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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Numerical Method for Determining the Elements of Circumbinary Orbits and Its Application to Circumbinary Planets and the Satellites of Pluto-Charon

Planets and satellites orbiting a binary system exist in the solar system and extrasolar planetary systems. Their orbits can be significantly different from Keplerian orbits, if they are close to the binary and the secondary-to-primary mass ratio is high. A proper description of a circumbinary orbit is in terms of the free eccentricity e {sub free} at the epicyclic frequency κ {sub 0}, forced eccentricity e {sub forced} at the mean motion n {sub 0}, and oscillations at higher frequencies forced by the non-axisymmetric components of the binary’s potential. We show that accurate numerical values for the amplitudes and frequencies of these terms can be extracted from numerical orbit integrations by applying fast Fourier transformation (FFT) to the cylindrical distance between the circumbinary object and the center of mass of the binary as a function of time. We apply this method to three Kepler circumbinary planets and the satellites of Pluto-Charon. For the satellite Styx of Pluto-Charon, the FFT results for κ {sub 0} and e {sub free} differ significantly from the first-order analytic value and the value reported by Showalter & Hamilton, respectively. We show that the deviation in κ {sub 0} is likely due to the effect of the 3:1 mean-motion resonance and discuss the implications of the lower value for e {sub free}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Understanding the Chemical Complexity of Multicomponent Systems: Uranium Polyoxometalates as Nanosorbents

The overarching objective of this project was to develop a molecular-scale understanding of chemical interactions of the transuranic (TRU) elements with uranyl-based polyoxometalate nanoclusters (UPOMs). The specific goals that supported this overarching objective were to: (i) quantify TRU interactions with UPOMs as a function of TRU, carbonate, and UPOM concentration, pH, and temperature; (ii) examine the molecular-level bonding environment of TRU-UPOM complexes; and (iii) develop a rate expression capable of describing the sorption and reduction of TRU by UPOMs. The published literature suggests several potential interactions can occur in TRU-UPOM systems: (i) encapsulation of TRU and coordination with the internal ‘-yl’ oxygens of the UPOM, (ii) coordination of TRU with the external ‘-yl’ oxygens of the UPOM, and (iii) coordination of TRU with the bridging ligands (e.g., peroxo, hydroxo, oxalate) of the UPOM. The latter two may induce aggregation of the UPOMs. The concept of encapsulation is demonstrated by the X-ray diffraction structure of crystallized U 20 , which reveals a Na + cation under each pentagonal face (Sigmon et al., 2009). Computational modeling shows that, while the negative charge of U 20 is spread among all the oxygen atoms, the internal ‘-yl’ oxygens host the most negative charge (Miro and Bo, 2012). This negative charge is reduced through the complexation of the templating sodium ions in the interior of the UPOM. Furthermore, Nyman et al. (2011) shows that cesium can exit through the pentagonal window of U 28 in the aqueous phase, suggesting that the TRU elements, which are smaller than cesium, may be capable of entering a UPOM through these windows in an exchange reaction. Under this project, we focused on hafnium, neptunium, and plutonium interactions with UPOMs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Vacuum-ultraviolet absorption cross-sections of functionalized cyclic hydrocarbons: Five-membered rings

Cross-sections were measured at 50 °C and 800 Torr using differential absorption spectroscopy in the vacuum-ultraviolet from 5.17 – 9.92 eV for a series of five-membered cyclic hydrocarbons: cyclopentane, cyclopentene, methylcyclopentane, tetrahydrofuran, furan, 2-methyltetrahydrofuran, 2,3-dihydro-5-methylfuran, tetrahydrofuran-3-one, 2-methyl-1,3-dioxolane, cyclopentanol, 2-hydroxytetrahydrofuran, cyclopentanone, 2-cyclopenten-1-one, 3-cyclopenten-1-one, 2-methylcyclopentan-1-one, 2-methylcyclopent-2-en-1-one, 1,2-epoxycyclopentane, 2,3-epoxycyclopentanone, and 3,4-epoxycyclopentanone. The majority of the species are functionalized and are directly related to chain-reactions relevant to combustion, such as substituted cyclic ethers and cycloalkenes. Uncertainty calculations were conducted as a function of photon energy to quantify errors associated with experimental repeatability, signal-to-noise ratio, and gas-phase concentration, yielding an upper limit of 5% in spectral regions where absorption is >2%. Here, the primary objective of the present work is to provide quantitative absorption cross-sections of species commonly produced in combustion reactions. Comparing qualitative differences among the five-membered cyclic species, in order to analyze the effect of functional groups on absorption, is a secondary objective. The majority of the spectra are reported for the first time and enable quantitative isomer-resolved speciation measurements in combustion chemistry.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Development and Optimization of a Multi-Functional SCR-DPF Aftertreatment System for Heavy-Duty NOX and Soot Emission Reduction

The objective of this project is to develop a strategy for the integration of catalytic NOX emission control and particulate matter emission control into a single device for the successful application to heavy-duty on-road diesel engine exhaust aftertreatment. The critical attributes of such a device, for successful application to PACCAR fleet, are as follows: 1. Must have sufficient filtration efficiency and successfully demonstrate soot management strategies (i.e., oxidation) to be economically attractive and regulatory compliant for particulate matter abatement. 2. In particular, the integrated device must retain sufficiently high soot oxidation capacity with NO2 (i.e., passive soot oxidation) across an applicable engine test cycle to be economically attractive. 3. Must exhibit sufficient catalytic NOX reduction activity in an economically-attractive fashion that is regulatory compliant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

Engineering↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles.

This paper develops a multi-objective eco-routing algorithm (combined eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce the BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,” “moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared to the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Calibration, modeling, parameterization, and verification of the instrument transfer function of an interferometric microscope

Interferometric microscopes are used to measure surface roughness, from which the computed power spectral density function can be used to extract bandwidth-limited values of the various surface properties, such as root-mean-square (rms) height and slope errors. Measurements with a microscope equipped with different objectives that have an overlapping spatial frequency range usually give different rms results over the common frequency bandwidth. This is a result of different instrument transfer functions (ITFs) that attenuate spatial frequencies by different amounts over the overlapping range. We report on the use of binary pseudo-random array (BPRA) standards to characterize the ITF of an interferometric microscope with the various objectives. We use a simple model of a 1D binary Results show that the spectrum for an undersampled array is a cosine function, rather than a straight line, constant white noise spectrum. We have an analytical model of the ITF that includes the effects of an obscured aperture and defocus, in addition to the usual parameters of numerical aperture, wavelength, and sampling period. In addition, the model includes the effect of aliasing of spatial frequency components beyond the Nyquist back into the sub-Nyquist region. We compare the model PSD predictions to the measurements performed with different objectives. Departures of the measured PSDs from the model predictions indicate that there are higher-order ITF corrections yet to be identified and included in the model.

Takacs, PZ↗
DOE OSTI2024

Axion stars: mass functions and constraints

The QCD axion and axion-like particles, as leading dark matter candidates, can also have interesting implications for dark matter substructures if the Peccei-Quinn symmetry is broken after inflation. In such a scenario, axion perturbations on small scales will lead to the formation of axion miniclusters at matter-radiation equality, and subsequently the formation of axion stars. Such compact objects open new windows for indirect searches for axions. We compute the axion star mass function based on recent axion minicluster studies and Bose star simulations. Applying this mass function, we find post-inflation axion-like particles with masses 1.8 × 10$^{-21}$ eV

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients: Impact of Particle Deposition in ICRP 66 Human Respiratory Tract Model

Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body’s metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. Finally, this study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.

International Commission on Radiological Protectio↗
DOE OSTI2021

Quantifying the Multi-Objective Cost of Uncertainty

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.

42 ENGINEERING↗
DOE OSTI2021

Students authoring molecular case studies as a partial course-based undergraduate research experience (CURE) for lab instruction

Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Predictions of the Nancy Grace Roman Space Telescope Galactic Exoplanet Survey. II. Free-floating Planet Detection Rates

The Nancy Grace Roman Space Telescope (Roman) will perform a Galactic Exoplanet Survey (RGES) to discover bound exoplanets with semimajor axes greater than 1 au using gravitational microlensing. Roman will even be sensitive to planetary-mass objects that are not gravitationally bound to any host star. Such free-floating planetary-mass objects (FFPs) will be detected as isolated microlensing events with timescales shorter than a few days. A measurement of the abundance and mass function of FFPs is a powerful diagnostic of the formation and evolution of planetary systems, as well as the physics of the formation of isolated objects via direct collapse. We show that Roman will be sensitive to FFP lenses that have masses from that of Mars (0.1 M {sub ⊕}) to gas giants (M ≳ 100 M {sub ⊕}) as isolated lensing events with timescales from a few hours to several tens of days, respectively. We investigate the impact of the detection criteria on the survey, especially in the presence of finite-source effects for low-mass lenses. The number of detections will depend on the abundance of such FFPs as a function of mass, which is at present poorly constrained. Assuming that FFPs follow the fiducial mass function of cold, bound planets adapted from Cassan et al., we estimate that Roman will detect ∼250 FFPs with masses down to that of Mars (including ∼60 with masses ≤ M {sub ⊕}). We also predict that Roman will improve the upper limits on FFP populations by at least an order of magnitude compared to currently existing constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The luminosity function of ringed galaxies

ABSTRACT We perform an analysis of the luminosity functions (LFs) of two types of ringed galaxies – polar-ring galaxies and collisional ring galaxies – using data from the Sloan Digital Sky Survey (SDSS). Both classes of galaxies were formed as a result of interaction with their environment and they are very rare objects. We constructed LFs of galaxies by different methods and found their approximations by the Schechter function. The luminosity functions of both types of galaxies show a systematic fall-off at low luminosities. The polar structures around bright (Mr ≤ −20m) and red (g − r > +0.8) galaxies are about twice as common as around blue ones. The LF of collisional rings is shifted towards brighter luminosities compared to polar-ring galaxies. We analysed the published data on the ringed galaxies in several deep fields and confirmed the increase in their volume density with redshift: up to z ∼ 1 their density grows as (1 + z)m, where m ≳ 5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Threshold resummation for computing large-x parton distribution through large-momentum effective theory

Parton distribution functions (PDFs) at large x are poorly constrained by high-energy experimental data, but extremely important for probing physics beyond standard model at colliders. We study the calculation of PDFs at large-x through large-momentum P z expansion of the lattice quasi PDFs. Similar to deep-inelastic scattering, there are two distinct perturbative scales in the threshold limit where the matching coefficient can be factorized into a space-like jet function at scale P z |1 – y| and a pair of heavy-light Sudakov form factors at scale P z . The matching formula allows us to derive a full renormalization group resummation of large threshold logarithms, and the result is consistent with the known calculation to the next-to-next to leading order (NNLO). This paves the way for direct large-x PDFs calculations in lattice QCD. As by-products, we find that the space-like jet function is related to a time-like version calculated previously through analytic continuation, and the heavy-light Sudakov form factor, calculated here to NNLO, is a universal object appearing as well in the large momentum expansion of quasi transverse-momentum-dependent PDFs and quasi wave-function amplitudes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗