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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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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 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.
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.
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Metal halide perovskite (MHP) photovoltaics provide high efficiencies with less stringent processing requirements than traditional photovoltaic materials. However, processing related defects must be suppressed as they can lead to decreases in initial device efficiency and potentially compromise long-term device operation. In this work we investigate morphological defects in MHP devices using luminescence imaging followed by in-depth structural and composition analysis using electron microscopy-based methods. We identify several different classes of spot-like processing-related defects and observe that a single device structure may contain multiple types of these defects. The presence of these defects in devices with different layer structures and absorber chemistries makes them relevant to the perovskite photovoltaic community as a whole. The defects are associated with voids in the perovskite layer, inclusions (glass, migrated Ag, dust), thickness variations, hole transport layer disruption with anomalous crystal growth, and electron transport layer disruptions that could allow Ag intrusion and lead to local shunts. As perovskite photovoltaic technology matures, mitigation of such defects is critical to improving not only initial performance but also the long-term stability required for industrial applications.
The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.
Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.
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The X-band Scanning ARM Precipitation Radar (X-SAPR) is an X-band dual-polarization Doppler weather radar. The X-SAPR operates in a simultaneous transmit and receive (STAR) mode, meaning that the transmit signal is split so that power is transmitted on both horizontal and vertical polarizations at the same time. The X-SAPR transmitter is a 200 kW (peak power) magnetron. The receiver is based upon the Vaisala RVP-900 and runs Vaisala’s IRIS software. In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the X-SAPR also provides differential reflectivity, correlation coefficient, and specific differential phases. The dual-polarization variables enable estimates of rainfall rates and identification of precipitation types. At the Southern Great Plains site, three X-SAPRs surround the Central Facility, allowing the use of multi-Doppler velocity retrievals to estimate wind fields.
Visual inspection criteria assure the metallurgical integrity of spot welds joining nickel leads and nickel ribbon in a 90 degree cross-wire configuration.
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Investigation of parallel gap soldering of electrical leads resulted in recommendation on material preparation, equipment, process control, and visual inspection criteria to ensure reliable solder joints. The recommendations will minimize problems in heat-dwell time, amount of solder, bridging conductors, and damage of circuitry.