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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 775 records · Page 43

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

MAGSAT and aeromagnetic data in the North American continent

Problems were encountered in deriving a proper reference field to be subtracted from the aeromagnetic data obtained from Project MAGNET. Field models tried thus far do not seem to eliminate properly the main field. The MAGSAT data in the North American continent for the period November 1 to December 22, 1979 are being compiled and compared with MAGNET data. Efforts are being made to eliminate the orbital bias errors. A computer program was developed and successfully tested which computes a topographic profile of the Curie depth isotherm which fits best to the observed vector or scalar field magnetic data.

Source record↗

DORMAN computer program (study 2.5). Volume 3: Original data bank listing

A number of analyses have been performed using the DORCA program for several NASA-funded Aerospace Corporation studies in the past few years. The data decks containing the input data for these analyses have been compiled and are submitted, under separate cover. A few of the data decks are full (basic) decks containing every data item and are used as reference decks in the data bank. The other data decks were obtained by differencing a full deck with respect to one of the reference decks. Using the DORMAN program, a full deck can be recreated from the modified deck and its reference deck when and if desired. The content and structure of the data bank are described. A description of each of these data decks is presented. Three of the cases that are included in this volume have become so widely recognized and accepted that additional descriptive material has been provided. The three cases are: Case 500 Costs, Case 506 Costs, and Case 403.

Stricker, L. T.↗

Existing cometary data and future needs

To assist scientists studying comets and their interaction with the interplanetary medium, compilations of existing cometary observations and data plans for additional publication are reported. The works cited include updates and/or supplements to: (1) the Catalogue of Cometary Orbits, (2) Physical Characteristics of Comets, (3) the Atlas of Representative Cometary Spectra, (4) the Atlas Cometas-Viento Solar, (5) the Isophotometrischer Atlas der Kometen, and (6) the Atlas of Cometary Forms. An Atlas of Cometary Spectra and an Atlas of Comet Halley 1910 (II) photographs and spectra are in preparation.

Rahe, J.↗

Determination of β -decay feeding patterns of 88 Rb and 88 Kr using the Modular Total Absorption Spectrometer at ORNL HRIBF

We report precise determination of ground-state feeding in the β decay of fission products is an important but challenging component in modeling reactor antineutrino flux and reactor decay heat. The Modular Total Absorption Spectrometer (MTAS) is a versatile NaI(Tl) detector array that determines the true β-decay pattern free from the pandemonium effect, including precise ground-state feeding intensities. In this paper, we report MTAS results of the β feeding intensities of 88 Rb and 88 Kr, fission products with large cumulative yields in nuclear reactors. By comparing MTAS results with previous measurements, 88 Rb provides a validation of MTAS's ability to determine ground-state feedings in β decays, while the precision of 88 Kr ground-state feeding is improved when compared with the Evaluated Nuclear Structure Data File (ENSDF). The investigation of sources that contribute to β feeding branching uncertainties in MTAS experiments is discussed in detail. Lastly, the deconvolution of 88 Rb decay spectra suggests that MTAS can distinguish an allowed β spectral shape from a first forbidden unique β spectral shape.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Shuttle ground operations efficiencies/technologies study. Volume 4: Preliminary Issues Database (PIDB) catalog

The Preliminary Issues Database (PIDB) was assembled very early in the study as one of the fundamental tools to be used throughout the study. Data was acquired from a variety of sources and compiled in such a way that the data could be easily sorted in accordance with a number of different analytical objectives. The system was computerized to significantly expedite sorting and make it more usable. The information contained in the PIDB is summarized and the reader is provided with the capability to manually find items of interest.

Scholz, A. L.↗

Constraints on rift thermal processes from heat flow and uplift

The implications of heat flow data available from five major Cenozoic continental rift systems for the processes of continental rifting are discussed, and simple thermal models of lithospheric thinning which predict uplift are used to further constrain the thermal processes in the lithosphere during rifting. Compilations of the heat flow data are summarized and the salient results of these compilations are briefly discussed. The uplift predictions of the slow and rapid thinning models, in which thinning is assumed to occur at a respectively slower and faster rate than heat can be conducted into the lithosphere, are presented. Comparison of uplift rates with model results indicates that the lithosphere is in a state between the two models. While uplift is predicted to continue after thinning has ceased due to thermal relaxation of the lithosphere, the rapid thinning model is always predicted to apply to surface heat flow, and an anomaly in this flow is not predicted to develop until after thinning has stopped.

Morgan, P.↗

Ten years in the library: new data confirm paleontological patterns

A comparison is made between compilations of times of origination and extinction of fossil marine animal families published in 1982 and 1992. As a result of ten years of library research, half of the information in the compendia has changed: families have been added and deleted, low-resolution stratigraphic data been improved, and intervals of origination and extinction have been altered. Despite these changes, apparent macroevolutionary patterns for the entire marine fauna have remained constant. Diversity curves compiled from the two data bases are very similar, with a goodness-of-fit of 99%; the principal difference is that the 1992 curve averages 13% higher than the older curve. Both numbers and percentages of origination and extinction also match well, with fits ranging from 83% to 95%. All major events of radiation and extinction are identical. Therefore, errors in large paleontological data bases and arbitrariness of included taxa are not necessarily impediments to the analysis of pattern in the fossil record, so long as the data are sufficiently numerous.

NASA Discipline Number 52-40↗

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗

A human antibody epitope map of Pfs230D1 derived from analysis of individuals vaccinated with a malaria transmission-blocking vaccine

Pfs230 domain 1 (Pfs230D1) is an advanced malaria transmission-blocking vaccine antigen demonstrating high functional activity in clinical trials. However, the structural and functional correlates of transmission-blocking activity are not defined. Here, we characterized a panel of human monoclonal antibodies (hmAbs) elicited in vaccinees immunized with Pfs230D1. These hmAbs exhibited diverse transmission-reducing activity, yet all bound to Pfs230D1 with nanomolar affinity. We compiled epitope-binning data for seventeen hmAbs and structures of nine hmAbs complexes to construct a high-resolution epitope map and revealed that potent transmission-reducing hmAbs bound to one face of Pfs230D1, while non-potent hmAbs bound to the opposing side. The structure of Pfs230D1D2 revealed that non-potent transmission-reducing epitopes were occluded by the second domain. The hmAb epitope map delineated binary hmAb combinations that synergized for extremely high-potency, transmission-reducing activity. Finally, this work provides a high-resolution guide for structure-based design of enhanced immunogens and informs diagnostics that measure the transmission-reducing response.

60 APPLIED LIFE SCIENCES↗

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗

A Curated Experimental Compilation Analyzed by Theory Is More than a Review

Macromolecules is an exceptional resource in the field of polymer science and now publishes more than 1000 original articles a year that set the standard for scientific rigor and creative insights. Over the years, these individual contributions have combined to build the foundation of polymer science, broadly and inclusively defined. In addition to the individual articles, many of which are being celebrated in this series of editorials, Macromolecules has published invaluable reviews and perspectives. These scholarly contributions integrate the insights and results from numerous sources into a unified whole and often recommend future directions for the field. Novices and experts alike benefit from these works that capture topics from emerging discoveries to long-pondered topics and everything in between. To explore the importance of Macromolecules’ reviews and perspectives, we considered their influence on the field and found the 1994 review by Fetters et al. entitled “Connection between Polymer Molecular Weight, Density, Chain Dimensions, and Melt Viscoelastic Properties”1 to be a singularity. This review expertly curates and compiles a trove of data to build robust correlations between molecular characteristics and macroscopic viscoelastic properties of polymer melts, in the context of the tube model of entanglements.

36 MATERIALS SCIENCE↗

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗

Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) – V. Group-to-cluster scale lens search from the HSC–SSP Survey

ABSTRACT We report the largest sample of candidate strong gravitational lenses belonging to the Survey of Gravitationally lensed Objects in HSC Imaging for group-to-cluster scale (SuGOHI-c) systems. These candidates are compiled from the S18A data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC–SSP) Survey. We visually inspect ∼39 500 galaxy clusters, selected from several catalogues, overlapping with the Wide, Deep, and UltraDeep fields, spanning the cluster redshift range of 0.05 < zcl < 1.38. We discover 641 candidate lens systems, of which 536 are new. From the full sample, 47 are almost certainly bona fide lenses, 181 of them are highly probable lenses, and 413 are possible lens systems. Additionally, we present 131 lens candidates at galaxy scale serendipitously discovered during the inspection. We obtained spectroscopic follow-up of 10 candidates using the X-shooter. With this follow-up, we confirm eight systems as strong gravitational lenses. Of the remaining two, one of the sources is too faint to detect any emission, and the other has a tentative redshift close to the lens redshift, but additional arcs in this system are yet to be observed spectroscopically. Since the HSC–SSP is an ongoing survey, we expect to find ∼600 definite or probable lenses using this procedure and even more if combined with other lens finding methods.

Jaelani, Anton T.↗

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗

Uncertainty quantification of optical models in fission fragment deexcitation

Here, we take the first step towards incorporating compound nuclear observables at astrophysically relevant energies into the experimental evidence used to constrain optical models, by propagating the uncertainty in two global optical potentials, one phenomenological and one microscopic, to correlated fission observables using the Monte Carlo Hauser-Feshbach formalism. We compare to a wide range of historic and recent experimental fission measurements, and discuss in detail regions of disagreement. We find that the parametric optical model uncertainty in neutron-fragment correlated observables involving neutron energy is significant. On the other hand, we observe that other experimental features, particularly neutron-fragment correlations near the 132 Sn shell closure and the high energy component of neutron spectra, are unlikely to be explained by the optical potential, and will require further experimental and theoretical effort to explain.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Parameterizations of electron scattering form factors for elastic scattering and electroexcitation of nuclear states in 27 Al and 40 Ca

Here, we report on empirical parameterizations of longitudinal ($\mathscr{R}$ L ) and transverse ($\mathscr{R}$ T ) nuclear elec- tromagnetic form factors for elastic scattering and the excitations of nuclear states in 27 Al and 40 Ca. The parameterizations are needed for the calculations of radiative corrections in measurements of electron scattering cross sections on 27 Al and 40 Ca in the quasi-elastic, resonance and inelastic con- tinuum regions, provide the contribution of nuclear excitations in investigations of the Coulomb Sum Rule, and test theoretical model predictions for excitation of nuclear states in electron and neutrino interactions on nuclear targets at low energies.

elastic scattering reactions↗

First observation of a high-𝐾 band structure in 162 Er and implications in the context of the identical bands phenomenon

The first ever identification of a high-𝐾 band structure in 162 Er is reported. Based on a 𝐾 𝜋 = 7 (−) isomer, it is found to be identical in nature to the corresponding 𝐾 𝜋 = 7 − sequence in 164 Er up to its highest observed spin. Furthermore, the phenomenon of identical high-K bands built on a two-quasiparticle configuration in an isotopic chain is reported here for the first time. While this is a notable addition to the systematics of known identical bands in nuclei at normal deformation, a satisfactory global understanding of the phenomenon remains elusive.

150 ≤ A ≤ 189↗