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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Correlations between asymmetric compression, burn amplification, and hot-spot velocities in inertial confinement fusion implosions

Here, this manuscript examines the correlations between the hot-spot velocity (an observable signature of residual kinetic energy), low-mode implosion asymmetries, and burn amplification in inertial confinement fusion implosions on the National Ignition Facility (NIF). Using a combination of two-dimensional axis-symmetric and three-dimensional radiation-hydrodynamic simulations coupled to neutronics, we find that for typical NIF implosions, the stagnation asymmetry multiplies the observed hot-spot velocity anywhere from 80% to 120%, while burn amplification always increases it. Additionally, we find stagnation asymmetry typically deflects the observed hot-spot flow. The two mechanisms (low-mode implosion asymmetries and burn amplification) can be decoupled, and application of a simple model to a database of cryogenic implosions on the NIF infers the total hot-spot velocity amplification. This finding modifies the interpretation of data collected from inertial confinement fusion experiments and impacts the magnitude and origin of low-mode asymmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A fast, matrix-based method to perform omnidirectional pressure integration

Abstract Experimentally-measured pressure fields play an important role in understanding many fluid dynamics problems. Unfortunately, pressure fields are difficult to measure directly with non-invasive, spatially resolved diagnostics, and calculations of pressure from velocity have proven sensitive to error in the data. Omnidirectional line integration methods are usually more accurate and robust to these effects as compared to implicit Poisson equations, but have seen slower uptake due to the higher computational and memory costs, particularly in 3D domains. This paper demonstrates how omnidirectional line integration approaches can be converted to a matrix inversion problem. This novel formulation uses an iterative approach so that the boundary conditions are updated each step, preserving the convergence behavior of omnidirectional schemes while also keeping the computational efficiency of Poisson solvers. This method is implemented in Matlab and also as a GPU-accelerated code in CUDA-C++. The behavior of the new method is demonstrated on 2D and 3D synthetic and experimental data. Three-dimensional grid sizes of up to 125 million grid points are tractable with this method, opening exciting opportunities to perform volumetric pressure field estimation from 3D PIV measurements.

42 ENGINEERING↗
DOE OSTI2024

Topological Signature of Stratospheric Poincaré-Gravity Waves

The rotation of Earth breaks time-reversal and reflection symmetries in an opposite sense north and south of the equator, leading to a topological origin for certain atmospheric and oceanic equatorial waves. Away from the equator, the rotating shallow-water and stably stratified primitive equations exhibit Poincaré inertia–gravity waves that have nontrivial topology as evidenced by their strict superinertial time scale and a phase singularity in frequency–wavevector space. This nontrivial topology then predicts, via the principle of bulk-interface correspondence, the existence of two equatorial waves along the equatorial interface, the Kelvin and Yanai waves. To directly test the nontrivial topology of Poincaré-gravity waves in observations, we examine ERA5 data and study cross correlations between the wind velocity and geopotential height of the midlatitude stratosphere at the 50 hPa height. We find the predicted vortex and antivortex in the relative phase of the geopotential height and velocity at the high frequencies of the waves. By contrast, lower-frequency planetary waves are found to have trivial topology also as expected from theory. These results demonstrate a new way to understand stratospheric waves and provide a new qualitative tool to investigate waves in other components of the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational Fluid Dynamics (CFD) Simulations of Taylor Bubbles in Vertical and Inclined Pipes with Upward and Downward Liquid Flow

Summary Two-phase flow is a common occurrence in pipes of oil and gas developments. Current predictive tools are based on the mechanistic two-fluid model, which requires the use of closure relations to predict integral flow parameters such as liquid holdup (or void fraction) and pressure gradient. However, these closure relations carry the highest uncertainties in the model. In particular, significant discrepancies have been found between experimental data and closure relations for the Taylor bubble velocity in slug flow, which has been determined to strongly affect the mechanistic model predictions (Lizarraga-García 2016). In this work, we study the behavior of Taylor bubbles in vertical and inclined pipes with upward and downward flow using a validated 3D computational fluid dynamics (CFD) approach with level set method implemented in a commercial code. A total of 56 cases are simulated, covering a wide range of fluid properties, pipe diameters, and inclination angles: Eo ∈ [10, 700]; Mo ∈ [1×10–6, 5×103]; ReSL ∈ [–40, 10]; θ ∈ [5°, 90°]. For bubbles in vertical upward flows, the simulated distribution parameter, C0, is successfully compared with an existing model. However, the C0 values of downward and inclined slug flows where the bubble becomes asymmetric are shown to be significantly different from their respective vertical upward flow values, and no current model exists for the fluids simulated here. The main contributions of this work are (1) the relatively large 3D numerical database generated for this type of flow, (2) the study of the asymmetric nature of inclined and some vertical downward slug flows, and (3) the analysis of its impact on the distribution parameter, C0.

Engineering↗
DOE OSTI2022

K-shell Emission from O vi Near 19 Å

Abstract Laboratory measurements of O vi K-shell emission lines are presented that are situated near the O viii Ly α line at 19 Å. The data provide additional rest-frame references for velocity determinations based on absorption features in the spectra of warm absorbers in active galactic nuclei and other astrophysical objects. They also provide benchmarks for testing atomic structure calculations of energy levels with electrons in a high principal quantum number ( n = 3, 4). Excellent agreement is found with our calculations using the many-body perturbation theory method, and we provide a complete listing of the O vi energy levels calculated with this approach.

Beiersdorfer, Peter (ORCID:000000030127599X)↗
DOE OSTI2021

Integration of Dynamical System Scaling to RAVEN and Facility Application

As part of the design optimization and model validation effort within the hybrid energy systems program, the research to implement dynamical system scaling (DSS) code to RAVEN is funded under the Integrated Energy System program in collaboration with the Digital Reactor Technology & Development department. The DSS data processing algorithm has been coded within the RAVEN framework along with other metrics and postprocessing models. The implemented code was tested using a gravity-driven draining tank draining case modeled in RELAP5-3D and generated data was successfully postprocessed based on the scaling analyses defined by DSS. Postprocessed data concluded less agreement for the tank exit velocity and indicated that the modeled input deck for RELAP5-3D requires modification to enforce minimal pressure differential effects to the draining process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Stellar kinematics of dwarf galaxies from multi-epoch spectroscopy: application to Triangulum II

ABSTRACT We present new MMT/Hectochelle spectroscopic measurements for 257 stars observed along the line of sight to the ultrafaint dwarf galaxy Triangulum II (Tri II). Combining results from previous Keck/DEIMOS spectroscopy, we obtain a sample that includes 16 likely members of Tri II, with up to 10 independent redshift measurements per star. To this multi-epoch kinematic data set, we apply methodology that we develop in order to infer binary orbital parameters from sparsely sampled radial velocity curves with as few as two epochs. For a previously identified (spatially unresolved) binary system in Tri II, we infer an orbital solution with period $296.0_{-3.3}^{+3.8} \rm ~ d$, semimajor axis $1.12^{+0.41}_{-0.24}\rm ~au$, and systemic velocity $-380.0 \pm 1.7 \rm ~km ~s^{-1}$ that we then use in the analysis of Tri II’s internal kinematics. Despite this improvement in the modelling of binary star systems, the current data remain insufficient to resolve the velocity dispersion of Tri II. We instead find a 95 per cent confidence upper limit of $\sigma _{v} \lesssim 3.4 \rm ~km~s^{-1}$.

Buttry, Rachel (ORCID:0000000155017609)↗
DOE OSTI2021

Halo-independent analysis of direct dark matter detection through electron scattering

Sub-GeV mass dark matter particles whose collisions with nuclei would not deposit sufficient energy to be detected, could instead be revealed through their interaction with electrons. Analyses of data from direct detection experiments usually require assuming a local dark matter halo velocity distribution. In the halo-independent analysis method, properties of this distribution are instead inferred from direct dark matter detection data, which allows then to compare different data without making any assumption on the uncertain local dark halo characteristics. Furthermore, this method has so far been developed for and applied to dark matter scattering off nuclei. Here we demonstrate how this analysis can be applied to scattering off electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications

Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.

Artificial intelligence↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2026

Effect of macroscopic surface defects on dynamic damage: An experimental and numerical study

This study examines the impact of macroscopic surface defects on the dynamic ductile damage behavior of polycrystalline metals using plate-impact experiments. Defects of various shapes (flat, round, and point) were manufactured on the free or impact surfaces of annealed copper specimens. The experiments were diagnosed with photon Doppler velocimetry measurements and soft recovery techniques. The experimental results revealed that defect shape and location significantly affect velocity–time profiles and void distribution. In order to understand the dynamics of shock propagation and corresponding ductile damage evolution within the target specimens, the impact experiments were modeled with a calibrated Tepla model, a dynamic ductile damage model for polycrystalline metals [Nguyen et al., Int. J. Solids Struct. 329, 113833 (2026)]. Overall, our resulting simulated velocity showed good agreement with measured velocity, and our simulated porosity distributions qualitatively matched experimental data. Based on our simulation results, defects on the free surface were found to distort rarefaction waves and therefore the corresponding spall planes. On the other hand, defects on the impact surface generated a delayed shock when the flyer plate and the defected target area were in contact, leading to distortion of the spall plane. The distortion of the spall plane resulted in a non-uniform distribution of voids within the defected specimens. Lastly, we examined the role of local work hardening due to the defect manufacturing process on the velocity and porosity distribution, using Tepla simulations with a simplified representation of local hardening. This investigation highlights the importance of defect geometry, location, and local hardening associated with defect generation in dynamic ductile damage processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

ABSTRACT The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray-path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground-motion simulations. Toward this goal, we introduce the probability graphical model (PGM), combining velocity models with heterogeneous resolutions and nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM’s adaptive weight selection facilitates preserving and learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

Geochemistry & Geophysics↗
DOE OSTI2019

Adaptive harnessing damping in hydrokinetic energy conversion by two rough tandem-cylinders using flow-induced vibrations

Alternating lift technology (ALT) harnesses hydrokinetic energy from currents and tides and is different from conventional steady lift technologies (turbines). ALT is still under development with an important research issue being the mitigation of wake effect on downstream cylinders in Flow Induced Oscillations (FIO) of multi-cylinder Current Energy Converters (CEC). Rather than adjust the configuration/parameters of the converter, a more direct and effective way is to actively adjust the harnessed power. In this paper, a hydrokinetic energy converter using FIO of two cylinders in tandem in one-degree-of-freedom oscillators, with nonlinear adaptive damping and linear spring stiffness, is tested experimentally. FIO include Vortex Induced Vibrations (VIV), galloping, and their coexistence. Introducing adaptive damping into the tandem cylinders overcomes the shielding effect, which previous research on two tandem cylinders has shown to reduce power output by the downstream cylinder. Shielding along with fixed damping result either in ceasing motion due to excessive damping, or in low harnessed energy due to insufficient damping. In this experimental study, damping-to-velocity rate, linear spring-stiffness, cylinder spacing, and flow-velocity are the parameters with Reynolds number 30,000 ≤ Re ≤ 120,000. Comparison to linear-oscillators in FIO shows that this nonlinear converter, with velocity-proportional damping coefficient, is more effective over the entire FIO range but especially in galloping, where both flow and cylinder speeds are higher. Experimental results for energy harvesting, efficiency and instantaneous energy of the converter are presented and discussed supported by amplitude and frequency response data. Here the results show that the nonlinear, adaptive, velocity-proportional damping coefficient is an effective way to increase the overall harnessed power and the power of downstream cylinder. The harnessed power in the VIV to galloping transition increases by up to 94%. The most significant improvement is in the galloping region, where the increase in harnessed power and efficiency is around 33%.

13 HYDRO ENERGY↗
DOE OSTI2022

What drives galaxy quenching? A deep connection between galaxy kinematics and quenching in the local Universe

ABSTRACT We develop a 2D inclined rotating disc model, which we apply to the stellar velocity maps of 1862 galaxies taken from the MaNGA survey (SDSS public Data Release 15) . We use a random forest classifier to identify the kinematic parameters that are most connected to galaxy quenching. We find that kinematic parameters that relate predominantly to the disc (such as the mean rotational velocity) and parameters that characterize whether a galaxy is rotation- or dispersion-dominated (such as the ratio of rotational velocity to velocity dispersion) are not fundamentally linked to the quenching of star formation. Instead, we find overwhelmingly that it is the absolute level of velocity dispersion (a property that relates primarily to a galaxy’s bulge/spheroidal component) that is most important for separating star-forming and quenched galaxies. Furthermore, a partial correlation analysis shows that many commonly discussed correlations between galaxy properties and quenching are spurious, and that the fundamental correlation is between quenching and velocity dispersion. In particular, we find that at fixed velocity dispersion, there is only a very weak dependence of quenching on the disc properties, whereby more discy galaxies are slightly more likely to be forming stars. By invoking the tight relationship between black hole mass and velocity dispersion, and noting that black hole mass traces the total energy released by active galactic nuclei (AGNs), we argue that these data support a scenario in which quenching occurs by preventive feedback from AGNs. The kinematic measurements from this work are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Towards a quantitative understanding of bonding in supersonic single particle impacts: A three-dimensional FIB-SEM exploration

Particle bonding is crucial to coating quality in cold spray, but it has been a challenge to accurately quantify bonding even in single particle impacts. This paper uses FIB-SEM to explicitly map the particle-substrate interface for Cu-on-Cu single microparticle impacts in a full 3D rendering that spans a wide range of impact velocities. This approach permits a detailed quantification of the total bonding area and all of its associated components. In addition to revealing why prior 2D characterization efforts have missed important details about impact bonding, these data quantitatively reveal the evolution of bonding from its onset at the “critical velocity” V cr (where bonding is generally poor, ∼6 %) to its peak at around 1.3‧V cr (where almost 90 % of the particle bonds). Further increase in the velocity to 1.5‧V cr and beyond finds the onset of hydrodynamic penetration and a decrease in bonding. These data then support the development of a simple analytical model based on oxide rarefication and extrusion of bare metal through gaps in the oxide layer as driving the development of bonding. As a result, the model reproduces the experiments and provides guidance on optimization of bonding as a function of material and process parameters.

Analytical methods↗
DOE OSTI2024

Enhancing Regional Seismic Velocity Models With Higher‐Resolution Local Results Using Sparse Dictionary Learning

Abstract We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with data in the common region of both models, the method can enhance a regional lower‐resolution model to match the style and resolution of local higher‐resolution results while preserving its regional coverage. The method is demonstrated by applying it to two‐dimensional V S and three‐dimensional V P and V S regional and local velocity models in southern California. The enhanced reconstructed regional results exhibit clear visual improvements, especially in the reconstructed V P / V S ratios, and better correlations with geological features. Moreover, the reconstructed regional V P , V S models outperform the original ones in comparison of simulated earthquake waveforms to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications of data enhancement in earth sciences.

Geochemistry & Geophysics↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗