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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

RMIT AR Android Application Abstract

This summer, I've worked as an intern for IT-C1's Rapid Model Import Tool (RMIT) project under Bill Little. The goal of this project was to develop a mobile application for IT-C1's larger project, RMIT, so that it could be deployed in a convenient way. The current function of RMIT is to import file types that are not normally compatible with the Unity 3D software development engine and make them into files that can be placed into Unity for the specific purpose of viewing CAD models in virtual reality (VR). The mobile application that I worked on uses augmented reality (AR), using the camera on a mobile device, to show virtual models in the real world for the purpose of visualization and understanding. In addition to single-player capabilities, this application will include a multi-player mode so that collaborating engineers can see each other's work on their own devices.Working on this application required many steps. First, I hard to learn a lot about how mobile development is different from desktop development, and what the limitations of augmented reality are at its current state. From there, I was assigned new tasks and goals every two weeks, which included addingground plane and air plane functionality, designing a user interface, adding multi-player capability and allowing the user to manipulate models placed in a scene.

Dorian Thompson↗
NASA NTRS1993

Operation of the helicopter antenna radiation prediction code

HARP is a front end as well as a back end for the AMC and NEWAIR computer codes. These codes use the Method of Moments (MM) and the Uniform Geometrical Theory of Diffraction (UTD), respectively, to calculate the electromagnetic radiation patterns for antennas on aircraft. The major difficulty in using these codes is in the creation of proper input files for particular aircraft and in verifying that these files are, in fact, what is intended. HARP creates these input files in a consistent manner and allows the user to verify them for correctness using sophisticated 2 and 3D graphics. After antenna field patterns are calculated using either MM or UTD, HARP can display the results on the user's screen or provide hardcopy output. Because the process of collecting data, building the 3D models, and obtaining the calculated field patterns was completely automated by HARP, the researcher's productivity can be many times what it could be if these operations had to be done by hand. A complete, step by step, guide is provided so that the researcher can quickly learn to make use of all the capabilities of HARP.

Braeden, E. W.↗
DOE OSTI2024

Photonuclear Reaction Types Map to ENDF Reaction Type Numbers MT

In the ENDF format, photonuclear cross secCons are stored in File = 3 under the NSUB = 0 sub-library. The type of reacCon data and the products resulCng from that reacCon are idenCfied by an integer number from 1 to 999, called MT number. However, the ENDF manual is wriPen with a strong focus on neutron induced reacCons and the meaning of MT numbers in the context of photonuclear reacCons can be at Cmes confusing and the assessment of evaluated photonuclear data may require summing various MT numbers to obtain the desired cross secCons to compare with experimental data. An important step in assessing the quality of evaluated data for photonuclear cross secCons is therefore to clarify the mapping between MT numbers and the nomenclature used to describe photonuclear reacCons in the literature. The intent of this document is to provide such a map.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Specifications of FIPD Fission Gas Release Data

All fission gas release data stored in the Fuels Irradiation & Physics Database (FIPD) was originally measured using the Gas Assay, Sample and Recharge (GASR) System in the Hot Fuel Examination Facility (HFEF). It is therefore called GASR data in FIPD. During the measurement of a sample, such as an irradiated EBR-II fuel element/capsule, a pinhole-sized region near the top of the element plenum was melted by a laser. Plenum gas then expanded into a calibrated volume (note: in this document, “sample” and “capsule/element” are used interchangeably consistent with GASR documents in FIPD). The pressure rise in the volume was recorded. Helium backfilling and expansion was then performed to determine the sample (e.g., fuel element plenum) volume using Boyle’s Law and assuming ideal gas behavior at constant temperature. With the plenum volume and the recorded pressure rise, the sample (e.g., fuel element plenum) pressure was derived with assumption of ideal gas law behavior. The plenum volume and pressure as well as the cladding temperature during the measurement were collected (GASR data in FIPD). Other records associated with the fission gas release data include: raw GASR data records including volumes and post-puncture pressures of seal head/sealing head and manifold, calibration data, backfilling gas pressure data, and the data analysis records. A sample(s) of the fission gas released from the plenum was collected by the GASR system into sample bottles. The chemical and isotopic composition of the gas sample was analyzed separately from GASR data, and will be discussed in a separate specification. The plenum volume, pressure, and cladding temperature during the measurement are typically utilized to determine the number of moles of gas in the plenum. This quantity is often compared to the number of moles of gas generated by fission events. However, calculating these values and their associated uncertainty is beyond the scope of this document, as it necessitates additional assumptions. The most important document to understand the FIPD fission gas data is the GASR operational manual (title: Gas Assay, Sample and Recharge System (GASR) operation and maintenance manual, HFEF/N OMM 4381, DOC. NO. W0018-0032-ES-00). This manual provides: (1) description of the GASR and the functions of each component (laser drilling, welding, seal head/sealing head, manifold, vacuum system, sample system, purge and gas tag system, etc.); (2) step-by-step guidance on calibrations, operations, and measurements; and (3) maintenance procedures and other details relating to the structure and operation of the GASR. Note that the original GASR operated until 2020. A new GASR with the same design and measurement methodology was installed in 2021. The specifications of the GASR presented on the HFEF website at this time are consistent with the ones given in the operational manual. The methods to calculate the plenum volume and pressures were not included in the operational manual, but were recorded in the legacy data analysis files. Details of the methods are given in Chapter 3.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

GCAM-Demeter LU dataset

The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

GCAM-Demeter-LU

This GCAM-Demeter-LU is made available under the Open Data Commons Attribution License:http://opendatacommons.org/licenses/by/1.0/. The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.See https://github.com/JGCRI/chen_et_al_2020a for details on how to reproduce this data. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required. To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program Title:tapir CPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1 Developer's repository link:https://gitlab.com/tapir-devs/tapir Licensing provisions: GPLv3 Programming language:python 3, C++ Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions. Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated. Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible. [1]B. Nickel, D. Meiron, G.A.J. Baker, Compilation of 2-pt and 4-pt graphs for continuous spin model, Report, University of Guelph, 1977.[2]A. Pak, J. Phys. Conf. Ser. 368 (2012) 012049, https://doi.org/10.1088/1742-6596/368/1/012049, arXiv:1111.0868.[3]B. Ruijl, T. Ueda, J. Vermaseren, FORM version 4.2, arXiv:1707.06453, 7 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

JGI Archive and Metadata Organizer (JAMO) v2.0.0

JAMO (JGI Archive and Metadata Organizer) helps researchers keep large collections of scientific files organized, findable, and safe. It lets you submit files with consistent, template-driven metadata, bundle related files into sets, and track them as a group instead of one by one. As data ages, JAMO automatically moves it from fast disk to cost-saving tape and can bring it back when needed, keeping storage lean without losing access. A simple web/CLI workflow supports submitting, checking status, retrying, and updating metadata. Compared with generic storage, JAMO's strengths are: clear, searchable metadata tuned for science; set-level organization that mirrors real projects; and built-in lifecycle care (archive, purge, restore) so you don't have to manage those steps yourself.

Cassol, Daniela [Lawrence Berkeley National Labora↗
NASA NTRS2011

Onboard Short Term Plan Viewer

Onboard Short Term Plan Viewer (OSTPV) is a computer program for electronic display of mission plans and timelines, both aboard the International Space Station (ISS) and in ISS ground control stations located in several countries. OSTPV was specifically designed both (1) for use within the limited ISS computing environment and (2) to be compatible with computers used in ground control stations. OSTPV supplants a prior system in which, aboard the ISS, timelines were printed on paper and incorporated into files that also contained other paper documents. Hence, the introduction of OSTPV has both reduced the consumption of resources and saved time in updating plans and timelines. OSTPV accepts, as input, the mission timeline output of a legacy, print-oriented, UNIX-based program called "Consolidated Planning System" and converts the timeline information for display in an interactive, dynamic, Windows Web-based graphical user interface that is used by both the ISS crew and ground control teams in real time. OSTPV enables the ISS crew to electronically indicate execution of timeline steps, launch electronic procedures, and efficiently report to ground control teams on the statuses of ISS activities, all by use of laptop computers aboard the ISS.

Hall, Tim↗
NASA NTRSDate not supplied

Modeling Radiation Sources for Experimental Validation

To build on previously completed experimental work, I have been working to implement a model of our lab’s Americium-Beryllium neutron source and Ludlum detection instruments computationally to provide secondary verification of our experimental results. This has been done using the radiation transport code MCNP6 (Monte Carlo N-Particle). I have developed files to calculate the mass ratio of various composite materials that we have exposed to the radiation source, as well as the components of the detection instruments and converted all of these into geometric coordinates that define the experimental setup. Identifying the proper energy distribution and detector efficiencies as well as the correct interpretation of the detector physics have proven to be the most difficult and time-consuming challenges of the work. It is currently too early for results based on the most updated and accurate models. With this experience, the next step is to develop another source model of a proton beam to simulate exposures on other composite targets. This will be developed based on existing experimental datasets, but not used to validate them. Each of these projects will further define the radiation shielding needs for humans in space radiation environments that astronauts will someday be exposed to beyond Low Earth Orbit.

Juliana Simon↗
NASA NTRS2001

The New Cloud Absorption Radiometer (CAR) Software: One Model for NASA Remote Sensing Virtual Instruments

The Cloud Absorption Radiometer (CAR) instrument has been the most frequently used airborne instrument built in-house at NASA Goddard Space Flight Center, having flown scientific research missions on-board various aircraft to many locations in the United States, Azores, Brazil, and Kuwait since 1983. The CAR instrument is capable of measuring scattered light by clouds in fourteen spectral bands in UV, visible and near-infrared region. This document describes the control, data acquisition, display, and file storage software for the new version of CAR. This software completely replaces the prior CAR Data System and Control Panel with a compact and robust virtual instrument computer interface. Additionally, the instrument is now usable for the first time for taking data in an off-aircraft mode. The new instrument is controlled via a LabVIEW v5. 1.1-developed software interface that utilizes, (1) serial port writes to write commands to the controller module of the instrument, and (2) serial port reads to acquire data from the controller module of the instrument. Step-by-step operational procedures are provided in this document. A suite of other software programs has been developed to complement the actual CAR virtual instrument. These programs include: (1) a simulator mode that allows pretesting of new features that might be added in the future, as well as demonstrations to CAR customers, and development at times when the instrument/hardware is off-location, and (2) a post-experiment data viewer that can be used to view all segments of individual data cycles and to locate positions where 'start' and stop' byte sequences were incorrectly formulated by the instrument controller. The CAR software described here is expected to be the basis for CAR operation for many missions and many years to come.

Roth, Don J.↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2026

Performance assessment of PHITS simulations for the inverse-kinematic p( 7 Li,n) 7 Be reaction based on fast-neutron measurements with a diamond detector

The inverse-kinematics p( 7 Li,n) 7 Be reaction produces forward-focused neutron emission, offering enhanced usable flux and reduced shielding requirements. Reliable simulation of such neutron fields is essential for the development of compact accelerator-based neutron sources. In this study, a PHITS-based simulation framework for the reaction was experimentally assessed using fast-neutron measurements. Forward-directed neutrons were measured with a diamond neutron detector and quantitatively compared with simulations with newly prepared IK-Frag cross-section file based on the proton-induced reaction data in ENDF/B-VIII.1, TENDL-2023, and JENDL-4.0/HE. Measurements and simulations were performed for incident 7 Li 3+ energies ranging from 15.0 to 25.0 MeV using a 50 μm-thick polypropylene target. For all conditions, the PHITS-based simulation framework reproduced the deposited energy spectra at the correct order of magnitude. The comparison of deposited energy spectra in the diamond detector showed high correlation coefficients across all investigated energies, indicating reasonable agreement in spectral shape between simulations and measurements. This work represents an initial step toward establishing a benchmark for PHITS simulations of the inverse kinematic reaction between an incident lithium-ion and a proton target.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
NASA NTRS2019

Development and Analysis of the Automated Object Reentry Survival Analysis Tool Parametric Study Wrapper

The NASA Orbital Debris Program Office (ODPO) Safety Group at the Johnson Space Center analyzes reentering spacecraft at the end of life. The program primarily used by ODPO in this effort is the Object Reentry Survival Analysis Tool (ORSAT). ORSAT utilizes shape primitives as well as a variety of other parameters (material, size, thickness, aerodynamic mass, orbit inclination, etc.) to simulate the reentry process and ultimately, to determine if a spacecraft could be hazardous to the population on the ground. The NASA ODPO plans to automate the ORSAT process to run multiple ORSAT input files either concurrently or consecutively. This type of automation program will provide several benefits. First, there is a need to run large parametric studies for ORSAT analysts to gain a greater understanding of reentering object’s sensitivity to certain input variables. Secondly, a database of pre-run ORSAT cases will be used to develop a survivability model, which could be made available to spacecraft developers as a design for demise (D4D) tool. The recently completed Automated Object Reentry Survival Analysis Tool (AutoORSAT) Wrapper is currently being used to build a survivability database, the first step in developing a survivability model. Already, the data that AutoORSAT has produced provides a greater understanding of the sensitivity of variables such as the initial temperature of the spacecraft, spacecraft breakup altitude, and the aerodynamic mass of spacecraft.

Smith, Andrew N.↗
DOE OSTI2020

Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2000

On Fast Post-Processing of Global Positioning System Simulator Truth Data and Receiver Measurements and Solutions Data

Post-processing of data, related to a GPS receiver test in a GPS simulator and test facility, is an important step towards qualifying a receiver for space flight. Although the GPS simulator provides all the parameters needed to analyze a simulation, as well as excellent analysis tools on the simulator workstation, post-processing is not a GPS simulator or receiver function alone, and it must be planned as a separate pre-flight test program requirement. A GPS simulator is a critical resource, and it is desirable to move off the pertinent test data from the simulator as soon as a test is completed. The receiver and simulator databases are used to extract the test data files for postprocessing. These files are then usually moved from the simulator and receiver systems to a personal computer (PC) platform, where post-processing is done typically using PC-based commercial software languages and tools. Because of commercial software systems generality their functions are notoriously slow and more than often are the bottleneck even for short duration simulator-based tests. There is a need to do post-processing faster and within an hour after test completion, including all required operations on the simulator and receiver to prepare and move off the post-processing files. This is especially significant in order to use the previous test feedback for the next simulation setup or to run near back-to-back simulation scenarios. Solving the post-processing timing problem is critical for a pre-flight test program success. Towards this goal an approach was developed that allows to speed-up post-processing by an order of a magnitude. It is based on improving the post-processing bottleneck function algorithm using a priory information that is specific to a GPS simulation application and using only the necessary volume of truth data. The presented postprocessing scheme was used in support of a few successful space flight missions carrying GPS receivers.

Kizhner, Semion↗