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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

High Flying Interns: NASA's Student Airborne Research Program

The NASA Student Airborne Research Program is an annual summer internship for upper-level undergraduate STEM majors. Each summer since 2009, we have competitively selected ~30 undergraduates from colleges and universities across the United States for this unique airborne research experience. In all past summers, students flew onboard a NASA research aircraft where they assisted in the operation of remote sensing and in situ instrumentation to study the Earth, ocean, and atmosphere. Students also participated in field trips where they acquired data to ground-truth and complement the airborne data. After their flights and field trips, students then spent the rest of summer developing individual research projects using the data they collected as well as data from previous year SARP flights, other NASA airborne campaigns, and NASA satellite data. This summer, we adapted the program to be entirely online. Each of the 28 undergraduate students still completed an individual research project using data from previous SARP flights as well as publicly available data from other airborne campaigns, satellites, and/or ground stations. Students were mentored remotely by five university faculty members, five graduate students, and several additional scientists and engineers from NASA. In order to preserve some aspects of the hands-on research experience, we also shipped each student a box of twenty-four Whole Air Sampling canisters identical to what they would have used to collect air samples onboard the NASA aircraft. Instead, each student collected ground samples near their home starting in mid-April through July. The goal of this sampling was to attempt to characterize the impacts of pandemic-related changes in emissions with time across the United States. The results of that ground sampling are being presented in scientific sessions in this meeting. In addition, students also measured PM2.5 and aerosol optical depth from June through August using sensors provided by the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) at Colorado State University. We will discuss strategies we employed to conduct research online, the unexpected opportunities that arose, and lessons learned.

STEM disciplines↗
DOE OSTI2022

Utah FORGE: Phase 1a Tensor Strainmeter Data for the April, 2022 Stimulation of Well 16A(78)-32

Data from two Tensor Optical Fiber Strainmeters that were operational during Stages 1, 2, and 3 of the April, 2022 stimulation of well 16A(78)-32. Each csv file contains data from each stimulation stage (stage1, stage2, stage3) for both Phase 1a strainmeter installations (FS01, formerly FS-C, and FS02, formerly FS1-2) in human-readable comma-separated value text files. There are two header lines in each file describing the data contained in that column along with their units, respectively. Data have been decimated from 2 to 1 Hz to match the Pason data found in the linked GDR dataset below (16A78-32 Stimulation Pason Data). These files contain the time series spanning the same time interval as the Pason data as well as ambient data for 5 hours before the stimulation and 5 hours following shut in. The station locations were chosen based on their proximity to the borehole seismometers owned and operated by the University of Utah. See README.txt for more information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Utah FORGE: Updated Phase 2C Well Location Coordinates

Utah FORGE has been established to develop, test, and improve the technologies and techniques required to develop EGS-type geothermal resources. Drilling of the first of two deep deviated wells, 16A(78)-32, will begin in the second half of 2020. This well will serve as the injection well for the injection-production well pair that will form the heart of the laboratory. This submission contains an archive of well location data within the Roosevelt Hot Springs geothermal area. An Excel spreadsheet is included containing updated GPS data for Utah FORGE wells drilled during Phase 2C (56-32, 68-32, 78-32). This data was collected over time by the Utah Geological Survey and contains all coordinates collected and the final averaged XY coordinates in both Longitude and Latitude and UTM Zone 12, NAD83. Elevation is also included. GIS shapefiles with well points are provided in the archive.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Temperature Uncertainty Modeling with Proxy Structural Data as Geostatistical Constraints for Well Siting: An Example Applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aim of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations . The data used to support the modeling are measured temperatures and structural proxies from 3D geologic modeling, the latter considered "secondary" data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally likely temperature models (i.e., realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: Vprior and entropy. Vprior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e., linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g., dilation versus distance to fault termination) should be used given the specific geothermal system. In general, Vprior from local varying mean results identify locations that are close to high values for the structural proxies: areas with highe r probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comp arison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

3D temperature modeling↗
NASA NTRS2004

Velocity and Temperature Measurement in Supersonic Free Jets Using Spectrally Resolved Rayleigh Scattering

The flow fields of unheated, supersonic free jets from convergent and convergent-divergent nozzles operating at M = 0.99, 1.4, and 1.6 were measured using spectrally resolved Rayleigh scattering technique. The axial component of velocity and temperature data as well as density data obtained from a previous experiment are presented in a systematic way with the goal of producing a database useful for validating computational fluid dynamics codes. The Rayleigh scattering process from air molecules provides a fundamental means of measuring flow properties in a non-intrusive, particle free manner. In the spectrally resolved application, laser light scattered by the air molecules is collected and analyzed using a Fabry-Perot interferometer (FPI). The difference between the incident laser frequency and the peak of the Rayleigh spectrum provides a measure of gas velocity. The temperature is measured from the spectral broadening caused by the random thermal motion and density is measured from the total light intensity. The present point measurement technique uses a CW laser, a scanning FPI and photon counting electronics. The 1 mm long probe volume is moved from point to point to survey the flow fields. Additional arrangements were made to remove particles from the main as well as the entrained flow and to isolate FPI from the high sound and vibration levels produced by the supersonic jets. In general, velocity is measured within +/- 10 m/s accuracy and temperature within +/- 10 K accuracy.

Panda, J.↗
DOE OSTI2018

Ultrasonic Acoustic Velocities During Partial Melting of a Mantle Peridotite KLB-1

Knowledge of the elastic properties of partially molten rocks is crucial for understanding low-velocity regions in the interior of the Earth. Models of fluid and solid mixtures have demonstrated that significant decreases in seismic velocity are possible with small amounts of melt, but there is very little available data for testing these models, particularly with both P and S waves for mantle compositions. We report ultrasonic measurements of P and S velocities on a partially molten KLB-1 sample at mantle conditions using a multi-anvil device at a synchrotron facility. The P, S, and bulk sound velocities decrease as melting occurs. We find that the quantity, ∂lnV S /∂lnV B (where V B is the bulk sound velocity) is lower than mechanical models estimate. Instead, our data, as well as previous data in the literature, are consistent with a dynamic melting model in which melting and solidification interact with the stress field of the acoustic wave.

58 GEOSCIENCES↗
DOE OSTI2019

Machine Learning for Computational Heterogeneous Catalysis

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

Schlexer Lamoureux, Philomena↗
DOE OSTI2023

Open‐source photovoltaic model pipeline validation against well‐characterized system data

Abstract All freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.

14 SOLAR ENERGY↗
DOE OSTI2020

Prediction of gas hydrate saturation using machine learning and optimal set of well-logs

We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S h ) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.

58 GEOSCIENCES↗
DOE OSTI2022

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluation of Am–Li neutron spectra data for active well type neutron multiplicity measurements of uranium

Safeguarding nuclear material is an important and challenging task for the international community. One particular safeguards technique commonly used for uranium assay is active neutron correlation counting. This technique involves irradiating unused uranium with ( α,n) neutrons from an Am-Li source and recording the resultant neutron pulse signal which includes induced fission neutrons. Although this non-destructive technique is widely employed in safeguards applications, the neutron energy spectra from an Am-Li sources is not well known. Several measurements over the past few decades have been made to characterize this spectrum; however, little work has been done comparing the measured spectra of various Am-Li sources to each other. This paper examines fourteen different Am-Li spectra, focusing on how these spectra affect simulated neutron multiplicity results using the code Monte Carlo N-Particle eXtended (MCNPX). Two measurement and simulation campaigns were completed using Active Well Coincidence Counter (AWCC) detectors and uranium standards of varying enrichment. The results of this work indicate that for standard AWCC measurements, the fourteen Am-Li spectra produce similar doubles and triples count rates. Finally, the singles count rates varied by as much as 20% between the different spectra, although they are usually not used in quantitative analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Guest Editorial: Advanced Data-Analytics for Power System Operation, Control, and Enhanced Situational Awareness

Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.

Xu, Yan↗
DOE OSTI2019

The Data Quality Monitoring Software for the CMS experiment at the LHC: past, present and future

The Data Quality Monitoring software is a central tool in the CMS experiment. It is used in the following key environments: (i) Online, for real-time detector monitoring; (ii) Offline, for the prompt-offline-feedback and final fine-grained data quality analysis and certification; (iii) Validation of all the reconstruction software production releases; (iv) Validation in Monte Carlo productions. Though the basic structure of the Run1 DQM system remains the same for Run2, between the Run1 and Run2 periods, the DQM system underwent substantial upgrades in many areas, not only to adapt to the surrounding infrastructure changes, but also to provide improvements to meet the growing needs of the collaboration with an emphasis on more sophisticated methods for evaluating data quality. We need to cope with the higher-energy and -luminosity proton-proton collision data, as well as the data from various special runs, such as Heavy Ion runs. In this contribution, we will describe the current DQM software, structure and workflow in the different environments. We then discuss the performance and our experiences with the DQM system in Run2. The main technical challenges which we have encountered and the solutions adopted during Run2 will also be discussed, including efficient use of memory in multithreading environments. Finally, we present the prospect of a future DQM upgrade with emphasis on functionality and long-term robustness for LHC Run3.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

The NuSTAR view of nearby Compton-thick active galactic nuclei: the cases of NGC 424, NGC 1320, and IC 2560

We present X-ray spectral analyses for three Seyfert 2 active galactic nuclei (AGNs), NGC 424, NGC 1320, and IC 2560, observed by NuSTAR in the 3-79 keV band. The high quality hard X-ray spectra allow detailed modeling of the Compton reflection component for the first time in these sources. Using quasi-simultaneous NuSTAR and Swift/XRT data, as well as archival XMM-Newton data, we find that all three nuclei are obscured by Compton-thick material with column densities in excess of ∼5 × 10{sup 24} cm{sup –2}, and that their X-ray spectra above 3 keV are dominated by reflection of the intrinsic continuum on Compton-thick material. Due to the very high obscuration, absorbed intrinsic continuum components are not formally required by the data in any of the sources. We constrain the intrinsic photon indices and the column density of the reflecting medium through the shape of the reflection spectra. Using archival multi-wavelength data we recover the intrinsic X-ray luminosities consistent with the broadband spectral energy distributions. Our results are consistent with the reflecting medium being an edge-on clumpy torus with a relatively large global covering factor and overall reflection efficiency of the order of 1%. Given the unambiguous confirmation of the Compton-thick nature of the sources, we investigate whether similar sources are likely to be missed by commonly used selection criteria for Compton-thick AGNs, and explore the possibility of finding their high-redshift counterparts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Validation of Physics Models of Geant4 using Data from CMS Experiment

CMS has tuned its simulation program and has chosen a specific physics model of GEANT4 by comparing the simulation results with dedicated test beam experiments. CMS continues to validate the physics models inside GEANT4 using the test beam data as well as collision data. Several physics lists (collection of physics models) inside the most recent version of GEANT4 provide good agreement of the energy response, resolution of pions and protons. The validation results from these studies are used to make the choice of physics list and GEANT4 version for the CMS experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Autonomous hybrid optimization of a SiO 2 plasma etching mechanism

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO 2 using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

36 MATERIALS SCIENCE↗