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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interpretable, Extensible Linear Regression Model for Electron Density Prediction

This code allows us to generate a model to predict ground state electron densities directly from atomic structure thereby bypassing the need to perform direct DFT calculations. It is based on many body correlation descriptors defined in https://arxiv.org/abs/2004.14442 (IM release numbers: LLNL-JRNL- 808791 and LLNL-JRNL-840698). The code reads in electron densities, in CHGCAR format used by the DFT code VASP, calculates many-body correlation descriptors based on atoms surrounding a grid point and then uses linear regression to obtain the fitting parameters.

Kumar, Shashikant↗
DOE OSTI2024

An Investigation of LES Wall Modeling for Rayleigh–Bénard Convection via Interpretable and Physics-Aware Feedforward Neural Networks with DNS

Abstract The traditional approach of using the Monin–Obukhov similarity theory (MOST) to model near-surface processes in large-eddy simulations (LESs) can lead to significant errors in natural convection. In this study, we propose an alternative approach based on feedforward neural networks (FNNs) trained on output from direct numerical simulation (DNS). To evaluate the performance, we conduct both a priori and a posteriori tests. In the a priori (offline) tests, we compare the statistics of the surface shear stress and heat flux, computed from filtered DNS input variables, to the stress and flux obtained from the filtered DNS. Additionally, we investigate the importance of various input features using the Shapley additive explanations value and the conditional average of the filter grid cells. In the a posteriori (online) tests, we implement the trained models in the System for Atmospheric Modeling (SAM) LES and compare the LES-generated surface shear stress and heat flux with those in the DNS. Our findings reveal that vertical velocity, a traditionally overlooked flow quantity, is one of the most important input features for determining the wall fluxes. Increasing the number of input features improves the a priori test results but does not always improve the model performance in the a posteriori tests because of the differences in input variables between the LES and DNS. Last, we show that physics-aware FNN models trained with logarithmic and scaled parameters can well extrapolate to more intense convection scenarios than in the training dataset, whereas those trained with primitive flow quantities cannot. Significance Statement The traditional near-surface turbulence model, based on a shear-dominated boundary layer flow, does not represent near-surface turbulence in natural convection. Using a feedforward neural network (FNN), we can construct a more accurate model that better represents the near-surface turbulence in various flows and reveals previously overlooked controlling factors and process interactions. Our study shows that the FNN-generated models outperform the traditional model and highlight the importance of the near-surface vertical velocity. Furthermore, the physics-aware FNN models exhibit the potential to extrapolate to convective flows of various intensities beyond the range of the training dataset, suggesting their broader applicability for more accurate modeling of near-surface turbulence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Need for Caution in Interpreting Extreme Weather Statistics

Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy. A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part II: Application to the Pahala, Hawaii, Earthquake Sequence

In the companion “Theory” article, we presented a new framing of the seismic location problem in terms of differential geometry (Harris et al., 2025). From that viewpoint, we developed a “project and correct” approach for estimating the relative locations of earthquakes. Here, in this study, we use project and correct to estimate high-precision relative locations of events from an earthquake sequence beneath the town of Pahala, Hawaii, using high-precision correlation-derived picks. The sequence was active from 2020 through 2022 and produced many highly correlated signals at Hawaii Volcano Observatory (HVO) stations on the island of Hawaii. The data we inverted consisted of 2882 events with observations at 5 HVO stations. For comparison with the travel-time image, we also produced conventional hypocenter solutions using both the Bayesloc program (Myers et al., 2007, 2009) and a purpose-built double-difference code. There were obvious structural elements in the resulting image, the resolution of which we used to test the performance of the project and the correct algorithm. For the projection step, we first produced a 3D local basis using an singular value decomposition (SVD) of the 2882 groups of times. Projection of the travel-time vectors into this basis resulted in an image with structures similar to those produced by our conventional locators, but with distortion as predicted by theory. Removing the distortion requires an inverse operator generated from the metric tensor at the geometric centroid of the events. We compared two approaches to obtaining such an inverse operator. The first uses an estimate of the geographic centroid of the event cloud from the centroid of the travel-time data. The second approach uses the centroid of the conventionally produced locations. The first approach produces a corrected image very similar to the conventional results, but with a rotation. The corrected image produced using the conventionally derived centroid is a near-exact match to the conventional locations.

Dodge, Douglas A. [Lawrence Livermore National Lab↗
DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗
DOE OSTI2020

Interpretation of Hydraulic Fractures Based on Microseismic Response in the Marcellus Shale, Monongalia County, West Virginia, USA: Implications for Shale Gas Production

Summary Hydraulic fracturing is critical for extracting shale gas in the subsurface. The treatment technique of multistage hydraulic fracturing is widely used to maximize production. In multistage hydraulic fracturing, not all pumping stages make the same contribution for production, although the designed stimulation process is almost same in every pumping stage. In this study, we characterize the microseismic responses of hydraulic fracturing. Significant variations of microseismic characteristics are observed among the different pumping stages. Pre-existing natural fractures are examined along the horizontal well in selected stages. Combined with the Mohr’s circle analysis, results of the fracture study show that induced hydraulic fractures can be captured by pre-existing natural fractures. Induced hydraulic fractures are simulated by the unconventional fracture model (UFM), and the result reveals that the stress-shadow effect diverts the direction of hydraulic fractures. The diverted hydraulic fracture affects the development of the hydraulic fracture network, which has influence on production. The treatment of two-step pumping is investigated by application of hydraulic diffusivity. Hydraulic fracturing performance and production could be optimized by the treatment of two-step pumping in a single stage. The second pumping creates new fractures and fills the fractures with additional proppants to maintain production for a long duration.

Energy & Fuels↗
DOE OSTI2022

Interpretation of HFTS 2 Microseismic Data Using Bedding-Plane-Slip Mechanism

Summary The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (low-frequency distributed acoustic sensing, or LF-DAS, strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data have three unique observations that have not been commonly observed in other datasets: (1) Due to the influence of offset pad depletion, microseismic data show that hydraulic fractures from the child well can propagate over 3,000 ft into the depleted low-stress zone. (2) By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that the microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages is much shorter than what is shown with LF-DAS strain data. (3) Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. This model successfully explains the above field observations from the HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

Engineering↗
DOE OSTI2012

Preliminary Interpretation of a Radionuclide and Colloid Tracer Test in a Granodiorite Shear Zone at the Grimsel Test Site, Switzerland

In February and March 2012, a tracer test involving the injection of a radionuclide-colloid cocktail was conducted in the MI shear zone at the Grimsel Test Site, Switzerland, as part of the Colloids Formation and Migration (CFM) project. The colloids were derived from FEBEX bentonite, which is mined in Spain and is being considered as a potential waste package backfill in a Spanish nuclear waste repository.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Interpretation of At-Line Spectra from AFS-2 Batch #3 Ferrous Sulfamate Treatment

Spectra from the “at-line” spectrometer were obtained during the ferrous sulfamate (FS) valence adjustment step of AFS-2 Batch #3 on 9/18/2013. These spectra were analyzed by mathematical principal component regression (PCR) techniques to evaluate the effectiveness of this treatment. Despite the complications from Pu(IV), we conclude that all Pu(VI) was consumed during the FS treatment, and that by the end of the treatment, about 85% was as Pu(IV) and about 15% was as Pu(III). Due to the concerns about the “odd” shape of the Pu(IV) peak and the possibility of this behavior being observed in the future, a follow-up sample was sent to SRNL to investigate this further. Analysis of this sample confirmed the previous results and concluded that it “odd” shape was due to an intermediate acid concentration. Since the spectral evidence shows complete reduction of Pu(VI) we conclude that it is appropriate to proceed with processing of this the batch of feed solution for HB-Line including the complexation of the fluoride with aluminum nitrate.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2014

Summary and Preliminary Interpretation of Tritium and Dissolved Noble Gas Data from Site 300

In October 2013, groundwater samples were collected from 10 wells from Site 300 and analyzed by the Environmental Radiochemistry Laboratory at Lawrence Livermore National Laboratory (LLNL). Groundwater samples were analyzed for groundwater age tracers: tritium, the helium isotope ratio of dissolved helium and the concentrations of dissolved noble gases (Helium, Neon, Argon, Krypton, and Xenon). A subset of the samples was also analyzed for excess nitrogen due to saturated zone denitrification. The age-dating data were used to evaluate the degree to which groundwater at a particular monitoring well was derived from pre-modern and/or modern sources. More specifically, the analyses can be used to determine whether the recharge age of the groundwater beneath the site pre-dates anthropogenic activities at the site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗