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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units

Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem’s speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. Finally, this greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Investigation of Dynamic and Physical Processes in the Upper Troposphere and Lower Stratosphere

Research under this Cooperative Agreement has been funded by several NASA Earth Science programs: the Atmospheric Effects of Radiation Program (AEAP), the Upper Atmospheric Research Program (UARP), and most recently the Atmospheric Chemistry and Modeling Assessment Program (ACMAP). The purpose of the AEAP was to understand the impact of the present and future fleets of conventional jet traffic on the upper troposphere and lower stratosphere, while complementary airborne observations under UARP seek to understand the complex interactions of dynamical and chemical processes that affect the ozone layer. The ACMAP is a more general program of modeling and data analysis in the general area of atmospheric chemistry and dynamics, and the Radiation Sciences program.

Selkirk, Henry B.↗
DOE OSTI2019

Memristor design rules for dynamic learning and edge processing applications

The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2 sigma variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations of memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Quantum correlation dynamics in photosynthetic processes assisted by molecular vibrations

During the long course of evolution, nature has learnt how to exploit quantum effects. In fact, recent experiments reveal the existence of quantum processes whose coherence extends over unexpectedly long time and space ranges. In particular, photosynthetic processes in light-harvesting complexes display a typical oscillatory dynamics ascribed to quantum coherence. Here, we consider the simple model where a dimer made of two chromophores is strongly coupled with a quasi-resonant vibrational mode. We observe the occurrence of wide oscillations of genuine quantum correlations, between electronic excitations and the environment, represented by vibrational bosonic modes. Such a quantum dynamics has been unveiled through the calculation of the negativity of entanglement and the discord, indicators widely used in quantum information for quantifying the resources needed to realize quantum technologies. We also discuss the possibility of approximating additional weakly-coupled off-resonant vibrational modes, simulating the disturbances induced by the rest of the environment, by a single vibrational mode. Within this approximation, one can show that the off-resonant bath behaves like a classical source of noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2001

Spacecraft attitude control systems with dynamic methods and structures for processing star tracker signals

Methods are provided for dynamically processing successively-generated star tracker data frames and associated valid flags to generate processed star tracker signals that have reduced noise and a probability greater than a selected probability P.sub.slctd of being valid. These methods maintain accurate spacecraft attitude control in the presence of spurious inputs (e.g., impinging protons) that corrupt collected charges in spacecraft star trackers. The methods of the invention enhance the probability of generating valid star tracker signals because they respond to a current frame probability P.sub.frm by dynamically selecting the largest valid frame combination whose combination probability P.sub.cmb satisfies a selected probability P.sub.slctd. Noise is thus reduced while the probability of finding a valid frame combination is enhanced. Spacecraft structures are also provided for practicing the methods of the invention.

Liu, Yong↗
DOE OSTI2017

Probing ultra-fast processes with high dynamic range at 4th-generation light sources: Arrival time and intensity binning at unprecedented repetition rates

Here, understanding dynamics on ultrafast timescales enables unique and new insights into important processes in the materials and life sciences. In this respect, the fundamental pump-probe approach based on ultra-short photon pulses aims at the creation of stroboscopic movies. Performing such experiments at one of the many recently established accelerator-based 4th-generation light sources such as free-electron lasers or superradiant THz sources allows an enormous widening of the accessible parameter space for the excitation and/or probing light pulses. Compared to table-top devices, critical issues of this type of experiment are fluctuations of the timing between the accelerator and external laser systems and intensity instabilities of the accelerator-based photon sources. Existing solutions have so far been only demonstrated at low repetition rates and/or achieved a limited dynamic range in comparison to table-top experiments, while the 4th generation of accelerator-based light sources is based on superconducting radio-frequency technology, which enables operation at MHz or even GHz repetition rates. In this article, we present the successful demonstration of ultra-fast accelerator-laser pump-probe experiments performed at an unprecedentedly high repetition rate in the few-hundred-kHz regime and with a currently achievable optimal time resolution of 13 fs (rms). Our scheme, based on the pulse-resolved detection of multiple beam parameters relevant for the experiment, allows us to achieve an excellent sensitivity in real-world ultra-fast experiments, as demonstrated for the example of THz-field-driven coherent spin precession.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors

The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗
DOE OSTI2023

GPLaSDI: Gaussian Process Latent Space Dynamics Identification

GPLaSDI is a software that introduces GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the reduced order model (ROM) predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. We demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

Choi, Youngsoo↗
DOE OSTI2026

Systematic analysis of melt pool dynamics in laser processing of mixed powder feedstocks

Functionally graded materials (FGMs) fabricated via additive manufacturing of blended powders offer the potential to spatially tailor properties for new technologies, such as fusion first-wall systems, turbine blades, and spacecraft. However, processing these materials is difficult due to the multiplicity of processing parameters to optimize, all of which must be changed as substrate material, powder feedstock compositions, and melt pool dynamics evolve. Here, this work systematically evaluates the qualitative and quantitative effects of these variables on the melt pool size, shape, composition, and particle distribution in an exemplar Ti-Ta system, and connects the experimental results to Marangoni flow behavior and phenomena observed in other systems. Increasing laser power linearly increases melt pool size and layer thickness, driving engineering considerations such as part/geometrical tolerances. Decreasing laser velocity changes the melt pool shape from lenticular to convex and reduces chemical homogeneity due to extreme thermal and compositional gradients between the melt pool center and boundaries. Thermophysical property differences between the powder feedstock and substrate material, as well as the directionality of the gradient, affect dilution and melt pool dynamics, which in turn affect the melt pool boundary characteristics, shape, and uniformity. Mixed powder feedstocks of intermediate compositions do not behave according to linear interpolations between single-material endpoints, instead building taller and wider melt pools. As such, it is recommended to quantify process maps for at least one intermediate composition in the FGM or multi-material system of interest to ensure optimized processing parameters, predictable melt pool sizes and shapes, and compositional and spatial precision.

Dissimilar↗
NASA NTRS1990

Convective dynamics

In addressing meteorological processes, including convection, it is convenient to separate these processes into their kinematic, dynamic, and microphysical components. Dynamic processes deal with the forces that initiate and sustain or alter motion, while kinematic processes describe the motion field. Microphysical processes address the distribution of water substance. This review of convective dynamics is organized along the following lines. First is an assessment of radar capability, particularly the use of one or more Doppler radars, to portray the wind field. Then various types of precipitating moist convection are reviewed. Each type or mode of convection results from a distinct environment. The principal interest of dynamics here is its role in the convection's evolution, structure, and movement. Thus, the important processes of storm rotation, splitting and propagation, including tornadogenesis, are examined.

Ray, Peter S.↗
DOE OSTI2023

Antarctic permafrost processes and antiphase dynamics of cold-based glaciers in the McMurdo Dry Valleys inferred from 10 Be and 26 Al cosmogenic nuclides

Abstract. Soil and sediment mixing and associated permafrost processes are not widely studied or understood in the McMurdo Dry Valleys of Antarctica. In this study, we investigate the stability and depositional history of near-surface permafrost sediments to ∼ 3 m depth in the Pearse and lower Wright valleys using measured cosmogenic 10Be and 26Al depth profiles. In Pearse Valley, we estimate a minimum depositional age of ∼ 74 ka for the active layer and paleoactive-layer sediments (< 0.65 m). Combined depth profile modelling of 10Be and 26Al gives a depositional age for near-surface (< 1.65 m) permafrost in Pearse Valley of 180 +20/-40 ka, implying that the deposition of permafrost sediments predates MIS 5 advances of Taylor Glacier. Deeper permafrost sediments (> 2.09 m) in Pearse Valley are thus inferred to have a depositional age of > 180 ka. At a coastal, lower-elevation site in neighbouring lower Wright Valley, 10Be and 26Al depth profiles from a second permafrost core exhibit near-constant concentrations with depth and indicate the sediments are either vertically mixed after deposition or sufficiently young so that post-depositional nuclide production is negligible relative to inheritance. 26Al/10Be concentration ratios for both depth profiles range between 4.0 and 5.2 and are all lower than the nominal surface production rate ratio of 6.75, indicating that prior to deposition, these sediments experienced complex, yet similar, exposure–burial histories. Assuming a single-cycle exposure–burial scenario, the observed 26Al/10Be ratios are equivalent to a total minimum exposure–burial history of ∼ 1.2 Myr. In proximity to the depth profile core site, we measured cosmogenic 10Be and 26Al in three granite cobbles from thin, patchy drift (Taylor 2 Drift) in Pearse Valley to constrain the timing of retreat of Taylor Glacier. Assuming simple continuous exposure, our minimum, zero-erosion exposure ages suggest Taylor Glacier partially retreated from Pearse Valley no later than 65–74 ka. The timing of retreat after 65 ka and until the Last Glacial Maximum (LGM) when Taylor Glacier was at a minimum position remains unresolved. The surface cobble ages and permafrost processes reveal Taylor Glacier advances during MIS 5 were non-erosive or mildly erosive, preserving the underlying permafrost sediments and peppering boulders and cobbles upon an older, relict surface. Our results are consistent with U/Th ages from central Taylor Valley and suggest changes in moisture delivery over Taylor Dome during MIS 5e, 5c, and 5a appear to be associated with the extent of the Ross Ice Shelf and sea ice in the Ross Sea. These data provide further evidence of antiphase behaviour through retreat of a peripheral lobe of Taylor Glacier in Pearse Valley, a region that was glaciated during MIS 5. We suggest a causal relationship of cold-based glacier advance and retreat that is controlled by an increase in moisture availability during retreat of sea ice and perhaps the Ross Ice Shelf, as well as, conversely, a decrease during times of sea ice and Ross Ice Shelf expansion in the Ross Sea.

58 GEOSCIENCES↗
DOE OSTI2016

Ferroelectric molecular field-switch based on double proton transfer process: Static and dynamical simulations

In this work, we present a reversible ferroelectric molecular switch controlled by an external electric field. The studied (2Z)-1-(6-((Z)-2-hydroxy-2-phenylvinyl)pyridin-3-yl)-2-(pyridin-2(1H) -ylidene)ethanone (DSA) molecule is polarized by two uniaxial intramolecular hydrogen bonds. Two protons can be transferred along hydrogen bonds upon an electric field applied along the main molecular axis. The process results in reversion of the dipole moment of the system. Static ab initio and on-the-fly dynamical simulations of the DSA molecule placed in an external electric field give insight into the mechanism of the double proton transfer (DPT) in the system and allow for estimation of the time scale of this process. The results indicate that with increasing strength of the electric field, the step-wise mechanism of DPT changes into the downhill barrierless process in which the synchronous and asynchronous DPTs compete with each other.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2010

Onboard Processing of Multispectral and Hyperspectral Data of Volcanic Activity for Future Earth-Orbiting and Planetary Missions

Autonomous onboard processing of data allows rapid response to detections of dynamic, changing processes. Software that can detect volcanic eruptions from thermal emission has been used to retask the Earth Observing 1 spacecraft to obtain additional data of the eruption. Rapid transmission of these data to the ground, and the automatic processing of the data to generated images, estimates of eruption parameters and maps of thermal structure, has allowed these products to be delivered rapidly to volcanologists to aid them in assessing eruption risk and hazard. Such applications will enhance science return from future Earth-orbiting spacecraft and also from spacecraft exploring the Solar System, or beyond, which hope to image dynamic processes. Especially in the latter case, long communication times between the spacecraft and Earth exclude a rapid response to what may be a transient process - only using onboard autonomy can the spacecraft react quickly to such an event.

Jupiter↗
NASA NTRS2013

Carbon nanotube vacuum gauges with wide-dynamic range and processes thereof

A miniature thermal conductivity gauge employs a carbon single-walled-nanotube. The gauge operates on the principle of thermal exchange between the voltage-biased nanotube and the surrounding gas at low levels of power and low temperatures to measure vacuum across a wide dynamic range. The gauge includes two terminals, a source of constant voltage to the terminals, a single-walled carbon nanotube between the terminals, a calibration of measured conductance of the nanotube to magnitudes of surrounding vacuum and a current meter in electrical communication with the source of constant voltage. Employment of the nanotube for measuring vacuum includes calibrating the electrical conductance of the nanotube to magnitudes of vacuum, exposing the nanotube to a vacuum, applying a constant voltage across the nanotube, measuring the electrical conductance of the nanotube in the vacuum with the constant voltage applied and converting the measured electrical conductance to the corresponding calibrated magnitude of vacuum using the calibration. The nanotube may be suspended to minimize heat dissipation through the substrate, increasing sensitivity at even tower pressures.

Manohara, Harish↗
DOE OSTI2019

MODELING THE DYNAMICS OF MATERIALS PROCESSING AND ELECTROCHEMICAL SYSTEMS FOR ENERGY STORAGE APPLICATIONS

To realize the promise of electric vehicles, personal electronics, and renewable energy sources, improvements in energy storage technology are crucial. However, avenues for improvement such as designing superior electrode materials or modifying the battery electrolyte and separator are difficult without sufficient understanding of the materials and processes used to produce them. One such process is electrospinning, which uses high voltage to produce polymer nanofibers cheaply and continuously. To examine the effect of electrospinning on material morphology, the placement of nanoparticles within lamella-forming polymer blends and block copolymers is simulated using molecular dynamics under planar elongational flow. Then, extensional flow is combined with cylindrical confinement to simulate a block copolymer-filled cylinder with a constantly shrinking diameter, which is a better model for electrospinning. The appearance of ordered or disordered morphologies is found to be connected to a Weissenberg number relating the deformation and self-assembly time scales. A second approach towards understanding energy storage systems is to simulate the battery’s electrochemical performance. Of particular interest is the lithium-sulfur (Li-S) battery, which aims to replace lithium-ion batteries due to its higher specific capacity and cheaper material costs. However, well-known challenges such as polysulfide crossover, large volume changes during cycling, and deposition of insulating lithium sulfide limit the capacity and capacity retention of Li-S batteries. Using a continuum-level numerical simulation which includes reaction, mass transport, adsorption, and nucleation, these challenges are investigated in order to explain the mechanisms underlying Li-S discharge behavior. The model is used to explain the end-of-discharge failure behavior of liquid and gel electrolyte systems, finding that insufficient mass transport of polysulfides is the cause. In addition, the effects of adsorptive materials, cathode passivation with insulating lithium sulfide, and spherical carbon cathode particle geometry is investigated. It is found that adsorptive materials can greatly improve battery performance when the location of lithium sulfide deposition is considered in the model. Finally, the model suggests that different cathode processing techniques in experiment can lead to different end-of-discharge failure mechanisms.

25 ENERGY STORAGE↗