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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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Cimel Sunphotometer (CSPHOT): lunar aerosol optical depth, filtered and quality-controlled with pre-field and post-field calibration applied, Level 2.0, version 3 data
The ability to handle data is critical at all stages of marine energy (ME) development. The marine hydrokinetic toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the ME community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission (IEC) technical specifications and other guidelines. A resource assessment of NDBC buoy 46050 near PACWAVE is performed using MHKiT and discusses comparisons to the resource assessment provided performed by Dunkel et al.
The ability to handle data is critical at all stages of marine energy (ME) development. The marine hydrokinetic toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the ME community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission (IEC) technical specifications and other guidelines. A resource assessment of NDBC buoy 46050 near PACWAVE is performed using MHKiT and discusses comparisons to the resource assessment provided performed by Dunkel et al.
Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.
Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.
The assembly of the ATLAS Inner Tracker requires the construction of 19,000 silicon strip sensor detector modules in eight different geometries. Modules will be assembled and tested at 31 institutes on four continents from sensors, readout chips, and flexes. In order to adhere to the module specifications defined for sufficient tracking performance, a rigorous programme of quality control (QC) was established to cover components at every stage of assembly. Here, this contribution presents an overview of the QC programme for ITk strip tracker modules, issues encountered during the pre-production phase (5% of the production volume), and their solutions.
Premature aging is a hallmark of Down syndrome, caused by trisomy of human chromosome 21; but the reason is unclear and difficult to study in humans. We used an aneuploid model in wild yeast to show that chromosome amplification disrupts nutrient-induced cell-cycle arrest, quiescence entry, and healthy aging, across genetic backgrounds and amplified chromosomes. We discovered that these defects are due in part to aneuploidy-induced dysfunction in Ribosome Quality Control (RQC). Aneuploids entering quiescence display aberrant ribosome profiles, accumulate RQC intermediates, and harbor an increased load of protein aggregates. Although they have normal proteasome capacity, aneuploids show signs of ubiquitin dysregulation, which impacts cyclin abundance to disrupt arrest. Remarkably, inducing ribosome stalling in euploids produces similar aberrations, while up-regulating limiting RQC subunits or proteins in ubiquitin metabolism alleviates many of the aneuploid defects. Our results raise major implications for other aneuploidy disorders including Down syndrome.
Premature aging is a hallmark of Down syndrome, caused by trisomy of human chromosome 21; but the reason is unclear and difficult to study in humans. We used an aneuploid model in wild yeast to show that chromosome amplification disrupts nutrient-induced cell-cycle arrest, quiescence entry, and healthy aging, across genetic backgrounds and amplified chromosomes. We discovered that these defects are due in part to aneuploidy-induced dysfunction in Ribosome Quality Control (RQC). Aneuploids entering quiescence display aberrant ribosome profiles, accumulate RQC intermediates, and harbor an increased load of protein aggregates. Although they have normal proteasome capacity, aneuploids show signs of ubiquitin dysregulation, which impacts cyclin abundance to disrupt arrest. Remarkably, inducing ribosome stalling in euploids produces similar aberrations, while up-regulating limiting RQC subunits or proteins in ubiquitin metabolism alleviates many of the aneuploid defects. Our results raise major implications for other aneuploidy disorders including Down syndrome.
Premature aging is a hallmark of Down syndrome, caused by trisomy of human chromosome 21; but the reason is unclear and difficult to study in humans. We used an aneuploid model in wild yeast to show that chromosome amplification disrupts nutrient-induced cell-cycle arrest, quiescence entry, and healthy aging, across genetic backgrounds and amplified chromosomes. We discovered that these defects are due in part to aneuploidy-induced dysfunction in Ribosome Quality Control (RQC). Aneuploids entering quiescence display aberrant ribosome profiles, accumulate RQC intermediates, and harbor an increased load of protein aggregates. Although they have normal proteasome capacity, aneuploids show signs of ubiquitin dysregulation, which impacts cyclin abundance to disrupt arrest. Remarkably, inducing ribosome stalling in euploids produces similar aberrations, while up-regulating limiting RQC subunits or proteins in ubiquitin metabolism alleviates many of the aneuploid defects. Our results raise major implications for other aneuploidy disorders including Down syndrome.
The DarkSide-20k dark matter direct detection experiment will employ a 21 m 2 silicon photomultiplier (SiPM) array, instrumenting a dual-phase 50 tonnes liquid argon Time Projection Chamber (TPC). SiPMs are arranged into modular photosensors called Tiles, each integrating 24 SiPMs onto a printed circuit board (PCB) that provides signal amplification, power distribution, and a single-ended output for simplified readout. Tiles are further grouped into Photo-Detector Units (PDUs). This paper details the production of the Tiles and the Quality Assurance and Quality Control (QA-QC) protocol established to ensure their performance and uniformity. The production and QA-QC of the Tiles are carried out at Nuova Officina Assergi (NOA), an ISO-6 clean room facility at LNGS. This process includes wafer-level cryogenic characterisation, precision die attaching, wire bonding, and extensive electrical and optical validation of each Tile. The overall production yield exceeds 83.5%, matching the requirements of the DarkSide-20k production plan. These results validate the robustness of the Tile design and its suitability for operation in a cryogenic environment.
Nanoindentation Continuous Stiffness Measurement (CSM) mapping, and Strain Rate Jump Testing (SRJT) proved effective to highlight and visualize the extent of property differences between the samples. From the analysis, Lot A has a significant dead zone at its center, especially when compared with the cross section of Lot B. Quick identification of property issues from one of the two feed stocks, both of which fit the specification. Mechanical performance was connected to the microstructure. Thus, using the structure-property results, it is noted that the processing of Lot A may have been insufficient. Nanoindentation allows for the sampling of quality control variables such as hardness and modulus in a short time span at high resolution, which is cheaper and faster than traditional strength experiments (tension and compression tests).
Here, we present a fast, low-cost ultraviolet (UV) spot test to screen photovoltaic modules for UV-induced degradation (UVID). The measurement is based on a fiber-coupled arc lamp that delivers a highly accelerated local UV dose-approximately five years of equivalent field exposure in less than a day. Spot-exposed areas are evaluated using electroluminescence imaging. We test a “known bad” product, which was observed to experience UVID in the field, and a “known good” product which did not exhibit UVID in the field. Results indicate that the method may be useful as a quick and relatively inexpensive quality control screening tool for customers and manufacturers.
Geophysical research on historical nuclear tests is an important aspect of future monitoring capabilities in seismic research. This research is challenging due to the limited number of digital seismic recordings during the peak of nuclear testing (1945-1992). These limited records are unique and non-reproducible data with potential high research impact. Releasing available nuclear explosion seismic records to the explosion monitoring community is thus of high value and is the motivation for this dataset release. The target of this effort was on compilation and quality control of regional seismic records of nuclear explosions recorded on Lawrence Livermore National Laboratory stations ELK, KNB, LAC, and MNV, known collectively as the Livermore National Network (LNN) (Figure 1). LNN was established in the early 1960s for the primary purpose of monitoring underground nuclear testing at the former Nevada Test Site (NTS), now known as the Nevada Nuclear Security Site (NNSS) following the signing of the Limited Test Ban Treaty (LTBT). LNN consisted initially of short-period vertical component Benioff’s recorded on film located at Mina, NV (MNV) and Kanab, Utah (KNB). LNN added two additional stations at Landers, CA (LAC) and Elko, NV (ELK) in 1967 and upgraded equipment to broadband seismometers recorded on frequency modulation (FM) tapes from 1967-1979, followed by digital recordings after 1979 (Jarpe, 1989). The digital recordings were on a variety of now obsolete media, including 9-track, Exabyte, and DAT tapes. Jarpe (1989) describes the seismic station instrumentation details over the period of deployment. LNN recorded valuable non-repeatable unique data of several hundreds of nuclear explosions at NNSS, as well as earthquakes and chemical and mining explosions (Walter, 2020). The details of these nuclear tests are provided in the Department of Energy Report NV-209 Rev 16 (DOE, 2015).
The Compact Muon Solenoid (CMS) experiment will undergo changes as part of the Large Hadron Collider upgrade. The CMS tracker will be upgraded to cope with the new radiation environment and to provide tracking at the first level trigger. This upgrade features a new type of silicon module called PS Module, which combines a Pixel sensor and a Strip sensor in the same module. The pixel portion of the PS module has a sensor bump bonded to 16 Macro Pixel ASICs (MPA) to form a Macro Pixel Sub Assembly (MaPSA). At Fermilab, MaPSAs are tested for quality control before being assembled with the strip sensors, readout and service electronics to form a PS Module. All of this test data is stored in a centralized database, and is used to grade the final module to determine if it will be installed in the detector. The Phase II Outer Tracker Analyzer of Test Outputs (POTATO) is the software that processes this data and determines the module grades. Using recent technologies, an AI agent is being im plemented into POTATO in order to allow users to more efficiently sort through the large amounts of analysis data and ensure that only the user specified data is being considered. This poster will display the process of testing a MaPSA, how that test data is relevant to module assembly and grading, and how the POTATO grading tool is being improved with the use of an embedded AI agent.
Down syndrome, caused by an extra copy of Chromosome 21, causes lifelong problems. One of the most common phenotypes among people with Down syndrome is premature aging, including early tissue decline, neurodegeneration, and shortened life span. Yet the reasons for premature systemic aging are a mystery and difficult to study in humans. Here we show that chromosome amplification in wild yeast also produces premature aging and shortens life span. Chromosome duplication disrupts nutrient-induced cell-cycle arrest, entry into quiescence, and cellular health during chronological aging, across genetic background and independent of which chromosome is amplified. Using a genomic screen, we discovered that these defects are due in part to aneuploidy-induced dysfunction in Ribosome Quality Control (RQC). We show that aneuploids entering quiescence display aberrant ribosome profiles, accumulate RQC intermediates, and harbor an increased load of protein aggregates compared to euploid cells. Although they maintain proteasome activity, aneuploids also show signs of ubiquitin dysregulation and sequestration into foci. Remarkably, inducing ribosome stalling in euploids produces similar aging phenotypes, while up-regulating limiting RQC subunits or poly-ubiquitin alleviates many of the aneuploid defects. We propose that the increased translational load caused by having too many mRNAs accelerates a decline in translational fidelity, contributing to premature aging.
Chloroplasts (photosynthetic plastids) of higher plants contain about 3000 proteins of which more than 95% are encoded by nuclear genes. To avoid the accumulation of reactive oxygen species that are the inherent by-product of photosynthesis, gene expression of these spatially separated genomes is regulated by two-way signaling. Thus, while plastid differentiation and development are largely under nuclear control, developmentally arrested or damaged plastids can regulate expression of nuclear genes via retrograde signaling pathways. In previous years of DOE-funded study, we performed a number of genetic screens that implicated the chloroplast-localized tetrapyrrole biosynthesis pathway as the source of both positive and stress-related retrograde signals. Using that work as a foundation, we have recently uncovered a new type of retrograde signal that leads to the selective removal of damaged chloroplasts from the cell. However, the mechanisms of such a chloroplast quality control pathway are not well characterized or understood.
Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.
While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.