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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering

We combine deep Gaussian processes (DGPs) with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian processes (GPs) with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems. In this article, we combine DGPs with multitask and transfer learning to allow for both an improved tuning of an application parameters on problems of interest but also the prediction of parameters on any potential problem the application might encounter.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Thermal Performance Expectations of the Advanced Stirling Convertor Over a Range of Operating Scenarios

The Advanced Stirling Radioisotope Generator (ASRG) will enable various missions such as small body sample return, atmospheric missions around Venus, as well as long - duration deep space missions. Analysis of the temperature distributions are performed on an Advanced Stirling Convertor, and the results are compared with available experimental measurements. This analysis includes applied environmental conditions that are similar to those that will be experienced while the convertor is in operation. The applied conditions represent a potential mission profile including pre-takeoff sterilization, launch, transit, and return. The results focus on the anticipated peak temperatures of the magnets in the linear alternator. These results confirm that the ASC can support future missions to deep space targets, extreme environment landers, as well as more conventional goals.

Reid, Terry V.↗
DOE OSTI2024

Physics Potential of a Future Large n Detector in Korea

The demand for underground labs for neutrino and rare event search experiments has been increasing over the last few decades. Yemilab, constructed in October 2022, is the first deep (1~km) underground lab dedicated to science in Korea, where a large cylindrical cavern (D: 20~m, H: 20~m) was excavated in addition to the main caverns and halls. The large cavern could be utilized for a low background neutrino experiment by a liquid scintillator-based detector (LSC) where a 2.26 kiloton LS target would be filled.It's timely to have such a large but ultra-pure LS detector after the shutdown of the Borexino experiment so that solar neutrinos can be measured much more precisely. Interesting BSM physics searches can be also pursued with this detector when it's combined with an electron linac, a proton cyclotron (IsoDAR source), or a radioactive source. This talk discusses the concept of a candidate detector and the physics potential of a large liquid scintillator detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A benchmark dataset for Hydrogen Combustion

The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ~290,000 potential energies and ~1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction.

08 HYDROGEN↗
DOE OSTI2019

Root-driven weathering impacts on mineral-organic associations in deep soils over pedogenic time scales

Plant roots are primary weathering agents in deep soils, yet the impact of resulting mineral transformations on the vast deep soil carbon (C) reservoir are largely unknown. Root-driven weathering of primary minerals causes the formation of reactive secondary minerals, which may protect mineral-organic associations (MOAs) for centuries or millennia. Conversely, root-driven weathering may also transform secondary minerals, potentially enhancing the bioavailability of C previously protected in MOAs. Here we examined the impact of root-driven weathering on MOAs and their capacity to store C over pedogenic time scales. To accomplish this, we compared soil that experienced root-driven weathering, resulting in the formation of discrete rhizosphere zones in deep soil horizons (100-160 cm) of the Santa Cruz Marine Terrace chronosequence (65ka-226ka), with adjacent soil that experienced no root growth. Using a combination of radiocarbon, mass spectrometry, Mössbauer spectroscopy, and X-ray spectromicroscopy approaches, we characterized transformations of mineral-organic associations in relation to changes in C content, turnover and chemistry across four soils ranging in age from 65 to 226 ka. We found that the onset of root-driven weathering (65-90ka) increased the amount of C associated with poorly crystalline iron (Fe) and aluminum (Al) phases, particularly highly-disordered np-goethite. This increase coincided with greater C concentrations, longer C residence times, and a greater abundance of microbially-derived C. Continued root-driven weathering (137-226ka) did not significantly change the amount of C associated with crystalline Fe and Al phases, but resulted in a decline in the amount of C associated with poorly crystalline metal phases. This decline coincided with a decrease in C concentrations and potential turnover rates, and a shift toward plant-derived C. In contrast, soil not affected by root-driven weathering showed comparatively low amounts of C bound in poorly crystalline Fe and Al phases regardless of soil age and, correspondingly, lower C concentrations and estimated residence times. Furthermore, our results demonstrate that root driven-formation and disruption of poorly crystalline Fe and Al phases is a direct control on both C accrual and loss in deep soil. This finding suggests that root impacts on soil C storage are dependent on soil weathering stage, a consideration that is critical for future predictions of the vulnerability of deep soil C to global change.

58 GEOSCIENCES↗
DOE OSTI2024

Understanding the High Energy Higgs Sector with the CMS Experiment and Artificial Intelligence

This dissertation describes efforts towards understanding the Higgs boson at the highest energies humanly accessible, using the CMS experiment at the Large Hadron Collider and advances in artificial intelligence (AI) and machine learning (ML). We present searches for resonant and nonresonant Higgs-boson (H) pair production in the all-hadronic two beauty-quark and two vector boson (V) final state, using a novel strategy to measure the quartic HHVV coupling and search for new Higgs-like bosons. By targeting highly Lorentz-boosted Higgs pairs, we probe effects of potential new physics in the high energy Higgs sector, which could hold answers to fundamental mysteries of nature such as baryon asymmetry. To enable these and future searches, we introduce as well significant developments in AI/ML, including in the identification of boosted H$\rightarrow$VV decays with deep transformer networks and advances in AI-accelerated fast simulations of the CMS detector. The latter notably includes the development of the first, highly performant generative models for point-cloud data in high energy physics, which have the potential to improve CMS' computational efficiency by up to three orders of magnitude. We also highlight novel solutions to the important and challenging problems of calibrating and validating these ML techniques. Finally, we present new approaches to search for new physics in a model-agnostic manner, using physics-informed ML methods equivariant to Lorentz transformations. The quartic HHVV coupling is observed (expected) to be constrained to $[-0.04, 2.05]$ ($[0.05, 1.98]$) at the 95% confidence level relative to the standard model prediction, representing the second-most sensitive measurement of this coupling by CMS to date. Exclusion limits on the production cross section of new heavy resonances decaying to two Higgs-like bosons are expected to be as low as 0.3 fb for high resonance masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

From lattice QCD to in-medium heavy-quark interactions via deep learning

Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks, which can be characterized by real (V R (T, r)) and imaginary (V I (T, r)) potentials, as functions of temperature and spatial separation. Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network (DNN), here, we obtain model-independent results for V R (T, r) and V I (T, r). The temperature dependence of V R (T, r) was found to be very mild between T ≈ 0 - 330 MeV. Meanwhile, VI(T, r) shows rapid increase with T and r, which is much larger than the perturbation theory based expectations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Induced seismicity potential based on probabilistic geomechanics for the San Juan Basin CarbonSAFE project

Although geologic carbon sequestration projects have yet to induce — or may never induce — a damaging earthquake, experiences from other deep injection industries such as hydraulic fracturing, enhanced geothermal systems, and saltwater disposal suggest that effective quantitative seismic risk assessment is necessary for deep saline carbon capture and sequestration (CCS) projects. One such imminent CCS project is the San Juan Basin CarbonSAFE Phase III program. We use Monte Carlo probabilistic geomechanical analyses combined with observations of the geologic and operational parameters of the San Juan Basin site and suggest that this project is of low-induced seismic risk. The primary analysis is split into four sections. First, we assess the literature for faults and past seismicity, and at least five faulting scenarios are directly relevant. Second, we develop and calibrate an integrated earth model for the project site. Third, we perform Monte Carlo simulations that consider reasonable uncertainties of the geomechanical parameters. Of five tested faulting scenarios, only one presents high Coulomb failure functions, but fourth, we determine the risk to be low based on the combined lack of historical seismicity, the geologic framework of the faulting scenario, and the presence of saltwater injection at the same depth as the proposed supercritical carbon dioxide injection. The most sensitive parameters in the geomechanical calculations are the fault dip and the coefficient of friction. The least sensitive are the fault strike and the orientation of the maximum horizontal principal stress.

58 GEOSCIENCES↗
NASA NTRS1999

Polar Stratospheric Descent of NO(y) and CO and Arctic Denitrification During Winter 1992-1993

Observations inside the November 1994 Antarctic stratospheric vortex and inside the April 1993 remnant Arctic stratospheric vortex by the Atmospheric Trace Molecule Spectroscopy (ATMOS) Fourier transform spectrometer are reported. In both instances, elevated volume mixing ratios (VMRS) of carbon monoxide (CO) were measured. A peak Antarctic CO VMR of 60 ppbv (where 1 ppbv = 10(exp -9) per unit Volume) was measured at a potential temperature of 710 K (about 27 km), about 1 km below the altitude of a pocket of elevated NO(y) (total reactive nitrogen) at a deep minimum in N2O (<5 ppbv). The Arctic observations also show a region of elevated vortex CO with a peak VMR of 90 ppbv it 630-670 K (-25 km) but no corresponding enhancement in NO(sub y) perhaps because of stronger dynamical activity in the northern hemisphere polar winter and/or interannual variability in the production of mesospheric or lower thermospheric NO. By comparing vortex and extravortex observations of NO(y) obtained at the same N2O VMR, Arctic vortex denitrification of 5 +/- 2 ppbv at 470 K (at approximately 18 km) is inferred. We show that our conclusion of substantial Arctic winter 1992-1993 denitrification is robust by comparing our extravortex observations with previous polar measurements obtained over a wide range of winter conditions. Correlations of NO(y) with N2O measured at the same potential temperature by ATMOS in the Arctic vortex and at midlatitudes on board the ER-2 aircraft several weeks later lie along the same mixing line. The result demonstrates the consistency of the two data sets and confirms that the ER-2 sampled fragments of the denitrified Arctic vortex following its breakup. An analysis of the ATMOS Arctic measurements of total hydrogen shows no evidence for significant dehydration inside the vortex.

Rinsland, C. P.↗
DOE OSTI2015

Cold and ultracold dynamics of the barrierless D{sup +} + H{sub 2} reaction: Quantum reactive calculations for ∼R{sup −4} long range interaction potentials

Quantum reactive and elastic cross sections and rate coefficients have been calculated for D{sup +} + H{sub 2} (v = 0, j = 0) collisions in the energy range from 10{sup −8} K (deep ultracold regime), where only one partial wave is open, to 150 K (Langevin regime) where many of them contribute. In systems involving ions, the ∼R{sup −4} behavior extends the interaction up to extremely long distances, requiring a special treatment. To this purpose, we have used a modified version of the hyperspherical quantum reactive scattering method, which allows the propagations up to distances of 10{sup 5} a{sub 0} needed to converge the elastic cross sections. Interpolation procedures are also proposed which may reduce the cost of exact dynamical calculations at such low energies. Calculations have been carried out on the PES by Velilla et al. [J. Chem. Phys. 129, 084307 (2008)] which accurately reproduces the long range interactions. Results on its prequel, the PES by Aguado et al. [J. Chem. Phys. 112, 1240 (2000)], are also shown in order to emphasize the significance of the inclusion of the long range interactions. The calculated reaction rate coefficient changes less than one order of magnitude in a collision energy range of ten orders of magnitude, and it is found in very good agreement with the available experimental data in the region where they exist (10-100 K). State-to-state reaction probabilities are also provided which show that for each partial wave, the distribution of HD final states remains essentially constant below 1 K.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Genomic comparisons of a bacterial lineage that inhabits both marine and terrestrial deep subsurface systems

It is generally accepted that diverse, poorly characterized microorganisms reside deep within Earth’s crust. One such lineage of deep subsurface-dwelling Bacteria is an uncultivated member of the Firmicutes phylum that can dominate molecular surveys from both marine and continental rock fracture fluids, sometimes forming the sole member of a single-species microbiome. Here, we reconstructed a genome from basalt-hosted fluids of the deep subseafloor along the eastern Juan de Fuca Ridge flank and used a phylogenomic analysis to show that, despite vast differences in geographic origin and habitat, it forms a monophyletic clade with the terrestrial deep subsurface genome of “ Candidatus Desulforudis audaxviator” MP104C. While a limited number of differences were observed between the marine genome of “ Candidatus Desulfopertinax cowenii” modA32 and its terrestrial relative that may be of potential adaptive importance, here it is revealed that the two are remarkably similar thermophiles possessing the genetic capacity for motility, sporulation, hydrogenotrophy, chemoorganotrophy, dissimilatory sulfate reduction, and the ability to fix inorganic carbon via the Wood-Ljungdahl pathway for chemoautotrophic growth. Our results provide insights into the genetic repertoire within marine and terrestrial members of a bacterial lineage that is widespread in the global deep subsurface biosphere, and provides a natural means to investigate adaptations specific to these two environments.

Jungbluth, Sean P.↗
DOE OSTI2021

hippynn Python Package

hippynn is a python package for defining, training, and applying neural networks to atomistic systems. In particular, it focuses on Hierarchical Interacting Particle Neural Networks (HIP-NNs), a deep learning architecture for atomistic systems. HIP-NNs take input data describing the properties of atomistic systems (often generated using ab-initio quantum mechanics) to learn fast and accurate models. A trained HIP-NN can predict potential energy surfaces, atomic charges, and more. hippynn uses PyTorch for portable high-performance code, including both CPU and GPU support. hippynn allows for extensive customization, including user-defined models and loss functions, to facilitate future research into extensions of HIP-NN as well as other atomistic deep learning models.

Lubbers, Nicolas↗
DOE OSTI2024

Forecast of Wildfire Potential Across California USA Using a Transformer

Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

Limber, Russell [ORNL]↗
DOE OSTI2024

Geothermal Reservoir Characterization at Wattenberg Area in Denver Basin

The geothermal potential of the Denver-Julesburg (D-J) Basin is largely overlooked. This study focuses on characterizing the geothermal potential in the greater Wattenberg area within the Denver Basin, using available well data and subsurface information and gas wells drilled to above 2500 m deep. The review reveals that the main structures comprise ENE-trending wrench faults, along with NNE, N, and NNW trending normal faults. A thermal anomaly is present bonded by Lafayette and Longmont wrench faults and overlying vitrinite reflectance anomaly. The geometry of thermal anomaly suggests that it is influenced by Longmont wrench fault and Lafayette fault, as well as the basin margin fault. Bottom hole temperatures are of 160 Degrees Celsius, with thermal gradients of and 62 Degrees Celsius/km. Reservoir temperature estimates from thermal gradients suggest a range of >200 Degrees Celsius at depths of 3 km.

Denver basin↗
DOE OSTI2018

Atomic-Scale Observation of Electrochemically Reversible Phase Transformations in SnSe 2 Single Crystals

2D materials have shown great promise to advance next-generation lithium-ion battery technology. Specifically, tin-based chalcogenides have attracted widespread attention because lithium insertion can introduce phase transformations via three types of reactions—intercalation, conversion, and alloying—but the corresponding structural changes throughout these processes, and whether they are reversible, are not fully understood. Here, the first real-time and atomic-scale observation of reversible phase transformations is reported during the lithiation and delithiation of SnSe 2 single crystals, using in situ high-resolution transmission electron microscopy complemented by first-principles calculations. Lithiation proceeds sequentially through intercalation, conversion, and alloying reactions (SnSe 2 → Li x SnSe 2 → Li 2 Se + Sn → Li 2 Se + Li 17 Sn 4 ) in a manner that maintains structural and crystallographic integrity, whereas delithiation forms numerous well-aligned SnSe 2 nanodomains via a homogeneous deconversion process, but gradually loses the coherent orientation in subsequent cycling. Furthermore, alloying and dealloying reactions cause dramatic structural reorganization and thereby consequently reduce structural stability and electrochemical cyclability, which implies that deep discharge for Sn chalcogenide electrodes should be avoided. Overall, the findings elucidate atomistic lithiation and delithiation mechanisms in SnSe 2 with potential implications for the broader class of 2D metal chalcogenides.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantum Chemical Simulations of CO 2 and N 2 Capture in Reline, a Prototypical Deep Eutectic Solvent

Deep eutectic solvents such as reline are an emerging class of low-cost, environmentally friendly solvents with tunable properties that are potentially applicable for the capture and separation of CO 2 . Experimental measurements showed that a reline-based membrane contactor can capture and separate CO 2 via physisorption through a dissolution process with 96.7% purity from a mixed gas containing CO 2 and N 2 (50:50% molar ratio). Here, we examine the nature of the interaction of CO 2 and N 2 with reline employing quantum chemical methods. We focus on explaining the mechanism by which CO 2 and N 2 bind to reline and the reason for the high selectivity for absorption of CO 2 compared to N 2 . We analyze the dynamics, energetics, and binding motifs for CO 2 and N 2 in reline employing density functional theory, density functional tight binding, and ab initio molecular dynamics. We also investigate the effect of reline on the vibrational spectra of CO 2 and reline. Our simulations indicate that the selective capture of CO 2 from the mixture of CO 2 and N 2 is due to the interplay between attractive electrostatic and charge polarization forces with opposing entropic effects, which shift the energetic balance and make the N 2 absorption unfavorable in reline.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗