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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Extended molecular eigenmodes treatment of dipole–dipole NMR relaxation in real fluids

Traditional models of NMR relaxation fail to account for the complex, multi-exponential behavior of the autocorrelation function in realistic systems characterized by soft-interactions and molecules that are chemically and physically complex. Here, in this study, we describe the relative diffusion of the spin dipoles by means of a Fokker–Planck equation that includes an interaction potential of mean force to account for the response of the physical/chemical environment around the dipoles. By numerically solving the Fokker–Planck equation for the diffusion propagator, we estimate dipole–dipole NMR relaxation for like- and unlike-spin systems via its eigenmode solution. We test the model against molecular simulations of diffusing dipoles with harmonic potentials and also validate using experimental longitudinal relaxation data from real systems, including Gd(III)–aqua and Gd(III)–DO3A–butrol complexes, the latter being an important MRI contrast agent. Using this novel approach, we predict both the inner- and outer-shell contributions to the relaxivity rates with excellent accuracy at frequencies relevant to MRI. We also show that, under the appropriate assumptions, our framework naturally recovers the Bloembergen–Purcell–Pound, the Solomon–Bloembergen–Morgan, and the Hwang–Freed models. Our implementation is general and publicly available for application to a broad range of systems.

Pinheiro dos Santos, Thiago J. [Rice Univ., Housto↗
DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗
DOE OSTI2024

A High-Order Eulerian–Lagrangian Runge–Kutta Finite Volume (EL–RK–FV) Method for Scalar Nonlinear Conservation Laws

Abstract We present a class of high-order Eulerian–Lagrangian Runge–Kutta finite volume methods that can numerically solve Burgers’ equation with shock formations, which could be extended to general scalar conservation laws. Eulerian–Lagrangian (EL) and semi-Lagrangian (SL) methods have recently seen increased development and have become a staple for allowing large time-stepping sizes. Yet, maintaining relatively large time-stepping sizes post shock formation remains quite challenging. Our proposed scheme integrates the partial differential equation on a space-time region partitioned by linear approximations to the characteristics determined by the Rankine–Hugoniot jump condition. We trace the characteristics forward in time and present a merging procedure for the mesh cells to handle intersecting characteristics due to shocks. Following this partitioning, we write the equation in a time-differential form and evolve with Runge–Kutta methods in a method-of-lines fashion. High-resolution methods such as ENO and WENO-AO schemes are used for spatial reconstruction. Extension to higher dimensions is done via dimensional splitting. Numerical experiments demonstrate our scheme’s high-order accuracy and ability to sharply capture post-shock solutions with large time-stepping sizes.

Chen, Jiajie↗
DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Three-dimensional modelling of a self-sustained atmospheric pressure glow discharge

The atmospheric pressure glow discharge (APGD) is a relatively simple and versatile plasma source used in diverse applications. Stable APGD operation at high currents, generally a challenge due to instabilities leading to glow-to-arc transition, has been demonstrated using actively-controlled cathodic cooling. This article presents the computational modelling and simulation of a self-sustained direct-current APGD in helium within a 10 mm pin-to-plate inter-electrode gap for currents ranging from 4 to 40 mA. The APGD model is comprised of the conservation equations for total mass, chemical species, momentum, thermal energy of heavy-species and of free electrons, and electric charge. The model equations are discretized using a nonlinear variational multi-scale finite element method that has demonstrated superior accuracy in other plasma flow problems, on a temporal and three-dimensional computational domain suitable to unveil the potential occurrence of instabilities. Modelling results show good agreement with experimental measurements of voltage drop and the same trend but higher values of temperature. The higher temperatures obtained by the simulations appear to be due to the absence of a near-cathode heat dissipation model. Here, the results also reveal that the distribution of electron density and of the ratio of atomic helium ions to total ions transitions from monotonically increasing away from the cathode to presenting a minimum near the centre of the gap with increasing current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Subsampling of Parametric Models with Bifidelity Boosting

Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are generated using an experimental design process (e.g., a quadrature grid) involving computationally expensive models, or when the data size is large, sketching techniques have shown promise at reducing the cost of the construction of the regression model while ensuring accuracy comparable to that of the full data. However, random sketching strategies, such as those based on leverage scores, lead to regression errors that are random and may exhibit large variability. To mitigate this issue, we present a novel boosting approach that leverages cheaper, lower-fidelity data of the problem at hand to identify the best sketch among a set of candidate sketches. This in turn specifies the sketch of the intended high-fidelity model and the associated data. We provide theoretical analyses of this bifidelity boosting (BFB) approach and discuss the conditions the low- and high-fidelity data must satisfy for a successful boosting. In doing so, we derive a bound on the residual norm of the BFB sketched solution relating it to its ideal, but computationally expensive, high-fidelity boosted counterpart. Finally, empirical results on both manufactured and PDE data corroborate the theoretical analyses and illustrate the efficacy of the BFB solution in reducing the regression error, as compared to the nonboosted solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Water isotope data for the TEMPEST study site, 2023-2026

This dataset contains water isotope (deuterium (dD) and oxygen (d18O)) data from porewater, experimental source water, and aquifer sources from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental field site in Edgewater, MD. Samples were taken from 2023-2026 and included in file "TEMPEST_Water_Isotope_LANL_2023-2026.csv". Porewater samples were taken from 15cm depth at multiple locations in each experimental plot (i.e., control, freshwater, and saltwater). Water samples were stored in 2 ml amber glass vials with septum caps under refrigeration until analysis. A L2140-i Picarro cavity ringdown spectrometer connected to an A0211 Picarro vaporization module was used to run water samples. Standards (United States Geological Survey (USGS) 47 and 48) were used to check instrument accuracy during each run of samples. Post-processing codes were run to calculate isotopic values from raw data. Reported isotope data is post-processed. Raw data CSV files from the Picarro were processed using a Python script and output as excel files containing data calibrated to Vienna Standard Mean Ocean Water (VSMOW). Standardization is based on USGS47 and USGS48 standards that are measured before and after every 5-10 unknowns. Every measurement is comprised of 10 injections. Processing involved discarding the first 4 injections due to a known memory effect. Raw means of the last 6 injections are taken for each measurement and then corrected from the linear calibration relationship of the USGS standards expected vs measured values to obtain corrected compositions relative to VSMOW. Reported isotope data are corrected mean results.

Aquifer↗
DOE OSTI2026

Validation of an Erythema-Weighted UV Model Using Broadband Solar Irradiance Measurements From Eleven U.S. Sites: Preprint

Erythema-weighted UV solar irradiance (UV-E) has a potential impact on human health if the recommended maximum exposure times are exceeded. In spite of this, it is not measured at most sites that measure Global Horizontal Irradiance (GHI). However, since UV-E is highly correlated with GHI and total ozone content it can be estimated from this information with sufficient accuracy to assisst in public health recommendations. The Power Model (PM) provides a simple method to estimate the erythema-weighted UV irradiance (UV-E) from measured GHI, total ozone column and air mass. In this work, the performance of the PM method is assessed using high-quality data from 11 sites in the continental U.S. (part of SURFRAD and SOLRAD networks) and total ozone estimates publicly available from the MERRA-2 re-analysis database. A three year period (2021- 2023) at 1-minute frequency is considered. The results (for time aggregations of 5 and 60 minutes) show a high Pearson's correlations (> 0.99), consistently positive mean bias deviations (below 13%) and dispersions in the 8-19% range at all sites. Relative values are expressed in terms of the corresponding measurement mean. The performance indicators remain consistent across time resolutions (5 or 60 minutes), suggesting that the model's performance is robust and not significantly affected by short-term variability (which is captured by GHI). Spatial patterns reveal higher biases and RMSD in northern and eastern locations. These values represent a significant improvement over widely used satellite-based global UV-E estimates and open the possibility of using the PM with satellite-based GHI estimates for operational UV-E mapping over the contiguous U.S. territory.

14 SOLAR ENERGY↗
DOE OSTI2022

Accurate relativistic observables from postprocessing light cone catalogs

We introduce and study a new scheme to construct relativistic observables from postprocessing light cone data. This construction is based on a novel approach, LC-metric, which takes general light cone or snapshot output generated by arbitrary N-body simulations or emulations and solves the linearized Einstein equations to determine the spacetime metric on the light cone. We find that this scheme is able to determine the metric to high precision, and subsequently generate accurate mock cosmological observations sensitive to effects such as post-Born lensing and nonlinear integrated Sachs-Wolfe contributions. By comparing to conventional methods in quantifying those general relativistic effects, we show that this scheme is able to accurately construct the lensing convergence signal. We also find that the accuracy of this method in quantifying the integrated Sachs-Wolfe effects in the highly nonlinear regime outperforms conventional methods by an order of magnitude. Furthermore, this scheme opens a new path for exploring and modeling higher-order and nonlinear general relativistic contributions to cosmological observables, including mock observations of gravitational lensing and the moving lens and Rees-Sciama effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens

Abstract High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeast Yarrowia lipolytica and acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Permeability Prediction Using Vision Transformers

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows.

58 GEOSCIENCES↗
DOE OSTI2024

High Magnetic Anisotropy and Magnetocaloric Effects in Single-Crystal Cr 2 Te 3

Here, we report a systematic investigation of anisotropic magnetocaloric effects in single-crystal Cr 2 Te 3 . Single-crystal samples are synthesized by chemical vapor transport and characterized by X-ray and Laue diffraction methods. The maximum magnetic entropy change –ΔS M max is 4.50 J kg –1 K –1 for the easy c-axis (3.36 J kg –1 K –1 for the hard axis along ab-plane), and the relative cooling power (RCP) is 296.7 J kg –1 for the easy c-axis (183.84 J kg –1 for the hard axis along ab-plane) for a magnetic field change of 9 T near the Curie temperature. The magneto-crystalline anisotropy constant K u is estimated to be 580.12 kJ m –3 at 140 K, decreasing to 148.60 kJ m –3 at 168 K. Meanwhile, the maximum of the rotational magnetic entropy change –ΔS M R (T, H) between the c-axis and the ab-plane is about 1.14 J kg –1 K –1 for magnetic-field change of 9 T. The critical exponents are estimated by analyzing magnetocaloric effects, which indicate a 2D-Ising type magnetic system. The accuracy of estimated critical exponents is verified by scaling analysis. The maximum magnetic entropy change –ΔS M max ≈ 5.25 J kg –1 K –1 (along the c-axis) and the corresponding adiabatic temperature change ΔT ad ≈ 3.31 K (along the c-axis) are estimated by analyzing heat capacity measurements with a magnetic field up to 9 T.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High temperature thick film sensor development based on doped lanthanum chromites refractory semiconductors materials

High temperature advanced sensing materials have generated high demand due the high accuracy temperature measurements requirements for process optimization, controlling and sensing. Some technological applications of harsh conditions sensing include monitoring tiles of space shuttles, rotating bearings in aircraft engines, turbines, jet engines dynamics and chemical reactors. High temperature conditions limit the sensing strategies, where typically traditional metal thermocouples are unstable, and the sensing options are limited to optical spectroscopy methods. Recently, refractory semiconductors thick- and thin-film thermocouples have been developed and, in many cases, preferred over conventional metallic thermocouples due their spatial resolution, and capability of direct deposition on any surface. Rare earth chromites ceramics materials, exhibit some properties of interest for high temperature sensing technologies development, such as: high microstructure and sintering stability, excellent conductive behavior at high temperatures, and matching thermal expansion coefficients relative to other conductors and refractory ceramics. In this work, high performance ultra-high temperature thermocouples using p-type and n-type doped lanthanum chromites materials were fabricated and tested at temperatures up to 1500 o C. Thermoelectric voltage, Seebeck Coefficients were established for all devices, evidencing high stability and performance in prolongated operational time and harsh conditions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

OSIRIS-REx constraints on local dark matter and cosmic neutrino profiles

We derive purely gravitational constraints on dark matter and cosmic neutrino profiles in the solar system using asteroid (101955) Bennu. We focus on Bennu because of its extensive tracking data and high-fidelity trajectory modeling resulting from the OSIRIS-REx mission. We find that the local density of dark matter is bound by $ρ_{DM}$ ≲ 3.3 × 10 -15 kg/m 3 ≃ 6 × 10 6 $\bar{ρ_{DM}}$, in the vicinity of ~ 1.1 au (where $\bar{ρ_{DM}}$ ≃ 0.3 GeV/cm 3 ). We show that high-precision tracking data of solar system objects can constrain cosmic neutrino overdensities relative to the Standard Model prediction n̅ν, at the level of η ≡ nν/n̅ν ≲ 1.7 × 10 11 (0.1 eV/m ν ) (Saturn), comparable to the existing bounds from KATRIN and other previous laboratory experiments (with m ν the neutrino mass). These local bounds have interesting implications for existing and future direct-detection experiments. Our constraints apply to all dark matter candidates but are particularly meaningful for scenarios including solar halos, stellar basins, and axion miniclusters, which predict overdensities in the solar system. Furthermore, introducing a DM-SM long-range fifth force with a strength tilde alphaDtimes stronger than gravity, Bennu can set a constraint on ρDM ≲ $\bar{ρ_{DM}}$(6 × 10 6 /$_{\tilde{αD}}$). These constraints can be improved in the future as the accuracy of tracking data improves, observational arcs increase, and more missions visit asteroids.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2020

High‐order symmetric cubature rules for tetrahedra and pyramids

Abstract In this article, we present an algorithm to construct high‐order fully symmetric cubature rules for tetrahedral and pyramidal elements, with positive weights and integration points that are in the interior of the domain. Cubature rules are fully symmetric if they are invariant to affine transformations of the domain. We divide the integration points into symmetry orbits where each orbit contains all the points generated by the permutation stars. These relations are represented by equality constraints. The construction of symmetric cubature rules require the solution of nonlinear polynomial equations with both inequality and equality constraints. For higher orders, we use an algorithm that consists of five sequential phases to produce the cubature rules. In the literature, symmetric numerical integration rules are available for the tetrahedron for orders p = 1 – 10, 14 , and for the pyramid up to p = 10 . We have obtained fully symmetric cubature rules for both of these elements up to order p = 20 . Numerical tests are presented that verify the polynomial‐precision of the cubature rules. Convergence studies are performed for the integration of exponential, weakly singular, and trigonometric test functions over both elements with flat and curved faces. With increase in p , improvements in accuracy is realized, though nonmonotonic convergence is observed.

Jaśkowiec, Jan↗