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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The analytical control program for the NASA Space Station Freedom Environmental Control and Life Support System (ECLSS) Water Recovery Test

NASA-Marshall has striven to maximize quality assurance and quality control measures in the course of Water Recovery Test (WRT) development for the Space Station Freedom ECLSS. The WRT was subjected to an independent analytical control program that is governed by the Analytical Control Test Plan and the Microbiological Methods for Water Recovery Testing Plan. Attention is given to analysis results for volatiles, sodium, and conductivity.

Tatara, James D.↗
NASA NTRS2007

Characterizing SWCNT Dispersion in Polymer Composites

The new wave of single wall carbon nanotube (SWCNT) infused composites will yield structurally sound multifunctional nanomaterials. The SWCNT network requires thorough dispersion within the polymer matrix in order to maximize the benefits of the nanomaterial. However, before any nanomaterials can be used in aerospace applications a means of quality assurance and quality control must be certified. Quality control certification requires a means of quantification, however, the measurement protocol mandates a method of seeing the dispersion first. We describe here the new tools that we have developed and implemented to first be able to see carbon nanotubes in polymers and second to measure or quantify the dispersion of the nanotubes.

Lillehei, Peter T.↗
NASA NTRS2011

Effects of Data Quality on the Characterization of Aerosol Properties from Multiple Sensors

Cross-comparison of aerosol properties between ground-based and spaceborne measurements is an important validation technique that helps to investigate the uncertainties of aerosol products acquired using spaceborne sensors. However, it has been shown that even minor differences in the cross-characterization procedure may significantly impact the results of such validation. Of particular consideration is the quality assurance I quality control (QA/QC) information - an auxiliary data indicating a "confidence" level (e.g., Bad, Fair, Good, Excellent, etc.) conferred by the retrieval algorithms on the produced data. Depending on the treatment of available QA/QC information, a cross-characterization procedure has the potential of filtering out invalid data points, such as uncertain or erroneous retrievals, which tend to reduce the credibility of such comparisons. However, under certain circumstances, even high QA/QC values may not fully guarantee the quality of the data. For example, retrievals in proximity of a cloud might be particularly perplexing for an aerosol retrieval algorithm, resulting in an invalid data that, nonetheless, could be assigned a high QA/QC confidence. In this presentation, we will study the effects of several QA/QC parameters on cross-characterization of aerosol properties between the data acquired by multiple spaceborne sensors. We will utilize the Multi-sensor Aerosol Products Sampling System (MAPSS) that provides a consistent platform for multi-sensor comparison, including collocation with measurements acquired by the ground-based Aerosol Robotic Network (AERONET), The multi-sensor spaceborne data analyzed include those acquired by the Terra-MODIS, Aqua-MODIS, Terra-MISR, Aura-OMI, Parasol-POLDER, and CalipsoCALIOP satellite instruments.

Petrenko, Maksym↗
NASA NTRS2016

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

DeLoach, Richard↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2022

U.S. Nuclear Operating Experience Program for Probabilistic Risk Assessment Parameter Estimations

This presentation describes the overall process of the U.S. Nuclear Regulatory Commission (NRC)operating experience (OpE) program to estimate probabilistic risk assessment parameters, the estimations of initiating event frequencies, the component unreliability, and the quality assurance and quality control activities for the OpE program. This presentation can be used for training, workshop, or knowledge transfer purpose.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1993

Mission operations and command assurance: Instilling quality into flight operations

Mission Operations and Command Assurance (MO&CA) is a Total Quality Management (TQM) task on JPL projects to instill quality in flight mission operations. From a system engineering view, MO&CA facilitates communication and problem-solving among flight teams and provides continuous process improvement to reduce the probability of radiating incorrect commands to a spacecraft. The MO&CA task has evolved from participating as a member of the spacecraft team to an independent team reporting directly to flight project management and providing system level assurance. JPL flight projects have benefited significantly from MO&CA's effort to contain risk and prevent rather than rework errors. MO&CA's ability to provide direct transfer of knowledge allows new projects to benefit from previous and ongoing flight experience.

Welz, Linda L.↗
NASA NTRS1992

Software assurance standard

This standard specifies the software assurance program for the provider of software. It also delineates the assurance activities for the provider and the assurance data that are to be furnished by the provider to the acquirer. In any software development effort, the provider is the entity or individual that actually designs, develops, and implements the software product, while the acquirer is the entity or individual who specifies the requirements and accepts the resulting products. This standard specifies at a high level an overall software assurance program for software developed for and by NASA. Assurance includes the disciplines of quality assurance, quality engineering, verification and validation, nonconformance reporting and corrective action, safety assurance, and security assurance. The application of these disciplines during a software development life cycle is called software assurance. Subsequent lower-level standards will specify the specific processes within these disciplines.

Source record↗
NASA NTRS1985

Develop and demonstrate manufacturing processes for fabricating graphite filament reinforced polymide (Gr/PI) composite structural elements

A study was conducted to assess the merits of using graphite/polyimide, NR-150B2 resin, for structural applications on advanced space launch vehicles. The program was divided into two phases: (1) Fabrication Process Development; and (2) Demonstration Components. The first phase of the program involved the selection of a graphite fiber, quality assurance of the NR-150B2 polyimide resin, and the quality assurance of the graphite/polyimide prepreg. In the second phase of the program, a limited number of components were fabricated before the NR-150B2 resin system was removed from the market by the supplier, Du Pont. The advancement of the NR-150B2 polyimide resin binder was found to vary significantly based on previous time and temperature history during the prepregging operation. Strength retention at 316C (600F) was found to be 50% that of room temperature strength. However, the composite would retain its initial strength after 200 hours exposure at 316C (600F). Basic chemistry studies are required for determining NR-150B2 resin binder quality assurance parameters. Graphite fibers are available that can withstand high temperature cure and postcure cycles.

Chase, V. A.↗
NASA NTRS1966

Support of NASA quality requirements by defense contract administration services regions

Defense Contract Administration Services Regions (DCASR) quality assurance personnel performing under NASA Letters of Delegation must work closely with the assigned technical representative of the NASA centers. It is realized that technical personnel from the NASA Centers cannot make on-site visits as frequently as they would like to. However, DCASR quality assurance personnel would know the assigned NASA technical representative and should contact him when problems arise. The technical representative is the expert on the hardware and should be consulted on any problem area. It is important that the DCASR quality assurance personnel recommend to the delegating NASA Center any new or improved methods of which they may be aware which would assist in achieving the desired quality and reliability in NASA hardware. NASA expects assignment of competent personnel in the Quality Assurance functional area and is not only buying the individual's technical skill, but also his experience. Suggestions by field personnel can many times up-grade the quality or the hardware.

Farrar, Hiram D.↗
NASA NTRS2013

Bayesian Revision of Residual Detection Power

This paper addresses some issues with quality assessment and quality assurance in response surface modeling experiments executed in wind tunnels. The role of data volume on quality assurance for response surface models is reviewed. Specific wind tunnel response surface modeling experiments are considered for which apparent discrepancies exist between fit quality expectations based on implemented quality assurance tactics, and the actual fit quality achieved in those experiments. These discrepancies are resolved by using Bayesian inference to account for certain imperfections in the assessment methodology. Estimates of the fraction of out-of-tolerance model predictions based on traditional frequentist methods are revised to account for uncertainty in the residual assessment process. The number of sites in the design space for which residuals are out of tolerance is seen to exceed the number of sites where the model actually fails to fit the data. A method is presented to estimate how much of the design space in inadequately modeled by low-order polynomial approximations to the true but unknown underlying response function.

DeLoach, Richard↗
NASA NTRS1997

Effective Documentation Tools

Quality assurance programs provide a very effective means to monitor and evaluate medical care. Quality assurance involves: (1) Identify a problem; (2) Determine the source and nature of the problem; (3) Develop policies and methods to effect improvement; (4) Implement those polices; (5) Monitor the methods applied; and (6) Evaluate their effectiveness. Because this definition of quality assurance so closely resembles the Nursing Process, the health unit staff was able to use their knowledge of the nursing process to develop many forms which improve the quality of patient care. These forms include the NASA DFRC Service Report, the occupational injury form (Incident Report), the patient survey (Pre-hospital Evaluation/Care Report), the Laboratory Log Sheet, the 911 Run Sheet, and the Patient Assessment Stamp. Examples and steps which are followed to generate these reports are described.

Sleboda, Claire↗
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 NTRS1971

NASA's quality program - Achievements and forecast.

The NASA quality assurance program during the preceding decade is reviewed, with emphasis on those events and accomplishments which bear on the improvement of quality assurance technology, practice, and management. The nature of the space program is discussed to help understand the significance of past quality assurance events and to anticipate future trends. Potential benefits are revealed which are not necessarily limited to the aerospace field.

Weiss, H. M.↗
NASA NTRS2010

Toward Unified Satellite Climatology of Aerosol Properties. 3. MODIS Versus MISR Versus AERONET

We use the full duration of collocated pixel-level MODIS-Terra and MISR aerosol optical thickness (AOT) retrievals and level 2 cloud-screened quality-assured AERONET measurements to evaluate the likely individual MODIS and MISR retrieval accuracies globally over oceans and land. We show that the use of quality-assured MODIS AOTs as opposed to the use of all MODIS AOTs has little effect on the resulting accuracy. The MODIS and MISR relative standard deviations (RSTDs) with respect to AERONET are remarkably stable over the entire measurement record and reveal nearly identical overall AOT performances of MODIS and MISR over the entire suite of AERONET sites. This result is used to evaluate the likely pixel-level MODIS and MISR performances on the global basis with respect to the (unknown) actual AOTs. For this purpose, we use only fully compatible MISR and MODIS aerosol pixels. We conclude that the likely RSTDs for this subset of MODIS and MISR AOTs are 73% over land and 30% over oceans. The average RSTDs for the combined [AOT(MODIS)+AOT(MISR)]/2 pixel-level product are close to 66% and 27%, respectively, which allows us to recommend this simple blend as a better alternative to the original MODIS and MISR data. These accuracy estimates still do not represent the totality of MISR and quality-assured MODIS pixel-level AOTs since an unaccounted for and potentially significant source of errors is imperfect cloud screening. Furthermore, many collocated pixels for which one of the datasets reports a retrieval, whereas the other one does not may also be problematic.

Mishchenko, Michael I.↗
NASA NTRS1990

Prototype automated post-MECO ascent I-load Verification Data Table

A prototype automated processor for quality assurance of Space Shuttle post-Main Engine Cut Off (MECO) ascent initialization parameters (I-loads) is described. The processor incorporates Clips rules adapted from the quality assurance criteria for the post-MECO ascent I-loads. Specifically, the criteria are implemented for nominal and abort targets, as given in the 'I-load Verification Data Table, Part 3, Post-MECO Ascent, Version 2.1, December 1989.' This processor, ivdt, compares a given l-load set with the stated mission design and quality assurance criteria. It determines which I-loads violate the stated criteria, and presents a summary of I-loads that pass or fail the tests.

Lardas, George D.↗
DOE OSTI2023

AmeriFlux BASE data pipeline to support network growth and data sharing

Abstract AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Contracting Quality Early in the Lifecycle Using AS9145 Data Deliverables

Development schedules and a highly dynamic supply chain are a challenge to developers of complex systems produced at low volume. Flaws in designs, parts and materials availability problems, poor manufacturability, and a lack of knowledge about critical items and key process attributes can be realized well before traditional second-party quality assurance activities begin. Supplier audits and product inspections may have little mitigating effect once these foundational problems have been realized. Their impacts can be significant lifecycle disruption, cost overruns, inability to deliver to plan, and even project cancellation. AS9145, Requirements for Advanced Product Quality Planning and Production Part Approval Process, can be used to drive quality engineering practices into early development lifecycles to significantly reduce this late-cycle risk and to reduce the cost of quality overall. Since its initial publication in 2016, it has had very limited adoption by the DoD, no adoption by NASA, and sparse adoption in the aerospace and defense supply chain. A task group within the Aerospace Industries Association's (AIA) Joint Strategic Quality Council (JSQC) identified that both acquirers and suppliers see as AS9145 as a cost-adder and are hesitant to use it as an alternative to late-stage-heavy quality assurance approaches. A lack of prior use creates large capability gaps in request-for-proposal (RFP) teams, proposal teams, suppliers’ quality management systems (QMS), and in experienced personnel executing the early lifecycle approach. To create a more realizable on-ramp for using AS9145 in the space and defense sectors, the AIA JSQC task team created five deliverable requirements descriptions (DRDs) that can be used in a contract to begin to engage both parties in early lifecycle quality engineering and quality assurance activities, that reduce exposure to late-stage cost and schedule collapse due to unidentified risks in design, supply chain, and manufacturability. These DRDs drive the parties to engage in planning and analysis discussions early on to understand what production risks can be known and how they will focus resources based on safety criticality and the key elements of design and construction. The suppliers and acquirers who will produce the data and information required by the DRD will be able to incrementally evolve their QMS and the acquirer will incrementally be able to track and understand the benefits of cost shifting from late to early development phases. A white paper describing this approach and the five recommended DRDs will be published by the AIA in late 2024 or early 2025.

Jeannette Plante↗