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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Quantum critical collapse abhors a naked singularity

Classical critical collapse yields naked singularities from smooth initial data, challenging cosmic censorship, and shaping the spectrum of primordial black holes. We show that one-loop vacuum polarization near the threshold qualitatively changes this outcome by dressing the singularity with a horizon within a controlled semiclassical regime. In analytically tractable Einstein-scalar critical spacetimes, a one-loop $s$-wave treatment linearized around self-similar backgrounds shows that regularity uniquely selects an asymptotically Minkowskian, vacuum-polarization state. Its renormalized stress tensor carries a universal quantum growing mode that competes with the classical unstable mode, shifts the critical point, and generates a trapped surface along with a finite mass gap at the new threshold, thereby enforcing horizon formation even under arbitrary fine-tuning. In primordial collapse, the threshold shift enters exponentially into the formation fraction, while the mass gap truncates the low-mass tail, suggesting potentially important consequences for the predicted mass spectrum. Furthermore, these results provide a self-consistent semiclassical treatment of critical collapse and yield sharp predictions within the one-loop, near-critical, linearized regime.

Anomalies↗
DOE OSTI2022

Online-compatible unsupervised nonresonant anomaly detection

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events—there must also be a strategy to provide context to the selected events. In this work, we propose the first complete strategy for unsupervised detection of nonresonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously trained autoencoders that are forced to be decorrelated from each other. This method can be deployed off-line for nonresonant anomaly detection and is also the first complete on-line-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2010

Search for spinning black hole binaries in mock LISA data using a genetic algorithm

Coalescing massive black hole binaries are the strongest and probably the most important gravitational wave sources in the LISA band. The spin and orbital precessions bring complexity in the waveform and make the likelihood surface richer in structure as compared to the nonspinning case. We introduce an extended multimodal genetic algorithm which utilizes the properties of the signal and the detector response function to analyze the data from the third round of mock LISA data challenge (MLDC3.2). The performance of this method is comparable, if not better, to already existing algorithms. We have found all five sources present in MLDC3.2 and recovered the coalescence time, chirp mass, mass ratio, and sky location with reasonable accuracy. As for the orbital angular momentum and two spins of the black holes, we have found a large number of widely separated modes in the parameter space with similar maximum likelihood values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning

Critical infrastructures such as power grids have become increasingly complex, connected, and vulnerable to adverse scenarios, including cyber and physical attacks and faults. Effective risk mitigation for such cyber-physical energy systems (CPES), requires preemptive knowledge of likely adversarial attack scenarios. Hybrid Attack Graph (HAG) is a structured way to represent an adversarial scenario as an attack sequence using a threat model. However, the scarcity of documented attack sequences hinders analysts and CPES planners’ ability to identify credible attack scenarios for a given CPES. We propose a data-driven Graph Convolutional Deep-Q Network (GCDQ) to address this data challenge through generating HAGs. By leveraging limited real-world observations from the MITRE ATT&CK knowledge base, our GCDQ model synthesizes realistic graphs with the targeted attribute of minimum detectability via reinforcement learning. This generative model is the first step in creating a tool to substantially boost the attack sequence dataset and enhance the performance of CPS defense-related tasks by providing insights into likely attack sequences with given attributes.

deep learning, artificial intelligence↗
DOE OSTI2017

Spherical Region Queries on Multicore Architectures

In this short paper, we report the performance of multiple thread-parallel algorithms for spherical region queries on multicore architectures motivated by a challenging data analytics application in materials science. Performances of two tree-based algorithms and a naive algorithm are compared to identify the length scales at which these approaches perform optimally. The optimal algorithm is then used to scale the driving materials science application, which is shown to deliver over 17X speedup using 32 OpenMP threads on data sets containing many millions of atoms.

Lu, Hao↗
DOE OSTI2015

Current Collector Corrosion in Ca-Ion Batteries

With significant improvements in electrical energy storage, researchers could change the way energy is generated and used. We report that one emerging approach is to change the cation that shuttles charge from lithium to calcium. Calcium cations, roughly the same size as Na + , have many attributes that make them a desirable charge carrier for energy storage applications, including deposition voltage and a porous passivation layer. However, system level issues, such as corrosion, have yet to be investigated. Corrosion of the current collectors must be considered whenever you change the electrolyte and we show that this is particularly true for calcium based systems. Reversible charge/discharge behavior that is due to corrosion can be seen with stainless steel in electrolytes containing calcium salts. This reversible behavior is similar to what might be expected from materials that are intercalating Ca, making the interpretation of electrochemical data challenging. Lastly, we have found that this corrosion reaction requires either carbon black and/or a transition metal oxide to catalyze the reaction, making it more difficult to detect. Unlike stainless steel, Graphite foil electrodes do not show high voltage reactions and can be used as a tool for testing Ca-ion cathode materials, although some reactions at low potentials have been observed.

25 ENERGY STORAGE↗
DOE OSTI2021

Enhanced Signal Processing of Distributed Brillouin Fiber Sensors using a Decoupled Radial Basis Function Network

A novel decoupled radial basis function network (D-RBFN) is proposed to accelerate signal processing and address the big data challenges associated with ultra-long distance Brillouin optical time-domain analysis (BOTDA) systems. The proposed frame- work is demonstrated on a dataset measured over a 100 km distance using a bi-directional Raman assisted BOTDA system.

Venketeswaran, Abhishek↗
DOE OSTI2026

Wire-arc Additive Manufacturing Benchmark

This is the dataset associated with the 2022 SRP Additive Manufacturing Prediction Challenge, originally hosted on Github at https://github.com/SRP-AM/SRP_AM_Prediction_Challenge. The benchmark was designed for validating prediction for the temperature history, residual stress, and distortion of an additively manufactured metal part with relatively simple geometry. A calibration problem with the same as-built geometry is provided with measured quantities of interest; including temperature histories at selective locations, post-build residual stress at selective locations, and overall distortion measurements. The challenge problem is presented with a different build sequence (i.e. thermal history). In this dataset, we include the actual recorded calibration and challenge measurements, as well as benchmark template files for testing predictions without incorporating the challenge data. Supplementary files around the materials and setup are available for transparency and reproducibility.

Bachus, Nicholas [UC Davis, Davis, CA]↗
DOE OSTI2022

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

Inventory of China's Energy-Related CO2 Emissions in 2008

Although China became the world's largest emitter of energy-related CO{sub 2} emissions in 2007, China does not publish annual estimates of CO{sub 2} emissions and most published estimates of China's emissions have been done by other international organizations. Undertaken at the request of the Energy Information Administration (EIA) of the US Department of Energy, this study examines the feasibility of applying the EIA emissions inventory methodology to estimate China's emissions from published Chinese data. Besides serving as a proof of concept, this study also helps develop a consistent and transparent method for estimating China's CO{sub 2} emissions using an Excel model and identified China-specific data issues and areas for improvement. This study takes a core set of data from the energy balances published in the China Energy Statistical Yearbook 2009 and China Petrochemical Corporation Yearbook 2009 and applies the EIA's eight-step methodology to estimate China's 2008 CO{sub 2} emissions. First, China's primary and secondary fuel types and consumption by end use are determined with slight discrepancies identified between the two data sources and inconsistencies in product categorization with the EIA. Second, energy consumption data are adjusted to eliminate double counting in the four potential areas identified by EIA; consumption data from China's Special Administrative Regions are not included. Physical fuel units are then converted to energy equivalents using China's standard energy measure of coal equivalent (1 kilogram = 29.27 MJ) and IPCC carbon emissions coefficients are used to calculate each fuel's carbon content. Next, carbon sequestration is estimated following EIA conventions for other petroleum products and non-energy use of secondary fuels. Emissions from international bunker fuels are also subtracted under the 'reference' calculation of estimating apparent energy consumption by fuel type and the 'sectoral' calculation of summing emissions across end-use sectors. Adjustments for the China-specific conventions of reporting foreign bunkers and domestic bunkers fueling abroad are made following IPCC definitions of international bunkers and EIA reporting conventions, while the sequestration of carbon in carbon steel is included as an additional adjustment. Under the sectoral approach, fuel consumption of bunkers and other transformation losses as well as gasoline consumption are reallocated to conform to EIA sectoral reporting conventions. To the extent possible, this study relies on official energy data from primary sources. A limited number of secondary sources were consulted to provide insight into the nature of consumption of some products and to guide the analysis of carbon sequestered in steel. Beyond these, however, the study avoided trying to estimate figures where directly unavailable, such as natural gas flaring. As a result, the basic calculations should be repeatable for other years with the core set of data from National Bureau of Statistics and Sinopec (or a similarly authoritative source of oil product data). This study estimates China's total energy-related CO{sub 2} emissions in 2008 to be 6666 Mt CO{sub 2}, including 234.6 Mt of non-fuel CO{sub 2} emissions and 154 Mt of sequestered CO{sub 2}. Bunker fuel emissions in 2008 totaled 15.9 Mt CO{sub 2}, but this figure is underestimated because fuel use by Chinese ship and planes for international transportation and military bunkers are not included. Of emissions related to energy consumption, 82% is from coal consumption, 15% from petroleum and 3% from natural gas. From the sectoral approach, industry had the largest share of China's energy-related CO{sub 2} emissions with 72%, followed by residential at 11%, transport and telecommunications at 8%, and the other four (commerce, agriculture, construction and other public) sectors having a combined share of 9%. Thermal electricity and (purchased) heat (to a lesser degree) are major sources of fuel consumption behind sectoral emissions, responsible for 2533 Mt CO2 and 321 Mt CO{sub 2}, respectively. The 2008 emissions estimated for China in this study falls within the range of other international estimates, and suggests that the EIA methodology can be adopted to estimate China's emissions if the proper adjustments are made. While these results are helpful in understanding China's annual emissions, several key areas of data challenges affect the accuracy of this estimate. Industrial process-based emissions - an important source of emissions given China's industry-intensive economy and size of its cement sector - have not been included in this calculation and could be the focus of further model refinement. The accuracy of the Chinese emissions estimate can be further improved by addressing two unreported international bunker categories and developing China-specific carbon sequestration coefficients for non-fuel use energy products.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Dark Energy Studies with LSST Image Simulations, Final Report

This grant funded the development and dissemination of the Photon Simulator (PhoSim) for the purpose of studying dark energy at high precision with the upcoming Large Synoptic Survey Telescope (LSST) astronomical survey. The work was in collaboration with the LSST Dark Energy Science Collaboration (DESC). Several detailed physics improvements were made in the optics, atmosphere, and sensor, a number of validation studies were performed, and a significant number of usability features were implemented. Future work in DESC will use PhoSim as the image simulation tool for data challenges used by the analysis groups.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Frontiers in the Economics of Widespread, Long-Duration Power Interruptions: Proceedings from an Expert Workshop

The costs to electric utility customers from short-term, limited geographic-scale power disruptions have been studied for many years. However, there is increasing interest among regulators, policy-makers, and utilities in developing and implementing methods for analyzing interruptions that are of longer duration (days, weeks, or longer) and of a larger geographic scope (entire metropolitan areas or regions which may extend across multiple service territories). A 2017 report from the U.S. National Academies of Sciences, Engineering, and Medicine indicated the importance of “develop[ing] comprehensive studies to assess the value to customers of improved reliability and resilience…during large-area, long-duration blackouts.” As a contribution toward fulfilling this recommendation, the U.S. Department of Energy’s (DOE) Office of Electricity’s Transmission Permitting and Technical Assistance Division, in partnership with Lawrence Berkeley National Laboratory (LBNL), convened a workshop to identify research needs and discuss potential avenues for methodological advances in the economics of widespread, long-duration power interruptions. The workshop was organized around six key themes in the economics of widespread, long-duration interruptions. LBNL conducted a competitive solicitation for white papers on these themes, and the authors selected were leading experts in academia, industry, and the non-profit sector. They were tasked with assessing the state of knowledge on particular topics, and identifying research needs and promising directions to expand it. These topics included definitions of resilience and reliability; regional economic modeling approaches; uncertainty quantification; data challenges and opportunities; contingent valuation survey techniques; and reduced-form analytical tools for assessing the impacts of power interruptions of this scale. The authors of the six white papers presented their findings at the workshop, titled “Frontiers in the Economics of Widespread, Long-Duration Power Interruptions,” and held in Washington, D.C., on March 6, 2018. Each paper was the subject of its own session, with the presentations followed by comments by assigned discussants, and in turn a general discussion of the papers, their findings, and the issues they raised. In addition to the authors, discussants, and LBNL and DOE staff, the workshop participants comprised government, industry, and academic thought leaders from across the country.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Dark Energy Studies with LSST using the Photon Simulator (Final Report)

This grant funded the development and dissemination of the Photon Simulator (PhoSim) for the purpose of studying dark energy at high precision with the upcoming Large Synoptic Survey Telescope (LSST) astronomical survey. The work was in collaboration with the Dark Energy Science Collaboration (DESC). Several detailed physics improvements were made in the optics, atmosphere and sensor, a number of validation studies were performed, and a significant number of usability features were implemented. Future work in DESC will use PhoSim as the image simulation tool for data challenges used by the analysis groups.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Retained Hydrogen Literature Study for DWPF

During Defense Waste Processing Facility (DWPF) operation in the Slurry Receipt and Adjustment Tank (SRAT) and the Slurry Mix Evaporator (SME), hydrogen is primarily produced by radiolysis and catalytic generation. The hydrogen is released from the sludge by agitation and removed from the vapor space by purging with air. Upon loss of agitation, the hydrogen begins accumulating in the sludge, and releases rapidly upon the resumption of agitation. If enough hydrogen accumulated in the sludge, and released rapidly upon the start of agitation, the hydrogen concentration in the vapor space could exceed the lower flammability limit (4 vol %). Savannah River Remediation (SRR) currently manages the risk for retained hydrogen in the DWPF vessels by implementing a Retained Hydrogen Program. The program primarily relies on the timely operation of vessel agitators to ensure that the retained hydrogen eruptions that could lead to flammable vapor spaces do not occur. The current program relies on conservative assumptions concerning gas release and retention (e.g., instantaneous release, conservative hydrogen bubble fraction, no release during quiescent periods) to develop allowable vessel quiescent times (Q-times). SRR would like to relax the conservative assumptions to the Retained Hydrogen Program and allow the Retained Hydrogen Program to extend past Sludge Batch 9. Relaxing the assumptions could potentially allow longer Q-times during agitator stoppages. SRR requested Savannah River National Laboratory (SRNL) personnel to perform a literature study to extract industry data pertaining to retained hydrogen and apply it to the DWPF process. The authors conducted a review of the technical literature, including work from the Savannah River Site (SRS), SRNL, the Hanford site, Pacific Northwest National Laboratory (PNNL), and the Sellafield site to identify literature describing the retention and release of gases with non-Newtonian slurries. The study was undertaken with the knowledge that SRS wastes are unique which makes using non-SRS data challenging. The literature search focused on the following topics: Mixing or blend time in Newtonian and non-Newtonian fluids, Quiescent release of gas from an unagitated non-Newtonian fluid, Release of gas from an agitated non-Newtonian fluid, and Changes in fluid rheology as a function of settling time.

08 HYDROGEN↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗