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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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A systematic Data Quality Checking Protocol for vessel Mounted Acoustic Doppler Current Profiler observations is proposed. Previous-to-acquisition conditions are considered along with simultaneous ones.
The Congressional committees that authorize the activities of NASA requested that the National Research Council (NRC) review the testing and quality assurance programs for the Advanced Solid Rocket Motor (ASRM) program. The proposed ASRM design incorporates numerous features that are significant departures from the Redesigned Solid Rocket Motor (RSRM). The NRC review concentrated mainly on these features. Primary among these are the steel case material, welding rather than pinning of case factory joints, a bolted field joint designed to close upon firing the rocket, continuous mixing and casting of the solid propellant in place of the current batch processes, use of asbestos-free insulation, and a lightweight nozzle. The committee's assessment of these and other features of the ASRM are presented in terms of their potential impact on flight safety.
Model serves as graphic tool for estimating complete product objectives from limited input information, and is applied to cost estimations, product-quality evaluations, and effectiveness measurements for manpower resources allocation. Six product quality levels are defined.
Curves (geometry) and surfaces created by Computer Aided Geometric Design systems in the engineering environment must satisfy two basic quality criteria: the geometric shape must have the desired engineering properties; and the objects must be parameterized in a way which does not cause computational difficulty for geometric processing and engineering analysis. Interactive techniques are described which are in use at Boeing to evaluate the quality of aircraft geometry prior to Computational Fluid Dynamic analysis, including newly developed methods for examining surface parameterization and its effects.
As assembly of the International Space Station (ISS) proceeds, predictive techniques are used to determine the best approach for handling a variety of cabin air quality challenges. These techniques use equipment offgassing data collected from each ISS module before flight to characterize the trace chemical contaminant load. Combined with crew metabolic loads, these data serve as input to a predictive model for assessing the capability of the onboard atmosphere revitalization systems to handle the overall trace contaminant load as station assembly progresses. The techniques for predicting in-flight air quality are summarized along with results from early ISS mission analyses. Results from groundbased analyses of in-flight air quality samples are compared to the predictions to demonstrate the technique's relative conservatism.
In the first section of this report background information for the use of the ERB-7 Master Archival Tapes (MAT) is provided. The second section gives details regarding the scientific validity and quality of the MAT. The MAT data analyzed covers the period from November 16, 1978 to October 31, 1979.
The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.
The publicly accessible databases for the Extreme Ultraviolet Explorer include: the EUVE Archive mailserver; the CEA ftp site; the EUVE Guest Observer Mailserver; and the Astronomical Data System node. The EUVE Performance Assurance team is responsible for verifying that these public EUVE databases are working properly, and that the public availability of EUVE data contained therein does not infringe any data rights which may have been assigned. In this poster, we describe the Quality Assurance (QA) procedures we have developed from the approach of QA as a service organization, thus reflecting the overall EUVE philosophy of Quality Assurance integrated into normal operating procedures, rather than imposed as an external, post facto, control mechanism.
The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs.
A variety of devices and techniques useful in nondestructive testing is described. Ranging in complexity from an automated ultrasonic testing system designed for complex laminated honeycomb structures, to a flexible leak detector probe, the items represent either potential savings in cost and time, or improvement in inspection quality over past techniques. Data cover weld and braze inspection, leak detection, and inspection of composite materials.
Manual gives detailed test procedures for controlling silicon-wafer processing in manufacture of integrated circuits. Included among 43 test procedures are: ionic, bacterial, and solids contamination of high-purity water needed for wafer processing; crystallographic reflection, purity, and orientation; substrate dimensions and finish; thickness of deposited epitaxial films; oxide quality; photoresist characteristics; pinholes in insulating layers; metallized adhesion; and quality of ohmic contact.
Printed Wiring Multilayer Board (PWMLB) structures for high speed, high density circuits are prone to failure due to the microcracking of electrolytic copper interconnections. The failure can occur in the foil that makes up the inner layer traces or in the plated through holes (PTH) deposit that forms the layer to layer interconnections. It is shown that there are some distinctive differences in the quality of Type E copper and that these differences can be detected before its use in a PWMLB. It is suggested that the strength of some Type E copper can be very low when the material is hot and that it is the use of this poor quality material in a PWMLB that results in PTH and inner layer microcracking. Since the PWMLB failure in question are induced by a thermal stress, and since the poorer grades of Type E materials used in these structures are susceptible to premature failure under thermal stress, the use of elevated temperature rupture and creep rupture testing is proposed as a means for screening copper foil, or its PTH equivalent, in order to eliminate the problem of Type E copper microcracking in advanced PWMLBs.
Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).
Idaho National Laboratory provides xenon radionuclide samples and standards containing 131mXe, 133Xe, 133mXe, and 135Xe in a variety of matrices and container types. Activities can range from 0.5 Bq to 30 kBq, depending on the radionuclide. The methods used to produce radiometrically pure 131mXe and 133Xe as well as mixed isotope 133m/133Xe and 135/133/133mXe samples will be discussed. Limitations of the production methods will also be covered. Quantification of the radionuclides is done by HPGe detector assays. The current calibration methods as well as the calibration methods currently under development will also be discussed.
Abstract Pure molybdenum disulfide (MoS 2 ) solid lubricant coatings could attain densities comparable to doped films (and the associated benefits to wear rate and environmental stability) through manipulation of the microstructure via deposition parameters. Unfortunately, pure films can exhibit highly variable microstructures and mechanical properties due to processes that are not controlled during deposition (i.e., batch-to-batch variation). This work focuses on developing a relationship between density, hardness, friction, and wear for pure sputtered MoS 2 coatings. Results show that dense films ( ρ = 4.5 g/cm 3 ) exhibit a 100 × lower wear rate compared to porous coatings ( ρ = 3.04–3.55 g/cm 3 ). The tribological performance of high density pure MoS 2 coatings is shown to surpass that of established composite coatings, achieving a wear rate 2 × ( k = 5.74 × 10 –8 mm 3 /Nm) lower than composite MoS 2 /Sb 2 O 3 /Au in inert environments.
The upgrades of the CMS and ATLAS experiments for the high luminosity phase of the Large Hadron Collider will employ precision timing detectors based on Low Gain Avalanche Detectors (LGADs). We present a suite of results combining measurements from the Fermilab Test Beam Facility, a beta source telescope, and a probe station, allowing full characterization of the HPK type 3.1 production of LGAD prototypes developed for these detectors. We demonstrate that the LGAD response to high energy test beam particles is accurately reproduced with a beta source. Finally, we further establish that probe station measurements of the gain implant accurately predict the particle response and operating parameters of each sensor, and conclude that the uniformity of the gain implant in this production is sufficient to produce full-sized sensors for the ATLAS and CMS timing detectors.
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