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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Single radio frequency bucket injection in the 88-Inch Cyclotron using a pulsed high voltage chopper

A single radio frequency bucket of the 88-Inch Cyclotron is filled using a fast chopper located in the axial line. The bucket then accelerates until it reaches the deflector, at which point, it is extracted as a train of bunches. This phenomenon can be attributed to the characteristic multi-turn extraction of the cyclotron and, by simplifying the complex dynamics of a cyclotron, corresponds to the conceptual transfer function of the cyclotron. In conclusion, the confirmation of the single radio frequency bucket injection was achieved by operating the cyclotron in the third harmonic mode and observing the absence of intermediate bunches during the multiple-bunch extraction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
DOE OSTI2020

Extracting Optical Bandgaps from Luminescence Images of Perovskite Solar Cells

We report a camera-based method to capture optical bandgap images of perovskite solar cells (PSCs). It is fast and non-destructive. It can also provide micron-scale spatial resolution. This imaging technique utilizes well-defined and relatively symmetrical band-to-band luminescence spectra emitted from perovskite materials. Their spectra can be fitted with Gaussian functions whose peak locations are similar to absorption thresholds and thus represent the material optical bandgaps. We apply the technique to resolve relative variations in optical bandgaps across various PSCs, and to show optical bandgap inhomogeneity within the same device due to material degradation and impurities. Degradation and impurities were found to both cause optical bandgap shifts inside the materials. Our results are independently confirmed with photoluminescence and absorption spectroscopy.

14 SOLAR ENERGY↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Photoinduced charge transfer in transition metal dichalcogenide heterojunctions – towards next generation energy technologies

Increasing interest in using two-dimensional transition metal dichalcogenides (2D TMDCs) in optical energy conversion technologies creates a demand for improving the yields and lifetimes of photogenerated charge carriers. Despite inherently fast photocarrier decay in neat 2D TMDCs, the unique photophysics in these quantum-confined systems motivates continued effort to control the evolution of photoexcited states and create functional devices. Here, an intriguing strategy to accomplish this goal is to employ TMDCs in heterojunctions with appropriate semiconductors, where energy level offsets drive photoinduced charge transfer (PCT) across material interfaces. PCT in TMDC-based systems can be optimized for many different applications, such as driving free carriers to photocatalytic sites for redox reactions like water splitting, extracting charge to perform work in photovoltaics or photodetectors, and manipulating the spin and momentum valley electronic degrees of freedom for quantum computing systems. Here, we review recent strides in optimizing PCT for such applications through greater fundamental understanding of the photophysics that occurs at TMDC/semiconductor interfaces. After giving an overview of isolated TMDC properties, synthetic methods, and the basics of PCT, we discuss TMDCs in heterojunctions with several classes of materials, including other TMDCs, small molecule semiconductors, polymers, single-walled carbon nanotubes, quantum dots, perovskites, and electrolytes. In addition to highlighting the unique benefits of each materials category, we also identify parallels across common themes, such as the roles of charge-transfer states, spin, electronic coupling, delocalization, interfacial atomic morphology, and the precise design of energy landscapes to direct charge and energy motion. We hope to capture a broad range of the valuable work in this fast-paced field to inspire new research directions for employing PCT in targeted TMDC-based systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

A nondestructive method to estimate the chlorophyll content of Arabidopsis seedlings

Chlorophyll content decreases in plants under stress conditions, therefore it is used commonly as an indicator of plant health. Arabidopsis thaliana offers a convenient and fast way to test physiological phenotypes of mutations and treatments. But, chlorophyll measurements with conventional solvent extraction are not applicable to Arabidopsis leaves due to their small size, especially when grown on culture dishes. We provide a nondestructive method for chlorophyll measurement whereby the red, green and blue (RGB) values of a color leaf image is used to estimate the chlorophyll content from Arabidopsis leaves. The method accommodates different profiles of digital cameras by incorporating the ColorChecker chart to make the digital negative profiles, to adjust the white balance, and to calibrate the exposure rate differences caused by the environment so that this method is applicable in any environment. We chose an exponential function model to estimate chlorophyll content from the RGB values, and fitted the model parameters with physical measurements of chlorophyll contents. As further proof of utility, this method was used to estimate chlorophyll content of G protein mutants grown on different sugar to nitrogen ratios. Our method is a simple, fast, inexpensive, and nondestructive estimation of chlorophyll content of Arabidopsis seedlings. This method lead to the discovery that G proteins are important in sensing the C/N balance to control chlorophyll content in Arabidopsis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Online Measurement of the Energy Spread of Multi-Turn Beam in the Fermilab Booster at Injection

We have developed a computer program interfaced with the ACNET environment of Fermilab accelerators to measure energy spread of the proton beam from the LINAC at an injection into the Booster. It uses a digitizing oscilloscope and provides users an ability to configure the scope settings for optimal data acquisition from a resistive wall current monitor. When the program is launched, a) a one shot timeline is generated to initiate beam injection into the Booster, b) a gap of about 40 ns is produced in the injected beam using a set of fast kickers, c) collects line charge distribution data from the wall current monitor for the first 200 μs from the injection and d) performs complete data analysis to extract full beam energy spread of the beam. The program also gives the option to store the data for offline analyses. We illustrate a case with an example. We also present results on beam energy spread as a function of beam intensity from recent measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Multimodal photon and neutron imaging devices with fast event-by-event multisampling readout and optimized particle type discrimination

A particle imaging method for distinguishing between types of incident particles, such as neutrons, photons, and alphas, and improving the position resolution of particle imaging devices with matrix readout. The method includes high frequency multisampling readout electronics that provides the sequences of multiple measurements for each detected event, resulting in recorded detailed waveform information describing the signals. Such detailed information is used to approximate each signal waveform with a parameterized function in which the extracted parameter sets determine the type of the incident particle in an optimized fashion. The detailed event-by-event multisampling information for each signal readout channel in the matrix readout of the radiation imaging devices improves and optimizes the position resolution for variable shapes of the signals. Such devices can be used in mixed radiation fields, creating a new class of multimodal photon and neutron imagers.

Degtiarenko, Pavel V.↗
DOE OSTI2018

On the Early In Situ Formation of Pluto’s Small Satellites

The formation of Pluto’s small satellites—Styx, Nix, Keberos, and Hydra—remains a mystery. Their orbits are nearly circular and are near mean-motion resonances and nearly coplanar with Charon’s orbit. One scenario suggests that they all formed close to their current locations from a disk of debris that was ejected from the Charon-forming impact before the tidal evolution of Charon. The validity of this scenario is tested by performing N-body simulations with the small satellites treated as test particles and Pluto–Charon evolving tidally from an initial orbit at a few Pluto radii with initial eccentricity e {sub C} = 0 or 0.2. After tidal evolution, the free eccentricities e {sub free} of the test particles are extracted by applying fast Fourier transformation to the distance between the test particles and the center of mass of the system and compared with the current eccentricities of the four small satellites. The only surviving test particles with e {sub free} matching the eccentricities of the current satellites are those not affected by mean-motion resonances during the tidal evolution in a model with Pluto’s effective tidal dissipation function Q = 100 and an initial e {sub C} = 0.2 that is damped down rapidly. However, these test particles do not have any preference to be in or near 4:1, 5:1, and 6:1 resonances with Charon. An alternative scenario may be needed to explain the formation of Pluto’s small satellites.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Ruprecht 147: A Paradigm of Dissolving Star Cluster

We employed recent Gaia/DR2 data to investigate the dynamical status of the nearby (300 pc), old (2.5 Gyr) open cluster Ruprecht 147. We found prominent leading and trailing tails of stars along the cluster orbit, which demonstrates that Ruprecht 147 is losing stars at fast pace. Star counts indicate the cluster has a core radius of 33.3 arcmin and a tidal radius of 137.5 arcmin. The cluster also possesses an extended corona, which cannot be reproduced by a simple King model. We computed the present-day cluster mass using its luminosity and mass function, and derived an estimate of 234 ± 52 M {sub ⊙}. We also estimated the cluster original mass using available recipes extracted from N-body simulations obtaining a mass at birth of 50,000 ± 6500 M {sub ⊙}. Therefore dynamical mass loss, mostly caused by tidal interaction with the Milky Way, reduced the cluster mass by about 99%. We then conclude that Ruprecht 147 is rapidly dissolving into the general Galactic disk.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Greenhouse Gas Concentration Data Recovery Algorithm for a Low Cost, Laser Heterodyne Radiometer

The goal of a coordinated effort between groups at GWU and NASA GSFC is the development of a low-cost, global, surface instrument network that continuously monitors three key carbon cycle gases in the atmospheric column: carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), as well as oxygen (O2) for atmospheric pressure profiles. The network will implement a low-cost, miniaturized, laser heterodyne radiometer (mini-LHR) that has recently been developed at NASA Goddard Space Flight Center. This mini-LHR is designed to operate in tandem with the passive aerosol sensor currently used in AERONET (a well established network of more than 450 ground aerosol monitoring instruments worldwide), and could be rapidly deployed into this established global network. Laser heterodyne radiometry is a well-established technique for detecting weak signals that was adapted from radio receiver technology. Here, a weak light signal, that has undergone absorption by atmospheric components, is mixed with light from a distributed feedback (DFB) telecommunications laser on a single-mode optical fiber. The RF component of the signal is detected on a fast photoreceiver. Scanning the laser through an absorption feature in the infrared, results in a scanned heterodyne signal io the RF. Deconvolution of this signal through the retrieval algorithm allows for the extraction of altitude contributions to the column signal. The retrieval algorithm is based on a spectral simulation program, SpecSyn, developed at GWU for high-resolution infrared spectroscopies. Variations io pressure, temperature, composition, and refractive index through the atmosphere; that are all functions of latitude, longitude, time of day, altitude, etc.; are modeled using algorithms developed in the MODTRAN program developed in part by the US Air Force Research Laboratory. In these calculations the atmosphere is modeled as a series of spherically symmetric shells with boundaries specified at defined altitudes. Temperature, pressure, and species mixing ratios are defined at these boundaries. Between the boundaries, temperature is assumed to vary linearly with altitude while pressure (and thus gas density) vary exponentially. The observed spectrum at the LHR instrument will be the integration of the contributions along this light path. For any absorption measurement the signal at a particular spectral frequency is a linear combination of spectral line contributions from several species. For each species that might absorb in a spectral region, we have pre-calculated its contribution as a function of temperature and pressure. The integrated path absorption spectrum can then by calculated using the initial sun angle (from location, date, and time) and assumptions about pressure and temperature profiles from an atmospheric model. The modeled spectrum is iterated to match the experimental observation using standard multilinear regression techniques. In addition to the layer concentrations, the numerical technique also provides uncertainty estimates for these quantities as well as dependencies on assumptions inherent in the atmospheric models.

Miller, J. Houston↗
DOE OSTI2019

Genomic Signatures for Sedimentary Microbial Utilization of Phytoplankton Detritus in a Fast-Flowing Estuary

In fast-flowing, river-dominated estuaries, “hotspots” of microbial biogeochemical cycling can be found within areas of extended water retention. Lateral bays located off of the North and South channels of the Columbia River estuary are proposed to be such hotspots. Previous metagenomic studies on water samples indicated that these regions function both as sources and sinks of biogenic particles, with potential to impact organic matter fluxes in the estuary. To extend this work, we analyzed 11 sediment metagenomes from three disparate bays: the freshwater Cathlamet Bay, and the brackish Youngs Bay and more saline Baker Bay located nearer the mouth to the south and north of the main channel, respectively. Samples were collected from upper layers of sediments in August of 2011 and 2013 for DNA extraction and metagenome sequencing. All metagenomes were dominated by bacterial sequences, although diatom sequences as high as 26% of the total annotated sequences were observed in the higher salinity samples. Unsupervised 2D hierarchical clustering analysis resulted in the eleven metagenome samples clustered into four groups by microbial taxonomic composition, with Bacteroides, diatom, and phage levels driving most of the grouping. Results of functional gene clustering further indicated that diatom bloom degradation stage (early vs. late) was an important factor. While the Flavobacteriia and Cytophagia classes were well represented in metagenomes containing abundant diatoms, taxa from the Bacteroidia class, along with certain members of the Sphingobacteriia class, were particularly abundant in metagenomes representing later stages of diatom decomposition. In contrast, the sediment metagenomes with low relative abundance of diatom and Bacteroidetes sequences appeared to have a metabolic potential biased toward microbial growth under nutrient limitation. While differences in water salinity clearly also influenced the microbial community composition and metabolic potential, our results highlight a central role for allochthonous labile organic matter (i.e., diatom detritus), in shaping bacterial taxonomic and functional properties in the Columbia River estuary lateral bay sediments. These results suggest that in fast-flowing, river-dominated estuaries, sediment microbial communities in areas of extended water retention, such as the lateral bays, may contribute disproportionately to estuarine organic matter degradation and recycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Quantum chemistry benchmarking of binding and selectivity for lanthanide extractants

Computer-aided screening methods facilitate the discovery of new extractants for heavy and rare-earth metal separations. In this work, we have benchmarked the accuracy of different quantum chemistry methods for calculating extractant binding energies and selectivities. Specifically, we compare calculated data from different exchange correlation functionals (B3LYP-D3, ωB97X-D3, and M06-L) and different basis sets (including large-core effective core potentials and all-electron basis sets). We report aqueous-phase binding energy and selectivity trends for 1:1 and 3:1 extractant/lanthanide models for the complexes. We find that binding selectivities are not particularly sensitive to model chemistry, but binding energies are sensitive. Furthermore, calculated trends in selectivity using 3:1 extractant/lanthanide models are in better agreement with available experimental trends than trends using 1:1 extractant/lanthanide models. Lastly, we find that the B3LYP-D3/6-31 + G* model chemistry with the Stuttgart large-core relativistic effective core potentials on the lanthanide sufficiently reproduces results from larger basis set calculations and is confirmed as suitable for relatively fast and efficient screening of lanthanide binding energies and selectivities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling the co-assembly of binary nanoparticles

Abstract In this work, we present a binary assembly model that can predict the co-assembly structure and spatial frequency spectra of monodispersed nanoparticles with two different particle sizes. The approach relies on an iterative algorithm based on geometric constraints, which can simulate the assembly patterns of particles with two distinct diameters, size distributions, and at various mixture ratios on a planar surface. The two-dimensional spatial-frequency spectra of the modeled assembles can be analyzed using fast Fourier transform analysis to examine their frequency content. The simulated co-assembly structures and spectra are compared with assembled nanoparticles fabricated using transfer coating method are in qualitative agreement with the experimental results. The co-assembly model can also be used to predict the peak spatial frequency and the full-width at half-maximum bandwidth, which can lead to the design of the structure spectra by selection of different monodispersed particles. This work can find applications in fabrication of non-periodic nanostructures for functional surfaces, light extraction structures, and broadband nanophotonics.

Mohanty, Saurav (ORCID:0009000302592855)↗
DOE OSTI2022

Ionic Control over Ferroelectricity in 2D Layered van der Waals Capacitors

The van der Waals layered material CuInP 2 S 6 features interesting functional behavior, including the existence of four uniaxial polarization states, polarization reversal against the electric field through Cu ion migration, a negative-capacitance regime, and reversible extraction of Cu ions. At the heart of these characteristics lies the high mobility of Cu ions, which also determines the spontaneous polarization. Therefore, Cu migration across the lattice results in unusual ferroelectric behavior. Here, we demonstrate how the interplay of polar and ionic properties provides a path to ionically controlled ferroelectric behavior, achieved by applying selected DC voltage pulses and subsequently probing ferroelectric switching during fast triangular voltage sweeps. Using current measurements and theoretical calculations, we observe that increasing DC pulse duration results in higher ionic currents, the buildup of an internal electric field that shifts polarization loops, and an increase in total switchable polarization by ~50% due to the existence of a high polarization phase which is stabilized by the internal electric field. Apart from tuning ferroelectric behavior by selected square pulses, hysteretic polarization switching can even be entirely deactivated and reactivated, resulting in three-state systems where polarization switching is either inhibited or can be performed in two different directions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Chen, Jie↗