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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Finding a common ground for RCM experiments. Part B: Benchmark study on ethanol ignition

Rapid compression machines (RCMs) are widely used to investigate gas phase reaction kinetics of various kind of fuels at application relevant conditions. In principle, the operation of an RCM is based on the idea of compressing a homogenous pre-mixed fuel-air mixture by a piston. Usually creviced pistons ensure a homogenous adiabatic core in the center of the reaction chamber which permits the assumption of an isentropic relation between the measured pressure and gas temperature. Despite the ideal core gas compression, non-ideal effects such as heat loss, differences in the compression behavior, and ultimately non-standardized design and operation of rapid compression machines lead to different experimental results in different facilities at nominally the same end of compression conditions. In this study ignition delay times of ethanol are investigated at four different conditions in five independent RCMs. As expected, the raw results of the different facilities indeed show notable differences at the same end of compression conditions. However, according to the adiabatic core hypothesis the agreement between kinetic simulations and experiments should be consistent for all facilities provided that the facility effects are correctly accounted for. To elaborate upon this hypothesis, a kinetic mechanism is optimized to reflect the experimental results of all facilities. In the end, the optimized mechanism predicts all experimental data within the expected uncertainty. This confirms the reliability of RCM experiments for kinetic investigations and the validity of the effective volume approach in simulating RCM data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

City-level impacts of building tune-ups: Findings from Seattle's building tune-ups program

Many U.S. cities are implementing policies to reduce greenhouse gas (GHG) emissions of their buildings. These range from building energy benchmarking and disclosure to building performance standards (BPS) that require buildings to meet specific targets of energy use or emissions. The City of Seattle adopted a climate action plan in 2013 that set a goal of zero net GHG emissions in the road transportation, buildings, and waste sectors by 2050, with a number of near and long term actions. Seattle implemented mandatory building tune-ups in 2016, applying to commercial buildings larger than 50,000 sqft. Building tune-ups1 involve assessment and implementation of operational and maintenance (O + M) improvements to achieve energy and water efficiency, such as changes to thermostat set points or adjusting lighting or irrigation schedules. Seattle's tune-ups program covered 27 such improvements in HVAC, lighting, domestic hot water, and envelope systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Experiential findings for sustainable software ecosystems to support experimental and observational science

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. Here, the study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: (i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and (ii) data management requirements for data-driven science using artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees

Here we report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Exotic hidden-heavy hadrons and where to find them

The Born-Oppenheimer potentials for QCD with light quarks include adjoint-hadron potentials that are repulsive at short distances and heavy-hadron-pair potentials that approach thresholds at large distances. The adjoint-hadron potentials must connect smoothly to the heavy-hadron-pair potentials at intermediate distances. We identify exotic hidden-heavy hadrons as bound states and resonances in adjoint-hadron potentials that cross below a heavy-hadron-pair threshold before approaching it. This explains why many exotic hidden-charm and hidden-bottom hadrons have energies near heavy-hadron-pair thresholds. The remarkable properties of some exotic hidden-heavy mesons can be explained by fine tunings of adjoint-meson energies in QCD.

Born-Oppenheimer approximation↗
DOE OSTI2020

Glassy polymers: Historical findings, membrane applications, and unresolved questions regarding physical aging

Polymers below their glass transition temperature (Tg) are non-equilibrium glasses because excess free volume “frozen” between kinetically-restricted polymer chains slowly relaxes over time towards equilibrium via local, segmental chain motion. This process, known as physical aging, is observed through time-dependent decreases in a polymer's specific volume, enthalpy, etc. This article focuses on the history of glassy polymers in membrane separation applications. Open questions regarding the influence of thickness (e.g., membrane geometry) and temperature on physical aging in glassy polymers are highlighted.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Finding middle ground: Flow regimes designed for salmon and energy value

In regulated rivers, shaping seasonal flows to recover species at risk depends on understanding when to expect conflicts with competing water users and when their interests are aligned. Multi-objective optimization can be used to reveal such conflicts and commonalities. When species are involved, multi-objective optimization is challenged by the need to simulate complex species responses to flow regimes. Previously, we addressed that challenge by developing a simplified salmon model (Quantus) that defines cohorts of salmon by the river section and time in which they were spawned. Salmon in these space-time cohorts are tracked from the time redds (nests) are constructed until the cohort exits the tributary en route to the ocean. In this study, we modeled seasonal patterns in energy value and developed a Pareto-optimal frontier of seasonal flow patterns to maximize in-river salmon survival and hydropower value. Candidate flow regimes were characterized by two pulse flows varying in magnitude, timing, and duration and constrained by a total annual flow near the historical median. Our analysis revealed times when economic and salmon objectives were aligned and times when they differed. Pulse flows that favored higher energy value were timed to meet demand during extreme temperatures. Both salmon and hydropower objectives produced optimal flow regimes with pulse flows in early summer, but only solutions favoring hydropower value included high flows in mid-winter. Solutions favoring higher age-0 salmon survival provided an extended pulse flow in late winter/early spring, which suggests that access to productive floodplain habitat allowed faster growth and earlier out-migration and reduced the need for higher temperature-moderating flows later in spring. Minimum flows were also higher among solutions favoring salmon over energy. The tools used to produce these results can help to design simplified seasonal flow regimes by revealing compromise solutions that satisfy both fish and energy producers and highlighting when potential conflicts are likely.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Chemistry Graduate Student Experience: Findings from an ACS Survey

Graduate training is a key element in producing a scientific workforce that reflects the nation’s diversity. This paper examines data from a 2013 American Chemical Society (ACS) survey of 2,544 chemistry masters and doctoral students and reveals barriers to reaching this goal. Multivariate statistical analyses indicate that women reported significantly less supportive relationships with advisors. Women were less likely to plan to finish their degrees, and for PhD students, the discrepancy was larger for students at the start of their graduate program. Women were also less likely to pursue the next level of training, and the gender difference related to postdoctoral plans was greater for those who identified with a racial-ethnic group traditionally underrepresented in chemistry (underrepresented minority, URM). URM students who were beyond the first year of their graduate program reported significantly less supportive relationships with peers. They were also less likely to have funding sufficient to meet their needs and more often used personal resources including loans. Despite these difficulties, URM students were more likely to definitely plan to finish their degrees, and men who identified as URM were more likely to plan to pursue postdoctoral work. Independent of gender and identification as URMs, students in more highly ranked schools reported less advisor support. Extensive open-ended comments indicated that large proportions of the students desired more attention and meaningful feedback from advisors and changes within their programs to promote support for students and advisor accountability. Suggestions for future research are given, and a companion commentary discusses needed directions for change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Finding predictive models for singlet fission by machine learning

Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.

36 MATERIALS SCIENCE↗