the performance of the generated executable code. Importantly, applying those optimizations depends on the source code structure, such as the parallel programming model used to parallelize an algorithm. Often, implementations of the same algorithm with different programming models have vastly different performance because the compiler optimized them differently. We create FAROS, a framework to structure and automate the analysis of compiler optimizations on programs. FAROS automates the building process, execution profiling, and analysis of compiler optimization of programs, through a configuration interface. It outputs compiler optimization reports to show which optimizations applied to which line of source code, leveraging compilation remarks output by the compiler. Also, FAROS supports benchmarking performance of different program versions by collecting execution time results. In this first release of FAROS, we provide a configuration file to analyze compiler optimization differences for sequential vs OpenMP compilation, including 38 programs consisting of HPC proxy/mini/large applications, and NAS and Rodina kernels for analysis.

Georgakoudis, Giorgis↗

DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1973

Small Interactive Image Processing System (SMIPS) system description

The Small Interactive Image Processing System (SMIPS) operates under control of the IBM-OS/MVT operating system and uses an IBM-2250 model 1 display unit as interactive graphic device. The input language in the form of character strings or attentions from keys and light pen is interpreted and causes processing of built-in image processing functions as well as execution of a variable number of application programs kept on a private disk file. A description of design considerations is given and characteristics, structure and logic flow of SMIPS are summarized. Data management and graphic programming techniques used for the interactive manipulation and display of digital pictures are also discussed.

Moik, J. G.↗

NASA NTRS2016

The Trick Simulation Toolkit: A NASA/Opensource Framework for Running Time Based Physics Models

The Trick Simulation Toolkit is a simulation development environment used to create high fidelity training and engineering simulations at the NASA Johnson Space Center and many other NASA facilities. Its purpose is to generate a simulation executable from a collection of user-supplied models and a simulation definition file. For each Trick-based simulation, Trick automatically provides job scheduling, numerical integration, the ability to write and restore human readable checkpoints, data recording, interactive variable manipulation, a run-time interpreter, and many other commonly needed capabilities. This allows simulation developers to concentrate on their domain expertise and the algorithms and equations of their models. Also included in Trick are tools for plotting recorded data and various other supporting utilities and libraries. Trick is written in C/C++ and Java and supports both Linux and MacOSX computer operating systems. This paper describes Trick's design and use at NASA Johnson Space Center.

Penn, John M.↗

NASA NTRSDate not supplied

Ground Segment Operations Concept for the Orion Artemis-2 Optical Communications System

The ACCESS Project (formerly Space Network) will implement an optical communications ground segment to support the Orion Artemis II Optical Communications (O2O) demonstration as part of the next manned human spaceflight mission to the moon, Artemis II. O2O implements laser communication (lasercomm) technology for operational use on the Orion series of spacecraft, as a development test objective (DTO), in order to demonstrate the feasibility and operational utility of lasercomm for human spaceflight missions. O2O consists of three segments: Space Segment, Ground Segment, and Operations Segment. The Space Segment consists of the Space Terminal Element and the Orion spacecraft. The Space Terminal Element effort is managed by the GSFC Laser-Enhanced Mission Communications Navigation and Operational Services (LEMNOS) project in collaboration with MIT Lincoln Laboratory. The Ground Segment consists of an optical ground terminal (GT) at the White Sands Complex (WSC), which is being developed in collaboration with MIT Lincoln Laboratory, the Ground Segment Operations and Analysis (GSOA) element and Ground Data Element (GDE), and a second optical GT in the Optical Communications Telescope Laboratory (OCTL) at the JPL Table Mountain Facility. The Operations Segment consists of the Artemis II Mission Control Center (MCC), the Lasercomm Space Terminal Console (LSTC), and the Lasercomm Link Planning & Analysis Center (LPAC), all located at the Johnson Space Center (JSC). O2O utilizes pulse-position modulation (PPM) direct-to-earth services resulting in an 80 Mbps downlink data rate from lunar orbit. The O2O concept of operations is to provide optical services for a minimum of 1 hour per day for each day of the Artemis II mission. O2O will utilize a 10-20 Mbps uplink data rate and 40-260 Mbps downlink data rate, depending on the Artemis II mission phase. The ACCESS project will also provide a centralized mission data interface for user data distribution and storage to the MCC and perform planning and scheduling of services in coordination with the Operations Segment for the O2O Ground Segment. The O2O Ground Segment will support the following O2O mission phases: Pre-Mission Planning; Daily Operations Planning; Event Execution; and Post-Pass Reporting. O2O will be used to exchange data files between Orion and the MCC and to distribute real-time video through the optical downlink service to the MCC; which would not be possible without the high-bandwidth link that O2O will provide to Orion. In this paper, I will discuss the O2O Ground Segment development approach and how it will support these critical O2O functions: plan and schedule the contact; acquire and track the optical link; flow information bidirectionally; distribute information; and control and accommodate the system.

optical communications↗

NASA NTRS2007

Automatic Command Sequence Generation

Automatic Sequence Generator (Autogen) Version 3.0 software automatically generates command sequences for the Mars Reconnaissance Orbiter (MRO) and several other JPL spacecraft operated by the multi-mission support team. Autogen uses standard JPL sequencing tools like APGEN, ASP, SEQGEN, and the DOM database to automate the generation of uplink command products, Spacecraft Command Message Format (SCMF) files, and the corresponding ground command products, DSN Keywords Files (DKF). Autogen supports all the major multi-mission mission phases including the cruise, aerobraking, mapping/science, and relay mission phases. Autogen is a Perl script, which functions within the mission operations UNIX environment. It consists of two parts: a set of model files and the autogen Perl script. Autogen encodes the behaviors of the system into a model and encodes algorithms for context sensitive customizations of the modeled behaviors. The model includes knowledge of different mission phases and how the resultant command products must differ for these phases. The executable software portion of Autogen, automates the setup and use of APGEN for constructing a spacecraft activity sequence file (SASF). The setup includes file retrieval through the DOM (Distributed Object Manager), an object database used to store project files. This step retrieves all the needed input files for generating the command products. Depending on the mission phase, Autogen also uses the ASP (Automated Sequence Processor) and SEQGEN to generate the command product sent to the spacecraft. Autogen also provides the means for customizing sequences through the use of configuration files. By automating the majority of the sequencing generation process, Autogen eliminates many sequence generation errors commonly introduced by manually constructing spacecraft command sequences. Through the layering of commands into the sequence by a series of scheduling algorithms, users are able to rapidly and reliably construct the desired uplink command products. With the aid of Autogen, sequences may be produced in a matter of hours instead of weeks, with a significant reduction in the number of people on the sequence team. As a result, the uplink product generation process is significantly streamlined and mission risk is significantly reduced. Autogen is used for operations of MRO, Mars Global Surveyor (MGS), Mars Exploration Rover (MER), Mars Odyssey, and will be used for operations of Phoenix. Autogen Version 3.0 is the operational version of Autogen including the MRO adaptation for the cruise mission phase, and was also used for development of the aerobraking and mapping mission phases for MRO.

Fisher, Forest↗

DOE OSTI2023

Model Data for the Mesh Convergence Study Demonstrating Benefits of Mixed-polyhedral Mesh in Integrated Hydrology Simulations

This archived model data is related to a study introducing a unique method that employs a stream-aligned mixed-polyhedral mesh to effectively and accurately represent river valleys, stream corridors, and narrow engineered channels in integrated hydrology simulations. The study finds that utilizing stream-aligned mixed-polyhedral meshes in integrated hydrology simulations achieves accuracy on par with a finely refined TIN-based mesh while markedly diminishing computational costs. This archive contains scripts and data files needed to generate the ATS model input, including mesh and ATS input files, for all mesh scenarios using the Watershed Workflow package. Additionally, this archive also provides key outputs from the model simulations that are used in the analysis and post-processing scripts to reproduce figures in the manuscript. The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Demonstration of the On-the-Fly Shielding Analysis Method: Spent Fuel and Waste Disposition

This report documents work performed supporting the US Department of Energy (DOE) Office of Nuclear Energy (NE) Spent Fuel and Waste Disposition (SFWD) Integrated Waste Management activities under work breakdown structure element 1.08.02.04.01, “Data and Tools Development, Validation, and Maintenance.” In particular, this report fulfills milestone M3SF-21OR020401016, “Implement on-the-fly dose analysis methodology in UNF-ST&DARDS” within work package SF-21OR02040101, “Commercial SNF Characterization - ORNL.” The Used Nuclear Fuel - Storage, Transportation & Disposal Analysis Resource and Data System (UNFST& DARDS) enables automated dose rate calculations for spent nuclear fuel (SNF) transportation packages and storage casks using a Monte Carlo radiation transport code. The explicit method uses a detailed model of the SNF system and its contents. Therefore, a dose rate calculation is required for each as-loaded transportation package or storage cask because the SNF assemblies within a canister typically have unique irradiation characteristics. An alternate method, referred to as the “on-the-fly” shielding analysis method, has been proposed that requires only a set of Monte Carlo dose rate calculations for each transportation packaging/storage cask design. The results of the Monte Carlo dose rate calculations are independent of the SNF assembly irradiation and decay characteristics. The dose rate values may then be combined with the radiation source strength of the SNF assemblies associated with a particular transportation packaging/storage cask design to determine actual dose rates. This report presents on-the-fly dose rate calculations for a representative SNF storage cask and verification of the on-the-fly dose rate calculation results by comparison with reference dose rate calculations using the explicit Monte Carlo dose rate calculation. The on-the-fly shielding analysis method was implemented in UNF-ST&DARDS. A Python program was developed to process the MAVRIC dose rate results obtained by source particle type, energy group, and fuel geometry region. A Python processor created binary files, which were saved as a special UNF-ST&DARDS library for on-the- fly shielding analyses. UNF-ST&DARDS uses the precalculated on-the-fly binary libraries generated by the Python data processor and directly executes the Python code for on-the-fly dose analysis. This Python code unzips the pre-generated binary files mentioned above, reads the data, and combines them with user-specified sources for dose and uncertainty calculations. The Python programs were verified using Excel calculations and by comparison with the values obtained with the MAVRIC post-processing utilities applied to the 3dmap files. This method can currently be used to determine dose rates for as-loaded HI-STORM FW storage casks. The UNF-ST&DARDS analysis wizard for on-the-fly shielding analysis is described in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗