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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Nucleation of soot: experimental assessment of the role of polycyclic aromatic hydrocarbon (PAH) dimers

The irreversible dimerization of polycyclic aromatic hydrocarbons (PAHs) – typically pyrene (C 16 H 10 ) dimerization – is widely used in combustion chemistry models to describe the soot particle inception step. This study concerns itself with the detection and identification of dimers of flame-synthesized PAH radicals and closed-shell molecules and an experimental assessment of the role of these PAH dimers for the nucleation of soot. To this end, flame-generated species were extracted from an inverse co-flow flame of ethylene at atmospheric pressure and immediately diluted with excess nitrogen before the mixture was analyzed using flame-sampling tandem mass spectrometry with collision-induced fragmentation. Signal at m/z = 404.157 (C 32 H 20 ) and m/z = 452.157 (C 36 H 20 ) were detected and identified as dimers of closed-shell C 16 H 10 and C 18 H 10 monomers, respectively. A complex between a C 13 H 9 radical and a C 24 H 12 closed-shell PAH was observed at m/z = 465.164 (C 37 H 21 ). However, a rigorous analysis of the flame-sampled mass spectra as a function of the dilution ratio, defined as the ratio of the flow rates of the diluent nitrogen to the sampled gases, indicates that the observed dimers are not flame-born, but are produced in the sampling line. In agreement with theoretical considerations, this paper provides experimental evidence that pyrene dimers cannot be a key intermediate in particle inception at elevated flame temperatures.

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

Spatiotemporal evolution of melt ponds on Arctic sea ice

Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Report on local data recovery approaches suitable for weather and climate prediction (Deliverable 1.3) (V.1.0)

Numerical weather and climate prediction rates as one of the scientific applications whose accuracy improvements greatly depend on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increasing average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys approaches for fault-tolerance in numerical algorithms and system resilience in parallel simulations from the perspective of numerical weather and climate prediction systems. A selection of existing strategies is analyzed, featuring interpolation-restart and compressed checkpointing for the numerics, in-memory checkpointing, user-level failure mitigation-based and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analyzed and some recommendations outlined for future developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Precipitation Trade Wind Cumuli within EUREC 4 A Field Campaign Report

The campaign Trades Cumuli Precipitation Initiation and Characterization (TCPIC) within the overarching Elucidating the Role of Clouds-Circulation Coupling in Climate EUREC 4 A campaign took place from 17 January 2020 to 20 February 2020, a total of 35 days. The campaign complemented the more extensive EUREC 4 A campaign focusing on cumulus clouds' role in the Earth's climate. TCPIC concentrated on understanding what triggers the onset and development of precipitation in trade wind cumulus clouds. The campaign requested the deployment of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) roll, pitch, and heave (RPH) stabilization platform to stabilize two radars on the Maria S. Merian research vessel (RV). The advanced remote-sensing techniques from stabilized radars characterized the entire precipitation life cycle, from the formation of larger cloud drops to precipitation evaporating on its way to the surface. Retrievals of evaporation rates, to track how rain affects the trade wind boundary layer, will be based on the zenith-pointing Doppler observations collected from the stabilized micro rain radar (MRR). Figure 1 shows the instrumentation's deployment on the RV: we installed the AMF RPH stabilization platform and the radars on the uppermost deck (peil deck) of the ship. For installing the devices, we coordinated a test phase of the equipment on the Maria S. Merian RV from 25 to 29 October 2019 in Emden harbor, Germany. In this testing phase, we set up all the connections, tested how to mount the W-band and the micro rain radars firmly on the table, and experimented with guided movements of the table to test the resistance of the whole system. Figure 1 shows the final tests conducted in Emden with the stable platform and the radars mounted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A New Approach to Fundamental Mechanism Discovery in Polymer Upcycling

We present a new experimental methodology for detailed experimental investigations of depolymerization reactions over solid catalysts. This project aims to address a critical need in fundamental research on chemical upcycling of polymers – the lack of rapid, sensitive, isomerselective probing techniques for the detection of reaction intermediates and products. Our method combines a heterogeneous catalysis reactor for the study of multiphase (gas/polymer melt/solid) systems, coupled to a vacuum UV photoionization time-of-flight mass spectrometer. This apparatus draws on our expertise in probing complex gas-phase chemistry and enables highthroughput, detailed chemical speciation measurements of the gas phase above the catalyst, providing valuable information on the heterogeneous catalytic reactions. Using this approach, we investigated the depolymerization of high-density polyethylene (HDPE) over Ir-doped zeolite catalysts. We showed that the product distribution was dominated by low-molecular weight alkenes with terminal C=C double bonds and revealed the presence of many methyl-substituted alkenes and alkanes, suggesting extensive methyl radical chemistry. In addition, we investigated the fundamental reactivity of model oligomer molecules n-butane and isobutane over ZSM-5 zeolites. We demonstrated the first direct detection of methyl radical intermediates, confirming the key role of methyl in zeolite-catalyzed activation of alkanes. Our results show the potential of this experimental method to achieve deep insight into the complex depolymerization reactions and pave the way for detailed mechanistic studies, leading to increased fundamental understanding of key processes in chemical upcycling of polymers.

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

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Advanced Measurements for Resilient Integration of Inverter-Based Resources: PROGRESS MATRIX Year-1 Report

As nearly every aspect of the electric power grid undergoes rapid change, measurement technologies that support grid operation and planning must evolve as well. Recent large-scale deployments of inverter-based resources (IBRs) have brought to the forefront the critical need for new measurement technologies. Though these IBRs are vital to achieving the nation’s clean energy goals, their rapid deployment has in some cases led to negative impacts on the reliability and security of the bulk power system (BPS). Advanced power system measurements, including synchronized phasor and waveform measurements, are key to making IBR integration secure and reliable. To this end, the Department of Energy (DOE) initiated a project in 2022 to develop advanced measurement capabilities and analytics that will accelerate adoption of IBRs while improving the reliability and resilience of the BPS. This report discusses a portion of the findings from the project’s first year, which focused on surveying existing measurement capabilities of partner utilities and comparing these capabilities with the requirements of applications that support IBR integration. This report also discusses how these findings will guide the development and demonstration of a set of applications in the project’s second year.

24 POWER TRANSMISSION AND DISTRIBUTION↗