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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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Allow myself to email Scott Ullsperger the training document to allow him to become more familiar with what was updated with the Core Internal Change out (CIC) for ATR. Scott is currently working on getting set up as a sub contractor. Scott Ullsperger is a retired BEA employee who has a thorough knowledge of the reactor. Scott has been selected to participate in the MSA for reactor start up.
Data, geospatial data resources, and the linked mapping tool and web services reflect data for two types of potentially qualifying energy communities: 1) Census tracts and directly adjoining tracts that have had coal mine closures since 1999 or coal-fired electric generating unit retirements since 2009. These census tracts qualify as energy communities. 2) Metropolitan statistical areas (MSAs) and non-metropolitan statistical areas (non-MSAs) that are energy communities for 2023 and 2024, along with their fossil fuel employment (FFE) status. Additional information on energy communities and related tax credits can be accessed on the Interagency Working Group on Coal & Power Plant Communities & Economic Revitalization Energy Communities website (https://energycommunities.gov/energy-community-tax-credit-bonus/). Use limitations: these spatial data and mapping tool may not be relied upon by taxpayers to substantiate a tax return position or for determining whether certain penalties apply and will not be used by the IRS for examination purposes. The mapping tool does not reflect the application of the law to a specific taxpayer’s situation, and the applicable Internal Revenue Code provisions ultimately control.
Oceanic emissions of dimethyl sulfide (DMS) have long been known to influence aerosol particle composition, cloud condensation nuclei (CCN) concentration, and Earth’s radiative budget. However, the impact of oceanic emissions of methanethiol (MeSH), a sulfur compound produced by the same oceanic precursor as DMS, has been relatively less explored. The gas-phase oxidation of MeSH has a higher effective yield of SO 2 and a shorter oxidative lifetime compared to DMS, highlighting the relevance of this pathway for the modeled representation of particle formation, growth, and CCN abundance in the marine atmosphere. Here, we use the global chemical transport model GEOS-Chem to explore possible scenarios representative of specific environmental conditions and MeSH emission schemes based on previous experimental studies. We further implement and test previously reported chemical mechanisms for MeSH oxidation, along with additional improvements, highlighting key uncertainties and sensitivities for regional and global sulfur budgets. We place our results in the context of recent modeling updates to DMS chemistry and cloud processing, which further impact SO 2 production in the marine atmosphere in parallel with MeSH oxidation. Within the overall marine sulfur budget, our findings highlight that MeSH plays a significant role in SO 2 production in the marine atmosphere, contributing to regional surface layer concentration increases of up to 40–60%. These results point to the importance of MeSH for efforts aimed at improving the modeled representation of sulfur spatiotemporal patterns relevant to air quality predictions and climate impact assessments.
This comprehensive analysis aimed to evaluate the measurement capability of the AMPTEC 630ES Explosive Safety Igniter Tester, a critical tool for organizations engaged in the measurement of High Explosive components and related Electro-Explosive devices. With a history of notable calibration issues involving specific model numbers, the Metrology Program and Calibration Laboratory (MPCL) initiated an extensive Measurement System Analysis. The analysis's primary focus was to thoroughly quantify the various sources of measurement variability intrinsic to the calibration process. It centered on two pivotal aspects: repeatability (the variation observed when the same operator uses the same instrument) and reproducibility (the variation seen when different operators perform measurements). A secondary objective was to determine whether equipment usage and age had any impact on the instrument's performance.
Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (G×E×T) and (2) two single-trait multi-environment (STME) models (with and without G×E interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.