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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An expectation–maximization framework for comprehensive prediction of isoform-specific functions

Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. We present isoform interpretation, a method that uses expectation–maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Investigation of magnetic noise from conductive shields in the 10–300 kHz frequency range

Here we present experimental and theoretical investigations of magnetic noise originating from radio-frequency (RF) conductive shields of flat geometry in the inductance-dominated impedance regime below 300 kHz. The measurement is based on a Q-factor determination of a coil that provides a sufficient sensitivity, placed at the position where the shield magnetic noise is measured. The theory is based on calculations of magnetic field and inductance of one or a few flat rings that emulate a conductive shield. The theoretical model is found to be in close agreement with experimental data. It can be used to predict the magnetic noise of a conductive shield with different thicknesses, conductivities, and temperatures at different distances for a wide range of frequencies. Although the model can be generalized for a more arbitrary shield geometry, in its presented form, it can be applied to the magnetic noise predictions when the shield surface curvature is not large. One important conclusion is that the RF conductive shield can generate the magnetic noise much lower than femtotesla, and, thus, it can be used in many precision experiments targeting minute high-frequency magnetic signals, such as detection of magnetic resonance imaging and nuclear quadrupole resonance signals and search for axion dark matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Stability of expanding accretion shocks for an arbitrary equation of state

We present a theoretical stability analysis for an expanding accretion shock that does not involve a rarefaction wave behind it. The dispersion equation that determines the eigenvalues of the problem and the explicit formulae for the corresponding eigenfunction profiles are presented for an arbitrary equation of state and finite-strength shocks. For spherically and cylindrically expanding steady shock waves, we demonstrate the possibility of instability in a literal sense, a power-law growth of shock-front perturbations with time, in the range of $h_c< h<1+2 {\mathcal {M}}_2$ , where $h$ is the D'yakov-Kontorovich parameter, $h_c$ is its critical value corresponding to the onset of the instability and ${\mathcal {M}}_2$ is the downstream Mach number. Shock divergence is a stabilizing factor and, therefore, instability is found for high angular mode numbers. As the parameter $h$ increases from $h_c$ to $1+2 {\mathcal {M}}_2$ , the instability power index grows from zero to infinity. This result contrasts with the classic theory applicable to planar isolated shocks, which predicts spontaneous acoustic emission associated with constant-amplitude oscillations of the perturbed shock in the range $h_c< h<1+2 {\mathcal {M}}_2$ . Examples are given for three different equations of state: ideal gas, van der Waals gas and three-terms constitutive equation for simple metals.

Huete, César (ORCID:0000000232278520)↗
DOE OSTI2023

Measurements at peak operational beam current in the SNS beam test facility

Work at the SNS beam test facility has focused on high dimensional and high dynamic range measurements of the medium energy (2.5 Mev) beam distribution. This is motivated by the need to understand and predict beam losses down to one-part-per-million. The initial demonstration of full-and-direct 6D phase space measurement was done at a current of 40 mA transported through the RFQ. Since that demonstration, more detailed studies have been performed at lower transported currents (in the range 30 mA and below). This is due to a hardware change - recent runs utilize the original SNS RFQ, which after a decade of service in the SNS achieves transmission significantly below design (50-60%, vs >80%). A short run in 2023 with a newly-commissioned RFQ enables maximum transmission. Preliminary results from beam distribution measurements during this run are discussed.

Ruisard, Kiersten↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2024

A model for oblique impacts on material surfaces

Many practical situations of material damage, wear, and erosion involve collisions between small particles and surfaces at inclined angles. While there are many well-validated models of normal incidence impact situations, elastic-plastic models for oblique incidence impact events are lacking. Here the finite element method is used to predict the normal and tangential coefficient of restitution in oblique impacts for hard, elastic spheres impacting an elastic-perfectly plastic material surface. The proposed model covers various impact angles ranging from 0° to 45°, within a limiting impact velocity below which the effects of heating are negligible. The normal coefficient of restitution follows power-laws with respect to normalized values of the impact velocity. Interestingly, the tangential coefficient of restitution follows a linear relationship with impact velocity. Together, these results provide a semi-empirical set of equations predicting oblique impact rebounds (both velocity and trajectory) for a wide range of conditions and material properties, with which experimental results can be rapidly interpreted. As a result, Laser-Induced Particle Impact Test (LIPIT) data are also presented for aluminum particles impacting aluminum substrates, at impact angles of 25° and 40°; the results compare favorably with the model and validate the general use of such models for the analysis of experimental data.

Coefficient of restitution↗
DOE OSTI2022

Machine learning for postprocessing ensemble streamflow forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting thermophysical properties of molten salts in the MgCl 2 -NaCl-KCl-LiCl system with a shell-model potential

Ternary eutectic salts composed of MgCl 2 , NaCl, and KCl, referred to as MNK salts, have recently emerged as promising candidates as high-temperature heat transfer fluids and thermal energy storage media. Here we performed classical molecular dynamics (MD) simulations to predict the densities, specific heat capacities, viscosities, and ionic self-diffusivities for MNK salts over a wide temperature range. The impact of LiCl additive on their thermophysical properties was also investigated. To capture the electronic polarization of Cl anions by neighboring cations, we developed a novel shell-model potential using the force-matching method and a dataset of ab initio calculated interatomic forces. Our extensive MD simulations predict structure and properties for pure salts and binary/ternary salt mixtures in the MgCl 2 -NaCl-KCl-LiCl system in overall good agreement with available experimental and theoretical data, which corroborates the accuracy and reliability of our developed potential.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Resilience in soil bacterial communities of the boreal forest from one to five years after wildfire across a severity gradient

Wildfires can represent a major disturbance to ecosystems, including soil microbial communities belowground. Furthermore, fire regimes are changing in many parts of the world, altering and often increasing fire severity, frequency, and size. The boreal forest and taiga plains ecoregions of northern Canada are characterized by naturally-occurring stand-replacing wildfires on a 40–350 year basis. We previously studied the effects of wildfire on soil microbial communities one year post-fire across 40 sites, spanning a range of burn severity. Here, we return to the same sites five years post-fire to test a series of hypotheses about the effects of fire on bacterial community composition. We ask questions on two themes: which factors control bacterial community composition during post-fire recovery, and how does the importance of different fire-responsive traits change during post-fire recovery? We find the following: Five years post-fire, vegetation community, moisture regime, pH, total carbon, texture, and burned/unburned all remained significant predictors of bacterial community composition with similar predictive value (R 2 ). Bacterial communities became more similar to unburned sites five years post-fire, across the range of severity, suggesting resilience, while general structure of co-occurrence networks remained similar one and five years post-fire. Fast growth potential, as estimated using predicted 16S rRNA copy numbers, was no longer significantly correlated with burn severity five years post-fire, indicating the importance of this trait for structuring bacterial community composition may be limited to relatively short timescales. Many taxa that were enriched in burned sites one year post-fire remained enriched five years post-fire, although the degree to which they were enriched generally decreased. Specific taxa of interest from the genera Massilia, Blastococcus, and Arthrobacter all remained significantly enriched, suggesting that they may have traits that allow them to continue to flourish in the post-fire environment, such as tolerance to increased pH or ability to degrade pyrogenic organic matter. This hypothesis-based work expands our understanding of the post-fire recovery of soil bacterial communities and raises new hypotheses to test in future studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multiscale study of helium diffusion in Ni-Cr alloys: Short-range trapping versus long-range channeling

Ni-Cr alloys are widely employed as structural materials in fast nuclear reactors but are vulnerable to high-temperature helium (He) embrittlement (HTHE) under fast neutron irradiation. A comprehensive understanding of He diffusion in Ni-Cr alloys, which governs the kinetics of HTHE, is therefore essential for developing resilient materials and preventing failure. In this work, we reveal the underlying mechanisms of He diffusion in pure Ni and Ni-Cr alloys by integrating density functional theory (DFT) with atomic kinetic Monte Carlo (AKMC) simulations. Our findings uncover a non-monotonic dependence of He diffusivity on Cr concentration, contradicting the monotonic trends predicted by DFT-parameterized theories. At low Cr concentrations, He diffusion is dominated by short-range trapping, characterized by multiple trapping sites and a distinct mechanism within the first nearest neighbor of Cr, differing from that in pure Ni. At high Cr concentrations, these local traps become interconnected, forming long-range fast diffusion channels that enhance He mobility. The competition between localized trapping and extended channeling results in a diffusivity that first decreases, then increases with rising Cr content. These atomic-scale insights offer critical guidance for the design of radiation-tolerant Ni-based alloys. Moreover, the combined DFT-AKMC methodology and the concept of random walker diffusion through interconnected energy basins present a broadly applicable framework for studying transport phenomena in disordered systems.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Flexible Stellarator Physics Facility

We propose to build a Flexible Stellarator Physics Facility to explore promising regions of the vast parameter space of disruption-free stellarator solutions for Fusion Pilot Plants (FPPs). The FESAC Long Range Plan recognized the quasi-symmetric stellarator as “the leading US approach to developing disruption-free, low-recirculating-power fusion configurations”. To deliver the ambitious Decadal Vision for Commercial Fusion Energy, we must establish a persuasive stellarator program in parallel to the tokamak one: the stellarator will prove to be a better path to a reactor if theoretical predictions are confirmed and novel optimization techniques and strategies work as desired. Since the release of the Long Range Plan in 2020, stellarators have arguably made the most significant advances of all fusion concepts. Groundbreaking results from W7-X demonstrated low neoclassical transport and the successful operation of the island divertor. Advances in theory and modeling now allow us to minimize turbulent transport, to achieve equilibria with precise quasisymmetry, to reduce neoclassical transport and fast ion loss to levels far below what has been previously achieved, and to minimize the effect of coil manufacturing errors. If realized, these advances will lead to cost-effective stellarator designs with confinement comparable to tokamaks but without the fundamental challenges of disruptions and current drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Predicted Reaction Mechanisms, Product Speciation, Kinetics, and Detonation Properties of the Insensitive Explosive 2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105)

2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105) is a relatively new and promising insensitive high-explosive (IHE) material that remains only partially characterized. IHEs are of interest for a range of applications and from a fundamental science standpoint, as the root causes behind insensitivity are poorly understood. In this work, we adopt a multitheory approach based on reactive molecular dynamic simulations performed with density functional theory, density functional tight-binding, and reactive force fields to characterize the reaction pathways, product speciation, reaction kinetics, and detonation performance of LLM-105. We compare and contrast these predictions to 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a prototypical IHE, and 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX), a more sensitive and higher performance material. The combination of different predictive models allows access to processes operative on progressively longer timescales while providing benchmarks for assessing uncertainties in the predictions. We find that the early reaction pathways of LLM-105 decomposition are extremely similar to TATB; they involve intra- and intermolecular hydrogen transfer. Additionally, the detonation performance of LLM-105 falls between that of TATB and HMX. We find agreement between predictive models for first-step reaction pathways but significant differences in final product formations. Predictions of detonation performance result in a wide range of values, and one-step kinetic parameters show the similar reaction rates at high temperatures for three out of four models considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Verification of the AGREE and Serpent for the steady state HTR-10 benchmark problems

This paper provides results from steady state HTR-10 benchmark calculations performed using AGREE and Serpent that are compared to experimental results as well as calculations performed by INET. The purpose of completing this benchmark is to validate AGREE and Serpent for the prediction of HTGR operation so that they may ultimately be used to support licensing and deployment efforts of advanced reactors. The benchmark consists of several problems ranging from control rod worth calculations to k{sub eff} calculations at various temperatures. Overall, AGREE and Serpent show good agreement with the reference solutions and are effectively able to predict HTGR operation for a variety of steady state cases. The largest difference from the reference was for the initial core single rod worth, which is possibly due to the larger core helium cavity causing inaccuracies in the diffusion calculation whereas the control rod worth determined by Serpent and AGREE is much closer to the reference result for the full core loading. Moreover, in every benchmark problem, increasing the number of energy groups in the cross sections results in improved agreement of the AGREE result with the reference, although it also causes an increase in computation time. For problems with experimental data available, the accuracy of results generated by Serpent and AGREE is comparable to results obtained by other benchmark participants. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Homologs of Phycobilisome Abundance Regulator PsoR Are Widespread across Cyanobacteria

During chromatic acclimation (CA), cyanobacteria undergo shifts in their physiology and metabolism in response to changes in their light environment. Various forms of CA, which involves the tuning of light-harvesting accessory complexes known as phycobilisomes (PBS) in response to distinct wavelengths of light, have been recognized. Recently, a negative regulator of PBS abundance, PsoR, about which little was known, was identified. We used sequence analyses and bioinformatics to predict the role of PsoR in cyanobacteria and PBS regulation and to examine its presence in a diverse range of cyanobacteria. PsoR has sequence similarities to the β-CASP family of proteins involved in DNA and RNA processing. PsoR is a putative nuclease widespread across Cyanobacteria, of which over 700 homologs have been observed. Promoter analysis suggested that psoR is co-transcribed with upstream gene tcpA. Multiple transcription factors involved in global gene regulation and stress responses were predicted to bind to the psoR-tcpA promoter. The predicted protein–protein interactions with PsoR homologs included proteins involved in DNA and RNA metabolism, as well as a phycocyanin-associated protein predicted to interact with PsoR from Fremyella diplosiphon (FdPsoR). The widespread presence of PsoR homologs in Cyanobacteria and their ties to DNA- and RNA-metabolizing proteins indicated a potentially unique role for PsoR in CA and PBS abundance regulation.

Layer, Alicia↗
DOE OSTI2020

Reconstruction and identification of boosted di-τ systems in a search for Higgs boson pairs using 13 TeV proton-proton collision data in ATLAS

In this paper, a new technique for reconstructing and identifying hadronically decaying τ+τ- pairs with a large Lorentz boost, referred to as the di-τ tagger, is developed and used for the first time in the ATLAS experiment at the Large Hadron Collider. A benchmark di-τ tagging selection is employed in the search for resonant Higgs boson pair production, where one Higgs boson decays into a boosted $b\overline b$ pair and the other into a boosted τ+τ- pair, with two hadronically decaying τ-leptons in the final state. Using 139 fb -1 of proton-proton collision data recorded at a centre-of-mass energy of 13 TeV, the efficiency of the di-τ tagger is determined and the background with quark- or gluon-initiated jets misidentified as di-τ objects is estimated. The search for a heavy, narrow, scalar resonance produced via gluon-gluon fusion and decaying into two Higgs bosons is carried out in the mass range 1–3 TeV using the same dataset. No deviations from the Standard Model predictions are observed, and 95% confidence-level exclusion limits are set on this model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Total thermal neutron cross section measurements of hydrogen dense polymers from 0.0005–20 eV

Hydrogen dense polymers, specifically polyethylene, polystyrene, and Plexiglas, have served as neutron moderator and reflector materials in hundreds of separate critical benchmark experiments because of their low cost and abundance of hydrogen. In order to accurately model and simulate these critical benchmarks, the thermal scattering law (TSL) evaluation that governs how neutrons will thermalize must be well understood and rigorously validated. To support this validation, researchers at Rensselaer Polytechnic Institute performed total neutron cross section measurements for high-density polyethylene & polystyrene over the energy range 0.0005–20 eV and for Plexiglas G & Plexiglas G-UVT over the energy range 0.0005–3 eV. Comparisons were made between the measured cross section and that predicted by the ENDF/B-VIII.0 and Oak Ridge National Laboratory/European Spallation Source/Rensselaer Polytechnic Institute TSL evaluations for polyethylene, Plexiglas, and polystyrene, as available. These experiments represent the first total neutron cross section measurements for polystyrene.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗