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DOE OSTI2021

Multi-barrier wellbore integrity inspection system with eccentricity correction

A downhole inspection system includes a neutron imaging device operable to generate data for detecting potential wellbore anomalies and an electromagnetic imaging device operable to generate data for detecting potential wellbore eccentricity. The neutron imaging device includes a neutron generator operable to emit neutrons, and a neutron detector fixed relative to the neutron generation unit and operable to detect backscattered neutrons from a surrounding environment. The electromagnetic imaging device includes at least one transmitter for generating electromagnetic pulse, and at least one receiver for detecting returning electromagnetic pulse. Correlation of the neutron imaging data with the electromagnetic imaging data provides additional data regarding the potential wellbore anomalies.

Kasten, Ansas Matthias↗

NASA NTRS1985

Foreland Basin Structures and Remote Sensing

Rocky Mountain foreland basins are somewhat unique in that the basins may exhibit a variety of structural styles. It is generally agreed that shortening has occurred in the foreland basement but the cause is controversial: vertical vs compressional horizontal tectonics. Even when shortening is attributed to compression, the attitude (dip) of the fault plane and whether the horizontal or vertical component of movement is dominant is unconstrained. The controversy is difficult to resolve from surface data alone due to the variety of possible interpretations. Detailed surface mapping and geologic modeling are needed to constrain subsurface interpretations. In many areas of the Wind River and Bighorn basins detailed geologic maps do not exist. State-of-the-art remote sensing data could potentially provide an efficient means of mapping surface geology. State-of-the-art remote sensing systems now provide geometrically correct data at 30 meter pixel size and increased spectral coverage, capable of more detailed geologic analyses. These data can be photographically enlarged to 1:24,000 scale and combined with 7 1/2' uses topographic quads to provide an excellent base map for geologic interpretations.

Paylor, E. D.↗

NASA NTRS1987

Cloud chemistry on Jupiter

Chemical equilibrium models used currently to interpret observations of Jupiter are reexamined using new data defining thermal profiles, which are substantially different from those used in the previous models. A model is developed for the chemical reactions controlling the composition of the upper troposphere on Jupiter, specifically the cloud-forming region from 10 bar to 0.1 bar, which includes, for the first time, the effects of aqueous chemistry on the composition and the vertical distribution of many measurable species in the atmosphere, identifying the factors influencing their abundances above the H2O cloud. The thermodynamic data for potential condensates on Jupiter, i.e., NH3(s), NH4SH(s), (NH4)2S(s), and H2S(s), are reexamined, recognizing the lack of data on sulfides for the temperature range of interest on Jupiter. Vertical profiles of mixing ratios for CO2, H2S, NH3, and H2, obtained for several assumed bulk abundances with respect to solar, are presented.

Carlson, Barbara E.↗

NASA NTRS2007

Stirling System Modeling for Space Nuclear Power Systems

A dynamic model of a high-power Stirling convertor has been developed for space nuclear power systems modeling. The model is based on the Component Test Power Convertor (CTPC), a 12.5-kWe free-piston Stirling convertor. The model includes the fluid heat source, the Stirling convertor, output power and heat rejection. The Stirling convertor model includes the Stirling cycle thermodynamics, heat flow, mechanical mass-spring damper systems, and the linear alternator. The model was validated against test data. Both nonlinear and linear versions of the model were developed. The linear version algebraically couples two separate linear dynamic models; one model of the Stirling cycle and one model of the thermal system, through the pressure factors. Future possible uses of the Stirling system dynamic model are discussed. A pair of commercially available 1-kWe Stirling convertors is being purchased by NASA Glenn Research Center. The specifications of those convertors may eventually be incorporated into the dynamic model and analysis compared to the convertor test data. Subsequent potential testing could include integrating the convertors into a pumped liquid metal hot-end interface. This test would provide more data for comparison to the dynamic model analysis.

Lewandowski, Edward J.↗

NASA NTRS2008

Stirling System Modeling for Space Nuclear Power Systems

A dynamic model of a high-power Stirling convertor has been developed for space nuclear power systems modeling. The model is based on the Component Test Power Convertor (CTPC), a 12.5-kWe free-piston Stirling convertor. The model includes the fluid heat source, the Stirling convertor, output power, and heat rejection. The Stirling convertor model includes the Stirling cycle thermodynamics, heat flow, mechanical mass-spring damper systems, and the linear alternator. The model was validated against test data. Both nonlinear and linear versions of the model were developed. The linear version algebraically couples two separate linear dynamic models; one model of the Stirling cycle and one model of the thermal system, through the pressure factors. Future possible uses of the Stirling system dynamic model are discussed. A pair of commercially available 1-kWe Stirling convertors is being purchased by NASA Glenn Research Center. The specifications of those convertors may eventually be incorporated into the dynamic model and analysis compared to the convertor test data. Subsequent potential testing could include integrating the convertors into a pumped liquid metal hot-end interface. This test would provide more data for comparison to the dynamic model analysis.

Lewandowski, Edward J.↗

NASA NTRS2024

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗

DOE OSTI2015

Groundwater monitoring report streamlining techniques - 15305

Compliance with national and state environmental regulations (e.g. RCRA and CERCLA [SuperFund] requires Savannah River Site (SRS) to extensively collect and report groundwater monitoring data, with potential fines for missed reporting deadlines. Large reports, which may contain dozens to hundreds of monitoring stations, are especially challenging to present data in a way that is quick and easy to interpret. Initially analyzing hundreds to thousands of records of data to be summarized in the report can be daunting, and producing maps can be extremely time consuming. Several utilities have been developed at SRS to facilitate production of the regulatory reports which include data tables, charts, maps, and cross-sections. Components of each report are generated in accordance with complex sets of regulatory requirements specific to each site monitored. SRS developed a relational database to incorporate the detailed reporting rules with the groundwater data, and created a set of automation tools to interface with the information and generate the report components. Maps produced with ArcGIS use a technique that can be repeatedly used to display data very quickly and in a consistent form. Cross-sections developed with RockWorks provide a more technical and geological analysis and representation of well and boring data, while allowing for automated data placement. These process improvements have streamlined the repetitive job of creating data tables, charts, maps, and cross-sections. It has also enhanced report quality and consistency by centralizing the information, and has reduced manpower and production time through automated efficiencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Measurement of central exclusive π⁺ π⁻ production in p p ¯ collisions at √s = 0.9 and 1.96 TeV at CDF

We measure exclusive π⁺ π⁻ production in proton-antiproton collisions at center-of-mass energies √s = 0.9 and 1.96 TeV in the Collider Detector at Fermilab. We select events with two oppositely charged particles, assumed to be pions, with pseudorapidity |η| < 1.3 and with no other particles detected in |η| < 5.9. We require the π⁺ π⁻ system to have rapidity |y| < 1.0. The production mechanism of these events is expected to be dominated by double pomeron exchange, which constrains the quantum numbers of the central state. The data are potentially valuable for isoscalar meson spectroscopy and for understanding the pomeron in a region of transition between nonperturbative and perturbative quantum chromodynamics. The data extend up to dipion mass M(π⁺ π⁻) = 5000 MeV / c² and show resonance structures attributed to f₀ and f₂ (1270) mesons. From the π⁺ π⁻ and K⁺K⁻ spectra, we place upper limits on exclusive χ c0 (3415) production.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Development of a Smart Signal Detection Method for Cyclic Voltammetry via Artificial Neural Intelligence

The electrorefiner (ER) is the heart of pyroprocessing technology which is a high-temperature method for separating uranium from Used Nuclear Fuel (UNF). It is important to improve this technology with respect to nuclear materials detection and accountability. Artificial Neural Intelligence (ANI) is a novel data analysis and simulation method that can be applied to electrochemical data sets. This computational code, which has been performed using the commercial software MATLAB, can be trained to generalize adequate electrical current and potential simulated data sets for the unseen data with a high accuracy of prediction. For this purpose, a massive collection of cyclic voltammetry (CV) data sets by Hoover (2014), for 0.5, 1, 2.5, and 5 wt% of zirconium chloride in LiCl-KCl molten salt with different scan rates at 773 K has been considered. The computer is trained via ANI to predict the unseen data after providing suitable hidden layers and validation numbers. In addition, this work can trace the CV plot for a blind condition by interpolating between two simulated data set. The different hidden layers with various neurons (from 5 to 30) at several validation numbers (from 5 to 30) has been studied and the average percent error between experimental and theoretical data for 0.5 wt% with 200 and 450 mV/s has been calculated. Preliminary results demonstrate that if the number of hidden layers increases from one to three, the average error falls down from 44% to around 8%. The best condition which gives a minimum average percent error has been discovered and the simulated CV graph has been compared with the experimental data. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Why concave rather than convex inflaton potential?

The Planck data on cosmic microwave background indicates that the Starobinsky-type model with concave inflation potential is favored over the convex-type chaotic inflation. Is there any reason for that? Here we argue that if our universe began with a Euclidean wormhole, then the Starobinsky-type inflation is probabilistically favored. It is known that for a more generic choice of parameters than that originally assumed by Hartle and Hawking, the Hartle–Hawking wave function is dominated by Euclidean wormholes, which can be interpreted as the creation of two classical universes from nothing. We show that only one end of the wormhole can be classicalized for a convex potential, while both ends can be classicalized for a concave potential. The latter is therefore more probable.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1971

Preprocessing techniques to reduce atmospheric and sensor variability in multispectral scanner data.

Multispectral scanner data are potentially useful in a variety of remote sensing applications. Large-area surveys of earth resources carried out by automated recognition processing of these data are particularly important. However, the practical realization of such surveys is limited by a variability in the scanner signals that results in improper recognition of the data. This paper discusses ways by which some of this variability can be removed from the data by preprocessing with resultant improvements in recognition results.

Crane, R. B.↗

NASA NTRS1994

Using the International Directory Network and connected information systems for research in the Earth and space sciences

Many researchers are becoming aware of the International Directory Network (IDN), an interconnected federation of international directories to Earth and space science data. Are you aware, however, of the many Earth-science-relevant information systems which can be accessed automatically from the directories? After determining potentially useful data sets in various disciplines through directories such as the Global Change Master Directory, it is becoming increasingly possible to get detailed information about the correlative possibilities of these data sets through the connected guide/catalog and inventory systems. Such capabilities as data set browse, subsetting, analysis, etc. are available now and will be improving in the future.

Thieman, J. R.↗

DOE OSTI2023

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. Here, the quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6–31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6–31+G(2df,p) and ωB97XD/6–311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2001

The Potential of Using Landsat 7 Data for the Classification of Sea Ice Surface Conditions During Summer

During spring and summer, the Surface of the Arctic sea ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt pond diameters generally do not exceed a couple of meters, the spatial resolutions of sensors like AVHRR and MODIS are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the pan-chromatic band). The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and meltponded areas, have different signatures in the individual Landsat bands. Consistent with in-situ albedo measurements, melt ponds show up as blueish whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable the distinction of melt ponds. The melt pond fraction for the scene studied in this paper was 37%.

Markus, Thorsten↗

NASA NTRS2010

Electrostatic Evaluation of the ARES I FTS Antenna Materials

Surface resistivity and volume resistivity data show all the tested non-metallic materials of the Ares I FTS antenna assembly to be insulative. The external materials (White foam, phenolic) should be able to develop a large surface charge density upon tribocharging with ice crystal impingement. Dielectric breakdown tests on the FTS antenna housing materials show that each of the insulative materials are very resistive to electrical breakdown. The thicknesses of these materials in a nominal housing should protect the antenna from direct breakdown from external triboelectric charging potentials. Per data from the Air Force study, a maximum external electric potential in the range of 100kV can be developed on surfaces tribocharged by ice crystal impingement. Testing showed that under operational pressure ranges, this level of exterior voltage can result in a potential of about 6 kV induced on the electrically floating interior antenna vanes. Testing the vanes up to this voltage level showed that electrostatic discharges can occur between the electrically floating vanes and the center, grounded screw heads. Repeated tests with multiple invisible and visible discharges caused only superficial physical damage to the vanes. Fourier analysis of the discharge signals showed that the frequency range of credible discharges would not interfere with the nominal operation of the FTS antenna. However, due to the limited scope, short timetable, and limited funding of this study, a direct measurement of the triboelectric charge that could be generated on the Ares I antenna housing when the rocket traverses an ice cloud at supersonic speeds was not performed. Instead, data for the limited Air Force study [3] was used as input for our experiments. The Air Force data used was not collected with a sensor located to provide us with the best approximation at the geometry of the Ares I rocket, namely that of the windshield electrometer, because brush discharges to the metal frame of the windshield periodically depleted any charge accumulated. The configuration of the Ares I antenna assembly does not include any exposed metals in the vicinity and the windshield data could not be used. Since the windshield sensor data was unusable, we decided that the Patch 2 location would provide us with a rough approximation to the Ares I antenna configuration and would give us an indication of the possible charging levels that would develop. This was the data that we used in this study. Whether these charging levels would be of the same order of magnitude as the actual charges developed by the Ares I traversing a cloud with ice particles is at this point unknown. An actual experimental test, requiring the acquisition of additional instrumentation, is strongly advised before a final recommendation can be formulated regarding the safe levels of electrostatic charging on the antenna housing. Thus the results of this study should be considered to be preliminary.

Hogue, Michael D.↗

NASA NTRS1988

A design for a ground-based data management system

An initial design for a ground-based data management system which includes intelligent data abstraction and cataloging is described. The large quantity of data on some current and future NASA missions leads to significant problems in providing scientists with quick access to relevant data. Human screening of data for potential relevance to a particular study is time-consuming and costly. Intelligent databases can provide automatic screening when given relevent scientific parameters and constraints. The data management system would provide, at a minimum, information of availability of the range of data, the type available, specific time periods covered together with data quality information, and related sources of data. The system would inform the user about the primary types of screening, analysis, and methods of presentation available to the user. The system would then aid the user with performing the desired tasks, in such a way that the user need only specify the scientific parameters and objectives, and not worry about specific details for running a particular program. The design contains modules for data abstraction, catalog plan abstraction, a user-friendly interface, and expert systems for data handling, data evaluation, and application analysis. The emphasis is on developing general facilities for data representation, description, analysis, and presentation that will be easily used by scientists directly, thus bypassing the knowledge acquisition bottleneck. Expert system technology is used for many different aspects of the data management system, including the direct user interface, the interface to the data analysis routines, and the analysis of instrument status.

Lambird, Barbara A.↗

DOE OSTI2015

Measurement of Central Exclusive $\pi^+ \pi^-$ Production in $p-\bar{p}$ Collisions at $\sqrt{s} = 0.9$ and 1.96 TeV at CDF

We have measured exclusive π + π - production in proton-antiproton collisions at center-of- mass energies $\sqrt{s}$ = 0.9 and 1.96 TeV in the Collider Detector at Fermilab. We sel ected events with two oppositely charged particles, assumed to be pions, with pseudorapidity | η | < 1.3 and with no other particles detected in | η | < 5.9. We required the π + π - system to have rapidity | y | < 1.0. The production mechanism of these hadrons is dominated by do uble pomeron exchange, which constrains the quantum numbers of the central state. The data are potentially valuable for isoscalar meson spectroscopy, and for understanding the pomeron in a r egion of transition between non- perturbative and perturbative quantum chromodynamics. Th e data extend up to dipion mass 5000 MeV/$c^2$ , and show resonance structures attributed to $f_0$ and $f_2$ (1270) mesons. We place upper limits on exclusive $χ_{c0}$(3415) production using the π + π - and K + K - decay channels

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗