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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Internship Final Report on the unsupervised learning sensor fusion (ULSF) approach

This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Final Scientific/Technical Report of Gas Hydrate Dynamics on the Alaskan Beaufort Continental Slope: Modeling and Field Characterization

Methane Hydrates, a solid form of methane and water, exist at high pressures and low temperatures, occurs on every continental margin on Earth, represents one of the largest reservoirs of carbon on the planet, and, if destabilized, may play an important role in both slope stability and climate change. For decades, researchers have studied methane hydrates with the hope of determining if methane hydrates are destabilizing, and if so, how this destabilization might impact slope stability and ocean/atmosphere carbon budgets. In the past ~5 years, it has become well established that the upper “feather-edge” of methane hydrate stability (intermediate water depths of ~200-500 meters below sea level) represents an important frontier for methane hydrates stability research, as this zone is most susceptible to destabilization due to minor fluctuations in ocean temperature in space and time. The Arctic Ocean—one of the fastest warming regions on Earth—is perhaps the best place to study possible changes to methane hydrate stability due to ocean warming. To address the stability of methane hydrates at intermediate ocean depths, Southern Methodist University in partnership with Oregon State University and The United State Geological Survey at Woods Hole began investigating methane hydrate stability in intermediate water depths below both the US Beaufort Sea and the Atlantic Margin, from 2012-2017. The work was funded by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). The key goal of the SMU component of this study was to collect the first ever heat flow data in the Beaufort Sea and compare measured shallow (probe-based1) heat flow values with deeper (BSR-derived2) heat flow values, and from this, determine whether hydrates were in thermal equilibrium. In September 2016, SMU/OSU collected the first ever heat flow measurements in the US Beaufort Sea. Despite poor weather and rough seas, the cruise was a success, with 116 heat flow measurements acquired across the margin, spanning 4 transects separated by more than 400 km. Useable heat flow data exists for 97% (113) of probe heat flow measurements, revealing a clear picture of regional heat flow across the basin. During the past 8 months since the cruise, SMU researchers have processed the heat flow and thermal conductivity measurements and compared results to deeper heat flow estimates obtained from seismic data. The analysis reveals clear, consistent trends: All probe heat flow measurements in depths greater than 800 mbsl are consistent with BSR-derived values; heat flow measurements obtained in water depths between ~250-750 mbsl are systematically lower than those estimated from BSRs; and heat flow estimates in water depths shallower than ~250 mbsl are systematically warmer than deeper estimates. The consistency between shallow (probe) and deep (BSR) heat flow measurements at depths greater than ~750 m where ocean temperature changes are minimal supports the premise that the hydrates consist primarily of methane and represent a valuable tool for estimating heat flow. The anomalous cooling trend observed in the upper 250 m is consistent with expected seasonal effects observed in shallow ocean buoy measurements in the arctic, when cold, less dense melting sea ice cools the upper 200 m of the ocean during the summer as ice melting occurs. The discrepancy in heat flow at intermediate water depths is best explained via recent intermediate ocean temperature warming, where long-term (annual or longer) warming intermediate ocean bottom waters result in an anomalously low heat flow in shallow heat flow measurements. Using the characteristic 1D time-length scale for diffusion, we estimate that ocean temperature warming began no later than ~1200 years ago but arguably much more recently as results are limited by seismic resolution. More importantly, our analysis indicates methane hydrate is destabilizing not only in the upper feather edge (200-500 mbsl) but at depths as great as 750 mbsl. The intermediate ocean warming rate supports previous studies suggesting geologically rapid warming (>0.1 deg C/decade) at intermediate ocean depths in the Beaufort Sea. Assuming no further changes or additional warming, our analysis indicates methane hydrates will destabilize at seafloor depths shallower than 750 mbsl in the Beaufort Sea within the next ~3000 years. 1 Probe outfitted with sensors inserted into the seafloor sediment 2 Bottom-simulating reflector (BSR) seismic data indicates presence of hydrate deposits

58 GEOSCIENCES↗
DOE OSTI2024

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies necessitates a comprehensive understanding of uncertainties in system code predictions. The forward uncertainty quantification (UQ) process involves the propagation of input uncertainties through the computational models to obtain uncertainties in the outputs. To this end, achieving an accurate estimation of input uncertainties is important, which is the focus of inverse UQ (IUQ). Traditionally, research in Bayesian IUQ within the nuclear engineering domain has largely relied on single-level Bayesian inference. While being effective for relatively small datasets, this approach encounters limitations for cases with large datasets. The use of a single-level model may prove inefficient, as the resultant posterior distributions can significantly differ when distinct subsets of data are employed. To address this issue, we employ an hierarchical Bayesian model for IUQ. Furthermore, this approach involves organizing observations into different groups based on the test conditions, thereby accommodating varying calibration parameters across these distinct groups. In this study, we developed and implemented a hierarchical Bayesian IUQ method to consider the grouping effect of critical flow measurement data from various geometries. Comparing the outcomes of IUQ under different selections of test data using hierarchical Bayesian IUQ against those obtained from single-level Bayesian IUQ, the forward propagation of hierarchical Bayesian IUQ results demonstrates a notably improved agreement with the experimental data.

42 ENGINEERING↗
DOE OSTI2025

Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400–1500 MeV/nucleon

The incompressibility K of symmetric nuclear matter (SNM) is determined through a Bayesian analysis of collective flow data from Au + Au collisions at beam energies $E = 400 -1500$ MeV/nucleon. This analysis utilizes a Gaussian process (GP) emulator applied to the isospin-dependent quantum molecular dynamics (IQMD) model for heavy-ion collisions, both with and without incorporating the momentum dependence of the single-nucleon potentials. Specifically, at the 68% confidence level, using rapidity and transverse velocity dependence of proton elliptic flow data with and without consideration of the momentum dependence, the inferred incompressibility values are $K=188.9^{+2.9}_{-4.5}$ MeV and $256.1^{+8.2}_{-8.7}$ MeV at $E = 400$ MeV/nucleon, respectively. When the transverse momentum dependence of proton-like directed flow data is included, the inferred incompressibility values become $K=222.3^{+9.0}_{-9.9}$ MeV and $K=285.5^{+6.7}_{-7.3}$ MeV, respectively. Furthermore, we found that the value of K derived from observables of proton elliptic flow increases with beam energy. Finally, this indicates that the equation of state (EoS) of nuclear matter hardens at higher densities and temperatures in reactions with higher beam energies.

Bayesian inference↗
DOE OSTI2013

AGR-2 Data Qualification Report for ATR Cycles 151B-2, 152A, 152B, 153A, 153B and 154A

This report documents the data qualification status of AGR-2 fuel irradiation experimental data from Advanced Test Reactor (ATR) Cycles 152A, 152B, 153A, 153B, and 154A, as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR-2 data streams addressed include thermocouple (TC) temperatures, sweep gas data (flow rate, pressure, and moisture content), and fission product monitoring system (FPMS) data for each of the six capsules in the experiment. A total of 13,400,520 every minute instantaneous TC and sweep gas data records were received and processed by NDMAS for this period. Of these data, 8,911,791 records (66.5% of the total) were determined to be Qualified based on NDMAS accuracy testing and data validity assessment. For temperature, there were 4,266,081 records (74% of the total TC data) that were Failed due to TC instrument failures. For sweep gas flows, there were 222,648 gas flow records (2.91% of the flow data) that were Failed. The inlet gas flow failures due to gas flow cross-talk and leakage problems that occurred after Cycle 150A were corrected by using the same gas mixture in all six capsules and the Leadout. For FPMS data, NDMAS received and processed preliminary release rate and release-to-birth rate ratio (R/B) data for three reactor cycles (Cycles 149B, 150B, and 151A) . This data consists of 45,983 release rate records and 45,235 R/B records for the 12 radionuclides reported. The qualification status of these FPMS data has been set to In Process until receipt of Quality Assurance-approved data generator reports. All of the above data have been processed and tested using a SAS®-based enterprise application software system, stored in a secure Structured Query Language database, made available on the NDMAS Web portal (http://ndmas.inl.gov), and approved by the INL STIM for release to both internal and appropriate external Very High Temperature Reactor Program participants.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Utah FORGE: Well 58-32 Stimulation Data

Pressure, temperature, and flow data from open-hole, upper perforation, and lower perforation well stimulations gathered from various tools collected at well 58-32 during phase 2C.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Fallon FORGE: GIS and Downhole Well Lithology Data

ArcGIS Map Package with MT Station Locations, 2D Seismic Lines, Well data, Known Regional Hydrothermal Systems, Regional Historic Earthquake Seismicity, Regional Temperature Gradient Data, Regional Heat Flow Data, Regional Radiogenic Heat Production, Local Geology, Land Status, Cultural Data, 2m Temperature Probe Data, and Gravity Data. Also a detailed down-hole lithology notes are provided.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Treatment Planning Constraints to Avoid Xerostomia in Head-and-Neck Radiotherapy: An Independent Test of QUANTEC Criteria Using a Prospectively Collected Dataset

Purpose: The severe reduction of salivary function (xerostomia) is a common complication after radiation therapy for head-and-neck cancer. Consequently, guidelines to ensure adequate function based on parotid gland tolerance dose-volume parameters have been suggested by the QUANTEC group and by Ortholan et al. We perform a validation test of these guidelines against a prospectively collected dataset and compared with a previously published dataset. Methods and Materials: Whole-mouth stimulated salivary flow data from 66 head-and-neck cancer patients treated with radiotherapy at the British Columbia Cancer Agency (BCCA) were measured, and treatment planning data were abstracted. Flow measurements were collected from 50 patients at 3 months, and 60 patients at 12-month follow-up. Previously published data from a second institution, Washington University in St. Louis (WUSTL), were used for comparison. A logistic model was used to describe the incidence of Grade 4 xerostomia as a function of the mean dose of the spared parotid gland. The rate of correctly predicting the lack of xerostomia (negative predictive value [NPV]) was computed for both the QUANTEC constraints and Ortholan et al. recommendation to constrain the total volume of both glands receiving more than 40 Gy to less than 33%. Results: Both datasets showed a rate of xerostomia of less than 20% when the mean dose to the least-irradiated parotid gland is kept to less than 20 Gy. Logistic model parameters for the incidence of xerostomia at 12 months after therapy, based on the least-irradiated gland, were D{sub 50} = 32.4 Gy and and {gamma} = 0.97. NPVs for QUANTEC guideline were 94% (BCCA data), and 90% (WUSTL data). For Ortholan et al. guideline NPVs were 85% (BCCA) and 86% (WUSTL). Conclusion: These data confirm that the QUANTEC guideline effectively avoids xerostomia, and this is somewhat more effective than constraints on the volume receiving more than 40 Gy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Detection of anomalous events

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Preserving nonlinear constraints in variational flow filtering data assimilation

Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by a computational model. The states of many dynamical systems of interest obey nonlinear physical constraints, and the corresponding dynamics is confined to a certain sub-manifold of the state space. Standard data assimilation techniques applied to such systems yield posterior states lying outside the manifold, violating the physical constraints. This work focuses on particle flow filters which use stochastic differential equations to evolve state samples from a prior distribution to samples from an observation-informed posterior distribution. The variational Fokker-Planck (VFP)—a generic particle flow filtering framework—is extended to incorporate non-linear, equality state constraints in the analysis. To this end, two algorithmic approaches that modify the VFP stochastic differential equation are discussed: (i) VFPSTAB, to inexactly preserve constraints with the addition of a stabilizing drift term, and (ii) VFPDAE, to exactly preserve constraints by treating the VFP dynamics as a stochastic differential-algebraic equation (SDAE). Additionally, an implicit-explicit time integrator is developed to evolve the VFPDAE dynamics. The strength of the proposed approach for constraint preservation in data assimilation is demonstrated on three test problems: the double pendulum, Korteweg-de-Vries, and the incompressible Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2026

Sap Velocity Data for East River Watershed Sites (2023-2025)

This dataset includes sap velocity measurements for aspen (populus tremuloides), fir (abies lasiocarpa), spruce (Engelmann spruce) and lodgepole pine (pinus contorta) trees at nine sites in the East River Watershed near Gothic, CO. This dataset was generated following a similar method as a previous dataset (Dataset. doi:10.15485/1647654) but was conducted at different sites in the area and now includes lodgepole pine. Site selection was done to explicitly improve understanding of topographic controls of tree water use. The data collection began in June 2023 and we provide data until December 2025 - though data collection is ongoing. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. We also provide a normalized sap velocity estimate by subtracting each measurement from that trees' annual minimum and dividing by that trees' annual maximum. This provides data for each tree and year on a 0-1 scale. This data entry contains one CSV file that includes all available sap flow data. The timestamps are provided in local time as year, day of year and hour and each measurement contains an associated latitude, longitude and site number which can be used to identify trees in a given stand. Each species is given a numeric value as: 1= populus tremuloides, 2=Engelmann spruce, 3=abies lasiocarpa and 4=pinus contorta.

abies↗
DOE OSTI2011

Google.org US Resource Project Update

The results from a new geothermal resource assessment of the United States, including for the first time detailed data for much of the eastern US, are summarized along with the techniques used to accomplish the assessment. Bottom hole temperature (BHT) data were incorporated in the eastern US, where conventional heat flow data is sparse, using BHT corrections and calculated conductivities from a regional lithology model; comparing results to overlapping conventional heat flow and BHT data for error calibration. A total of 5,000 points are now available in the northeast as opposed to the 1,000 used to produce the 2004 Geothermal Map of North America. Where neither heat flow or BHT data were not available, geophysical data (regional gravity and magnetics) were used as an ancillary predictor to the process for areas with sedimentary cover. The effectiveness of that process is demonstrated. This study uses the new heat flow data to improve the calculated heat in place to 10 km for the US. Based the preliminary results from this wok, the Appalachian Basin may contain some of the most favorable potential targets for EGS geothermal exploration in the eastern 1/3 of the United States and especially in eastern West Virginia, where temperatures of at least 150°C are predicted at a depth of 4.5 km in localized areas.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Utah FORGE: Well 16A(78)-32 Stimulation Tracer Test Results

This archive contains data from the tracer test performed during the Utah FORGE well 16A(78)-32 stimulation. This includes: 1. the raw data file from Pason in the csv format; 2. a one-page Word document from Pason that explains the headers and units used in their data file; 3. an Excel spreadsheet with concentration data and relevant Pason flow data; 4. a Word document with jpg versions of concentration charts (Figs. 1-3) and a tracer mass recovery chart (Fig. 4).

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

CSEM, Bottomhole Pressure, Salinity, and Flow-Rate Data Collected in Sep. 2020 for Proj. DE-FE0031785

This dataset consists of two directories: first directory named as EM for the controlled source electromagnetic (CSEM) survey data that are zipped per day for September 22, 23, 24, 25, and 27, 2020, and second directory named as "Bottomhole Pressure, Salinity, and Flow-Rate" for the simultaneous collection of bottomhole pressure and salinity and pump flow rate during the CSEM surveys.

CSEM↗