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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated qualification data tool for high temperature metallic materials

This report describes a framework for storing, processing, and displaying qualification data for high temperature mechanical properties. The framework automates the process of generating design data from mechanical test results, for example for a data qualification report for the ASME Boiler \& Pressure Vessel Code. The framework has three parts: a data storage model with common formats for several types of typical mechanical property tests, a backend based on the \pycreep Python library for correlating and extrapolating the data to generate design material properties and allowable stresses, and a demonstration user interface for displaying, sorting, and filtering the data and exploring different options for modeling the design mechanical properties. The report discusses the options available for data processing, with illustrations from real test data on Alloy 617, Alloy 709, Alloy 740H, and Laser-Powder Bed Fusion 316H. The framework is complete for ASME type data analysis and will be used to store test data generated by the Department of Energy, Office of Nuclear Energy, Advanced Materials and Manufacturing Technologies sponsored qualification programs. Future work could extend the tool to other types of material properties and/or expand the demo user interface to make it accessible across the AMMT program.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Sensor Data Qualification System (SDQS) Implementation Study

The Sensor Data Qualification System (SDQS) is being developed to provide a sensor fault detection capability for NASA s next-generation launch vehicles. In addition to traditional data qualification techniques (such as limit checks, rate-of-change checks and hardware redundancy checks), SDQS can provide augmented capability through additional techniques that exploit analytical redundancy relationships to enable faster and more sensitive sensor fault detection. This paper documents the results of a study that was conducted to determine the best approach for implementing a SDQS network configuration that spans multiple subsystems, similar to those that may be implemented on future vehicles. The best approach is defined as one that most minimizes computational resource requirements without impacting the detection of sensor failures.

Wong, Edmond↗
NASA NTRS2013

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

Fulton, Christopher↗
DOE OSTI2025

Data Qualification Report: SRNL Glass Composition-Properties (ComPro) Database

The Savannah River National Laboratory Glass Composition-Properties (ComPro) database is an extensive database containing pertinent composition and durability data to support the accelerated clean-up mission at the Defense Waste Processing Facility. The activities described in this data qualification report were performed to support the information contained in the database. There were two objectives of the original data qualification process. The first objective was to review supporting documentation to determine if DOE/RW-0333P Quality Assurance Requirements and Description had been implemented during the original work. If the DOE/RW-0333P Quality Assurance Requirements and Description had not been directly implemented during the original work, the second objective was to determine if the controls that were used were adequate to meet the intent of the DOE/RW-0333P Quality Assurance Requirements and Description. The results of these two objectives and the activities performed to support these decisions are described in this document. An assessment of each dataset was made to determine if the data were RW-0333P Compliant, RW-0333P Equivalent or Non-RW-0333P Compliant. The original data qualification was performed in accordance with E7, Conduct of Engineering Manual, Procedure 3.70, Revision 4, Qualification of Data. The specific method that was used was Equivalent Controls as described in E7, 3.70. Revision 2 of this document adds supporting information for the RW-0333P Compliant datasets added to Revision 3 of the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2013

Sensor Data Qualification Technique Applied to Gas Turbine Engines

This paper applies a previously developed sensor data qualification technique to a commercial aircraft engine simulation known as the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k). The sensor data qualification technique is designed to detect, isolate, and accommodate faulty sensor measurements. It features sensor networks, which group various sensors together and relies on an empirically derived analytical model to relate the sensor measurements. Relationships between all member sensors of the network are analyzed to detect and isolate any faulty sensor within the network.

control↗
NASA NTRS2006

Sensor Data Qualification for Autonomous Operation of Space Systems

NASA's new Exploration initiative for both robotic and manned missions will require higher levels of reliability, autonomy and reconfiguration capability to make the missions safe, successful and affordable. Future systems will require diagnostic reasoning to assess the health of the system in order to maintain the system s functionality. The diagnostic reasoning and assessment will involve data qualification, fault detection, fault isolation and remediation control. A team of researchers at the NASA Glenn Research Center is currently working on a Sensor Data Qualification (SDQ) system that will support these critical evaluation processes, for both automated and human-in-the-loop applications. Data qualification is required as a first step so that critical safety and operational decisions are based on good data. The SDQ system would monitor a network of related sensors to determine the health of individual sensors within that network. Various diagnostic systems such as the Caution and Warning System would then use the sensor health information with confidence. The proposed SDQ technology will be demonstrated on a variety of subsystems that are relevant to NASA s Exploration systems, which currently include an electrical power system and a cryogenic fluid management system. The focus of this paper is the development and demonstration of a SDQ application for a prototype power distribution unit that is representative of a Crew Exploration Vehicle electrical power system; this provides a unique and relevant environment in which to demonstrate the feasibility of the SDQ technology.

Maul, William A.↗
NASA NTRS2012

Onboard Sensor Data Qualification in Human-Rated Launch Vehicles

The avionics system software for human-rated launch vehicles requires an implementation approach that is robust to failures, especially the failure of sensors used to monitor vehicle conditions that might result in an abort determination. Sensor measurements provide the basis for operational decisions on human-rated launch vehicles. This data is often used to assess the health of system or subsystem components, to identify failures, and to take corrective action. An incorrect conclusion and/or response may result if the sensor itself provides faulty data, or if the data provided by the sensor has been corrupted. Operational decisions based on faulty sensor data have the potential to be catastrophic, resulting in loss of mission or loss of crew. To prevent these later situations from occurring, a Modular Architecture and Generalized Methodology for Sensor Data Qualification in Human-rated Launch Vehicles has been developed. Sensor Data Qualification (SDQ) is a set of algorithms that can be implemented in onboard flight software, and can be used to qualify data obtained from flight-critical sensors prior to the data being used by other flight software algorithms. Qualified data has been analyzed by SDQ and is determined to be a true representation of the sensed system state; that is, the sensor data is determined not to be corrupted by sensor faults or signal transmission faults. Sensor data can become corrupted by faults at any point in the signal path between the sensor and the flight computer. Qualifying the sensor data has the benefit of ensuring that erroneous data is identified and flagged before otherwise being used for operational decisions, thus increasing confidence in the response of the other flight software processes using the qualified data, and decreasing the probability of false alarms or missed detections.

Wong, Edmond↗
DOE OSTI2026

HDG-1 Experiment Irradiation Monitoring Data Qualification Final Report

SUMMARY The U.S. Department of Energy (DOE) Advanced Reactor Technologies (ART) Graphite Research and Development (GRD) Program is conducting a series of six experiments to quantify the effects of irradiation on nuclear-grade graphite. This report documents the qualification of irradiation monitoring data for the fifth experiment, High Dose Graphite-1 (HDG-1). Qualified monitoring data are required by the ART program to support the design and licensing of the first high-temperature reactor (HTR) nuclear plant. Data are classified as Qualified if they meet the usage requirements described in the experiment planning and quality assurance (QA) documents, Failed if they do not meet those requirements and provide no usable information, or Trend if they do not fully meet all requirements but still provide useful information subject to an assessment of how any deficiencies may affect a particular use of the data. HDG-1 irradiation began with Advanced Test Reactor (ATR) Cycle 168B on August 24, 2020, and concluded after Cycle 173C on January 27, 2025. The HDG-1 capsule was removed from the reactor core twice—during core internal change (CIC) Cycle 170A and powered axial locator mechanism (PALM) Cycle 172A—to prevent overheating of the graphite specimens during high-power PALM cycles. The capsule was therefore irradiated during a total of seven normal ATR cycles: 168B, 169A, 171A, 171B, 173A, 173B, and 173C. Irradiation monitoring data evaluated in this report include thermocouple (TC) temperature, gas flow rate, gas moisture, gas pressure, specimen load, and graphite stack displacement. Temperature. A total of 14,508,065 TC temperature records were captured. Of these, 13,901,785 (95.8%) are Qualified and 606,280 (4.2%) are Failed. The principal source of failed temperature data was the instrument failure of TC-9 (Zone 2) on June 24, 2024, and TC-10 (Zone 1) on July 5, 2024, near the end of Cycle 173A, which resulted in 595,554 Failed readings. An additional 379 missing values and 10,347 slightly negative values from TC-13 during ATR outages are also Failed. Neither TC-9 nor TC-10 was used as a temperature-control TC, and their failures did not compromise capsule condition monitoring. Correlation analysis of all 13 TCs found no evidence of virtual junction formation. Control chart analysis revealed clear downward drift of approximately 80°C for TC-6 (Zone 3) relative to other stable TCs, and possible downward drift of approximately 60°C for TC-13 relative to the Zone 5 control TC (TC-1), though TC-13 remained consistent with the Zone 2 control TC (TC-12). Gas flow. A total of 20,088,090 gas flow rate records were captured. Of these, 19,941,463 (99.3%) are Qualified and 146,627 (0.7%) are Failed due to missing values. All argon, helium, and total gas flow data were within expected ranges throughout the irradiation. Gas moisture. A total of 1,116,005 outlet gas moisture values were captured. Of these, 1,101,421 (98.7%) are Qualified and 14,584 (1.3%) are Failed, comprising 14,556 out-of-range values and 28 missing values. The out-of-range moisture values exceeded 22,000 ppmv for approximately 1 week at the beginning of Cycle 173A, when accumulated moisture evaporated after the capsule was retrieved from water storage during PALM Cycle 172A and reinserted into the east flux trap. Moisture levels returned to below 25 ppmv for the remaining three cycles, and the transient high-moisture event did not affect the integrity of specimen irradiation. Gas pressure. A total of 7,812,035 gas pressure values were captured. Of these, 6,642,048 (85.0%) are Qualified and 1,169,987 (15.0%) outlet pressure values are Failed, comprising 718,537 zero outlet pressure values due to sensor failure from Cycle 168B through Cycle 171B, 54,550 missing values, and 396,900 too-low outlet pressure values, ranging from 1.1 to 1.6 psia after sensor replacement during Cycle 173A. Load. A total of 6,696,030 load values were captured. Of these, 6,694,580 (99.98%) are Qualified and 1,450 (0.02%) are Failed due to missing values. Applied loads to the six specimen stacks were stable throughout the irradiation. Stack displacement. A total of 6,696,030 displacement values were captured. Of these, 5,713,297 (85.32%) are Qualified and 3,781 (0.06%) are Failed due to missing values. Stack displacement increased consistently throughout the irradiation, reaching approximately 3.08 in. for Channels 5 and 6 by the end of irradiation. 978,952 (14.62%) substantially elevated displacements observed for Channel 6 beginning in Cycle 171A and for Channel 5 beginning in Cycle 173A are assigned Trend status. Raising pressure. A total of 1,115,999 raising pressure values were captured. Of these, 1,115,430 (99.95%) are Qualified and 569 (0.05%) are Failed due to missing values. Ram pressure. A total of 6,696,030 ram pressure values were captured. Of these, 6,692,249 (99.95%) are Qualified and 3,484 (0.05%) are Failed due to missing values. Stack raising was perf

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Fuels Irradiation and Physics Database (FIPD) Development and Data Qualification FY24 Updates

The DOE-NE Advanced Reactor Technologies program has supported efforts to recover and preserve metallic fuel data generated throughout the past fast reactor R&D programs. Those efforts are currently focused on establishing databases for information from the experiments conducted during the Integral Fast Reactor program including data generated at EBR-II, FFTF, and TREAT reactors, as well as out of pile transient testing data. These databases are essential for future-licensing activities of metallic fuel based advanced fast reactors. This report describes the Metallic Fuels Irradiation & Physics Database (FIPD) development, content updates, data qualification status, and user support in FY24. Future plans are also provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advances in Metallic Fuel Database Development and Data Qualification

The Fuels Irradiation and Physics Database (FIPD [1]) is a comprehensive repository of data and documents related to Uranium-Zirconium based metallic fuel test pins. This database stores operational conditions of these pins, calculated using a suite of Argonne National Laboratory analysis codes developed during the Integral Fast Reactor (IFR) program. Key calculated data include axial distributions of power, temperature, fluence, burnup, and isotopic densities. Additionally, the FIPD holds post-irradiation examination (PIE) data such as fission gas release, gas chemistry measurements, and axial distributions derived from profilometry, gamma scanning, and neutron radiography. Complementing these data is an extensive archive of documents related to various pins and experiments. These include raw PIE records, design details, safety analyses, and operational reports. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The recent improvements to the database and the data QA status are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Summary of Savannah River Site FY24 Salt Waste Qualification Data

Savannah River Mission Completion (SRMC), the Liquid Waste Operations subcontractor at SRS, and Savannah River National Laboratory (SRNL) analyzed samples from Savannah River Site (SRS) Waste Tanks 21H, 41H, and 42H to support qualification of Salt Waste Processing Facility (SWPF) Waste Batches 10, 11, 12, and 13 for processing (the FY24 Salt Batch Qualification samples). These Tanks (i.e. 21H, 41H and 42H) are blend tanks for feed to SWPF. This report focuses on the characterization of the Salt Batch Qualification sub-samples (i.e., any adjustments after qualification or as a part of processing are documented elsewhere). None of the samples displayed any unusual or unexpected characteristics such as large amounts of solids, floating solids, or unusual color. Characterization of these samples confirmed similar chemical composition and characteristics to previous salt waste batches. The analytical results (both rapid, typically 4 weeks, and long-term, typically 8 weeks) for Salt Batches 10, 11, 12, and 13 are now summarized and discussed in this technical report. SRMC-Analytical Laboratory (SRMC-AL), for the first time, provided all the short-term analyses data used in the qualification evaluations of SWPF Salt Batches 10, 11, 12, and 13. These SRMC short-term results are presented along with the results for the long-term analyses provided by SRNL.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Legacy Metallic Fuel U(Pu)Zr Data Qualification

The Metallic Fuels Irradiation and Physics Database (FIPD) is an organized collection of legacy metallic fuel U(Pu)Zr data, measurements, and reactor conditions from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The database provides a wealth of information and is easily accessed and utilized by the U.S. nuclear industry and the Nuclear Regulatory Commission (NRC). The database contains three categories of data. The first category covers fuel pin fabrication specifications, including fuel slug diameter and length, cladding diameter, smeared density, etc. The second consists of operational conditions, which include axial distributions of power, temperature, fluence, burnup, isotopic density, etc. The third category contains post-irradiation examination (PIE) results, largely collected at facilities now part of the Idaho National Laboratory (INL), formerly part of Argonne National Laboratory -West (ANL-W) and at the Alpha-Gamma Hot Cell Facility (AGHCF) at Argonne National Laboratory, comprising fission gas release and chemistry analysis, profilometry measurements, neutron radiography data, etc. All these data have been organized and preserved in FIPD.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Summary of Savannah River Site FY23 Salt Waste Qualification Data

The Savannah River National Laboratory (SRNL) analyzed samples from Savannah River Site (SRS) Waste Tanks 41H and 21H to support qualification of Salt Waste Processing Facility (SWPF) Waste Batches 8 and 9 for processing (the FY23 Salt Batch Qualification samples). These Tanks (i.e. 41H and 21H) are blend tanks for feed to SWPF. None of the samples displayed any unusual or unexpected characteristics such as large amounts of solids, floating solids, or unusual color. Characterization of these samples confirmed similar chemical composition and characteristics to previous salt waste batches. The results for Batches 8 and 9 were provided by SRNL to Savannah River Mission Completion (SRMC), the Liquid Waste Operations subcontractor at SRS, as External Sample Results (Laboratory Information Management System (LIMS)) Reports. Additionally, a separate technical memo was issued by SRNL to report re-test data for Batch 8 for Cs-137 for filtered samples only which were run at the request of SWPF. For Batch 9, a set of samples was also analyzed in parallel by the SWPF-Analytical Laboratory (SWPF-AL). The SWPF-AL data is included herein for comparison with the SRNL data where applicable. The analytical results (both rapid, typically 4 weeks, and long term, typically 8 weeks) for Batches 8 and 9 are now summarized and discussed in this technical report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Progress Report on SFR Metallic Fuel Data Qualification

This report summarizes the progress of SFR metallic fuel qualification related activities, which are focused on providing quality assurance relevant information applicable to experiments irradiated during the Integral Fast Reactor (IFR) program. An overview of the metallic fuel performance data and the associated databases, including the EBR-II Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT Experimental Relational Database (TREXR) is included. The legacy data in the databases, including as-built, post-irradiation examination (PIE), operating parameters, and out-of-pile experiment post-test data are introduced. The SFR metallic fuel Quality Assurance Program Plan (QAPP) and its implementation to qualify these legacy data is described in detail. Important PIE data QA documents and the specifications of seven types of PIE measurements (contact profilometry, laser profilometry, neutron radiography, gamma scan, fission gas release fission gas chemistry, and metallography) are provided. Examples of the implementation of the QAPP to qualify each of those types of PIE data are provided.

Mo, Kun [Argonne National Laboratory (ANL), Argonn↗
DOE OSTI2025

Quality Assurance Program Plan for SFR Metallic Fuel Data Qualification

This document contains an evaluation of the applicability of the current Quality Assurance Standards from the American Society of Mechanical Engineers Standard NQA-1 (NQA-1) criteria and identifies and describes the quality assurance process(es) by which attributes of historical, analytical, and other data associated with sodium-cooled fast reactor [SFR] metallic fuel will be evaluated. This process is being instituted to facilitate validation of data to the extent that such data may be used to support future licensing efforts associated with advanced reactor designs. The initial data to be evaluated under this program were generated during the US Integral Fast Reactor program between 1984-1994, where the data include, but are not limited to, research and development data and associated documents, test plans and associated protocols, operations and test data, technical reports, and information associated with past United States Nuclear Regulatory Commission reviews of SFR designs. It is recognized that managing the data generated by large research and development projects presents a significant challenge for retaining data integrity and availability. American Society of Mechanical Engineers Standard NQA-1 (NQA-1) 2008/2009a provides appropriate requirements for this plan.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2017

Towards a Qualification Data Set: Expanded SEE Data on the P2020 Processor

Earlier P2020 SEE data are compared and expanded to a recent die revision, significantly increasing samples tested by protons by five devices, and by heavy ions by five devices. Earlier tested SEE types are found to be fairly similar in register, L1 cache, L2 cache, and CPU crashes. New test methods give SEE performance for the flash memory controller, watchdog circuit, and a built-in Ethernet port on the P2020 processor. Results from heavy ion and proton tests are presented, with data separated over a large number of specific error types and test programs.

Vartanian, Sargeh↗