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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

High-resolution infrared spectroscopy of jet cooled trans -deuteroxycarbonyl ( trans -DOCO) radical

The rovibrational spectrum of jet cooled trans-deuteroxycarbonyl (trans-DOCO) radical has been explored at suppressed-Doppler resolution via direct infrared absorption spectroscopy. The trans-DOCO is produced in a supersonic slit discharge of rare-gas/CO mixture doped with D 2 O, whereby the OD forms an energized adduct with CO, cooling in the supersonic expansion and stabilizing DOCO in the trans well. Active laser-frequency stabilization and collisional quenching of Doppler broadening along the slit axis yield <10 MHz frequency precision, with the absorbance noise approaching the quantum shot-noise limit. The current high-resolution spectral results are in excellent agreement with recent studies of the trans-DOCO radical by infrared frequency comb spectroscopy under room temperature conditions [Bui et al., Mol. Phys. 116, 3710 (2018)]. Combined with previous microwave/millimeter wave rotational studies, the suppressed-Doppler infrared data permit characterization of the vibrational ground state, improved structural parameters for the OD stretch vibrational level, and trans-DOCO spin-rotation information in both ground and excited vibrational states. Additionally, the infrared data reveal a-type and much weaker b-type contributions to the spectrum, analysis of which yields orientation of the OD stretch transition dipole moment in the body fixed frame. Of dynamical interest is whether the nascent trans-DOCO complex formed in the entrance channel has sufficient time to convert into the cis-DOCO isomer, or whether this is quenched by rapid stabilization into the trans-DOCO well. Finally, ab initio and Rice–Ramsperger–Kassel–Marcus analysis of the intrinsic reaction coordinate for trans-DOCO to cis-DOCO interconversion rates supports the latter scenario, which helps explain the failure of previous high resolution infrared efforts to detect cis-hydroxycarbonyl.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

NGC 1980 Is Not a Foreground Population of Orion: Spectroscopic Survey of Young Stars with Low Extinction in Orion A

We perform a spectroscopic survey of the foreground population in Orion A with MMT/Hectospec. We use these data, along with archival spectroscopic data and photometric data, to derive spectral types, extinction values, and masses for 691 stars. Using the Spitzer Space Telescope data, we characterize the disk properties of these sources. We identify 37 new transition disk (TD) objects, 1 globally depleted disk candidate, and 7 probable young debris disks. We discover an object with a mass of less than 0.018–0.030 M {sub ⊙}, which harbors a flaring disk. Using the H α emission line, we characterize the accretion activity of the sources with disks, and confirm that the fraction of accreting TDs is lower than that of optically thick disks (46% ± 7% versus 73% ± 9%, respectively). Using kinematic data from the Sloan Digital Sky Survey and APOGEE INfrared Spectroscopy of the Young Nebulous Clusters program (IN-SYNC), we confirm that the foreground population shows similar kinematics to their local molecular clouds and other young stars in the same regions. Using the isochronal ages, we find that the foreground population has a median age of around 1–2 Myr, which is similar to that of other young stars in Orion A. Therefore, our results argue against the presence of a large and old foreground cluster in front of Orion A.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used to develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.

Bramer, Lisa M.↗
DOE OSTI2019

SECARB Project Overview Book

In 2003, the United States Department of Energy (DOE) issued awards initiating seven Regional Carbon Sequestration Partnerships (RCSP) spanning the United States and portions of Canada. Managed by DOE’s National Energy Technology Laboratory (NETL), the RCSPs currently represent more than 400 state agencies, universities, and private companies, spanning 43 states, three Native American Organizations, and four Canadian provinces. The Southern States Energy Board (SSEB) received an award on October 1, 2003, which established the Board’s overall management and administration of the Southeast Regional Carbon Sequestration Partnership, or SECARB. The geographical region currently includes 13 states and a network of more than 100 stakeholders. The Nation’s leading scientists, university researchers, national laboratories, industrial representatives, environmental organizations, and many others have taken a proactive approach at identifying and characterizing the most promising options for technology deployment and carbon dioxide (CO2) utilization and storage (CCUS) for the Southeast. The results obtained during these injections will be important to the future commercialization of CCUS technologies and the continued use of fossil fuels as a significant energy source in a manner that is environmentally responsible. Each of the six SECARB project locations has been locally coordinated by its own field team. The field teams assume responsibility for the technical scope of work, local education and outreach, permitting, monitoring, verification, and accounting (MVA), and maintaining the validation test’s schedule and budget. Each team contributes new information to the continued characterization of the region. In addition, a task is dedicated to integrating field data and filling gaps in regional characterization data sets. Data and tools developed in the Continued Characterization task are incorporated into a relational database and geographic information system (GIS). All field tests, the continued characterization project, and the cross-cutting functions are designed to support the DOE roadmap by validating technologies and identifying locations throughout the region that could support full-scale CCUS deployment opportunities. Detailed fact sheets for the SECARB projects are available on the SECARB website at www.secarbon.org (see “Projects” tab).

RCSP↗
NASA NTRS2001

Mars Array Technology Experiment Developed to Test Solar Arrays on Mars

Solar arrays will be the power supply for future missions to the planet Mars, including landers, rovers, and eventually human missions to explore the Martian surface. Until Mars Pathfinder landed in July 1997, no solar array had been used on the surface. The MATE package is intended to measure the solar energy reaching the surface, characterize the Martian environment to gather the baseline information required for designing power systems for long-duration missions, and to quantify the performance and degradation of advanced solar cells on the Martian surface. To measure the properties of sunlight reaching the Martian surface, MATE incorporates two radiometers and a visible/NIR spectrometer. The radiometers consist of multiple thermocouple junctions using thin-film technology. These devices generate a voltage proportional to the solar intensity. One radiometer measures the global broadband solar intensity, including both the direct and scattered sunlight, with a field of view of approximately 130. The second radiometer incorporates a slit to measure the direct (unscattered) intensity radiation. The direct radiometer can only be read once per day, with the Sun passing over the slit. The spectrometer measures the global solar spectrum with two 256-element photodiode arrays, one Si sensitive in the visible range (300 to 1100 nm), and a second InGaAs sensitive to the near infrared (900 to 1700 nm). This range covers 86 percent of the total energy from the Sun, with approximately 5-nm resolution. Each photodiode array has its own fiber-optic feed and grating. Although the purpose of the MATE is to gather data useful in designing solar arrays for Mars surface power systems, the radiometer and spectrometer measurements are expected to also provide important scientific data for characterizing the properties of suspended atmospheric dust. In addition to measuring the solar environment of Mars, MATE will measure the performance of five different individual solar cell types and two different solar cell strings, to qualify advanced solar cell types for future Mars missions. The MATE instrument, designed for the Mars-2001 Surveyor Lander mission, contains a capable suite of sensors that will provide both scientific information as well as important engineering data on the operation of solar power systems on Mars. MATE will characterize the intensity and spectrum of the solar radiation on Mars and measure the performance of solar arrays in the Mars environment. MATE flight hardware was built and tested at the NASA Glenn Research Center and is ready for flight.

Landis, Geoffrey A.↗
DOE OSTI2013

Analysis of Potential Leakage Pathways and Mineralization within Caprocks for Geologic Storage of CO 2

We used a multifaceted approach to investigate the nature of caprocks above, and the interface between, reservoir-quality rocks that might serve as targets for carbon storage. Fieldwork in southeastern Utah examined the regional- to m-scale nature of faults and fractures across the sedimentiological interfaces. We also used microscopic analyses and mechanical modeling to examine the question as to how the contacts between units interact, and how fractures may allow fluids to move from reservoirs to caprock. Regional-­scale analyses using ASTER data enabled us to identify location of alteration, which led to site-­specific studies of deformation and fluid flow. In the Jurassic Carmel Formation, a seal for the Navajo Sandstone, we evaluated mesoscale variability in fracture density and morphology and variability in elastic moduli in the Jurassic Carmel Formation, a proposed seal to the underlying Navajo Sandstone for CO 2 geosequestration. By combining mechano-stratigraphic outcrop observations with elastic moduli derived from wireline log data, we characterize the variability in fracture pattern and morphology with the observed variability in rock strength within this heterolithic top seal. Outcrop inventories of discontinuities show fracture densities decrease as bed thickness increases and fracture propagation morphology across lithologic interfaces vary with changing interface type. Dynamic elastic moduli, calculated from wireline log data, show that Young’s modulus varies by up to 40 GPa across depositional interfaces, and by an average of 3 GPa across the reservoir/seal interface. We expect that the mesoscale changes in rock strength will affect the distributions of localized stress and thereby influence fracture propagation and fluid flow behavior within the seal. These data provide a means to closely tie outcrop observations to those derived from subsurface data and estimates of subsurface rock strength. We also studied damage zones associated normal faults in the Permian Cedar Mesa Sandstone, southeastern Utah. These faults are characterized by a single slip surfaces and damage zones containing deformation bands, veins, and joints. Field observations include crosscutting relationships, permeability increase, rock strength decrease, and ultraviolet light induced mineral fluorescence within the damage zone. These field observations combined with the interpreted paragenetic sequence from petrographic analysis, suggests a deformation history of reactivation and several mineralization events in an otherwise low-permeability fault. All deformation bands and veins fluoresce under ultraviolet light, suggesting connectivity and a shared mineralization history. Pre-­existing deformation features act as loci for younger deformation and mineralization events, this fault and its damage zone illustrate the importance of the fault damage zone to subsurface fluid flow. We model a simplified stress history in order to understand the importance of rock properties and magnitude of tectonic stress on the deformation features within the damage zone. The moderate confining pressures, possible variations in pore pressure, and the porous, fine-­grained nature of the Cedar Mesa Sandstone results in a fault damage zone characterized by enhanced permeability, subsurface fluid flow, and mineralization. Structural setting greatly influences fracture spacing and orientation. Three structural settings were examined and include fault proximity, a fold limb of constant dip, and a setting proximal to the syncline hinge. Fracture spacing and dominant fracture orientation vary at each setting and distinctions between regional and local paleo-stress directions can be made. Joints on the fold limb strike normal to the fold axis/bedding and are interpreted to be sub-parallel to the maximum regional paleo-stress direction as there is no fold related strain. Joints proximal to faults and the syncline hinge may have formed under local stress conditions associated with folding and faulting, and in some cases, are perpendicular to the maximum regional paleo-stress direction. Macroscale analyses of the Iron Wash fault, which cuts most of the stratigraphy of the reservoir- seal analogs, reveals that seal bypass occurs along faults with mixed lithologic composition. While portions of the fault may be effective seals, when modeled as a whole the fault zone cannot support a high CO 2 column. We adapted oil-field methods to calculate column heights and sealing capacities. Examination of the burial and tectonic histories of the rocks, coupled with field- and micro-­scale studies, allow us to examine the potential for seal failure using basic rock mechanics failure analyses. These studies reveal that the sealing lithologies experienced multiple episodes of deformation that resulted in numerous fractures and small faults. In a stress state based on burial history and overburden loads, we show that most of the seals could fail with normal to slightly excess pore pressures.

58 GEOSCIENCES↗
NASA NTRS1981

Prediction of jet exhaust noise on airframe surfaces during flight

The behavior of pressure fluctuations measured on the airframe of a prototype high lift jet transport (YC-14) is presented. The data are characterized in terms of a particular jet exhaust flow field idealization, jet mixing noise, and exhaust shock noise. Generalized spectrum shapes and scaling relations for peak level and frequency of peak level are developed, and the frequency is found to depend on jet exhaust velocity and aircraft velocity. Comparisons are made with near-field engine exhaust noise of a conventional jet, and results suggest that the same two exhaust noises are important for both aircraft types. Surface fluctuating pressure data are assessed, and results suggest that the jet mixing and exhaust shock noise source characterizations for the YC-14 have useful applicability to conventionally configured jets.

Butzel, L. M.↗
NASA NTRS2011

Deep Space Network-Wide Portal Development: Planning Service Pilot Project

The Deep Space Network (DSN) is an international network of antennas that supports interplanetary spacecraft missions and radio and radar astronomy observations for the exploration of the solar system and the universe. DSN provides the vital two-way communications link that guides and controls planetary explorers, and brings back the images and new scientific information they collect. In an attempt to streamline operations and improve overall services provided by the Deep Space Network a DSN-wide portal is under development. The project is one step in a larger effort to centralize the data collected from current missions including user input parameters for spacecraft to be tracked. This information will be placed into a principal repository where all operations related to the DSN are stored. Furthermore, providing statistical characterization of data volumes will help identify technically feasible tracking opportunities and more precise mission planning by providing upfront scheduling proposals. Business intelligence tools are to be incorporated in the output to deliver data visualization.

project development↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stability of aqueous neodymium complexes in carbonate-bearing solutions from 100–600 °C

Rare earth element exploration requires a quantitative understanding of factors governing their mobilization and economic concentration. However, the behavior of rare earth elements in carbonate- bearing hydrothermal fluids associated with carbonatite-hosted deposits is poorly understood, and conflicting mechanisms of rare earth transport by anionic ligands and alkali behavior have been described. Here, we report quantitative data to characterize the role of carbonate-bearing solutions in the hydrothermal mobilization of neodymium. Solubility studies of neodymium phosphate were performed at temperatures ranging from 100 to 600 °C in carbonate-bearing solutions. The thermodynamic data determined for the predominant complex were used to model the separation of neodymium from thorium in a simple flow-through system based on fluid and mineral compositions characteristic of carbonatite deposits. Our data suggest that neodymium transport is controlled by the stability of the carbonate species NdCO 3 OH o , and at temperatures of 500–600 °C, the concentrations of neodymium in solutions can reach ~1000 ppm.

58 GEOSCIENCES↗
NASA NTRS2014

The Global Albedo of the Moon at 1064 nm from LOLA

The Lunar Orbiter Laser Altimeter (LOLA) measures the backscattered energy of the returning altimetric laser pulse at its wavelength of 1064 nm, and these data are used to map the reflectivity of the Moon at zero-phase angle with a photometrically uniform data set. Global maps have been produced at 4 pixels per degree (about 8 kilometers at the equator) and 2 kilometers resolution within 20 deg latitude of each pole. The zero-phase geometry is insensitive to lunar topography, so these data enable characterization of subtle variations in lunar albedo, even at high latitudes where such measurements are not possible with the Sun as the illumination source. The geometric albedo of the Moon at 1064 nm was estimated from these data with absolute calibration derived from the Kaguya Multiband Imager and extrapolated to visual wavelengths. The LOLA estimates are within 2 sigma of historical measurements of geometric albedo. No consistent latitude-dependent variations in reflectance are observed, suggesting that solar wind does not dominate space weathering processes that modify lunar reflectance. The average normal albedo of the Moon is found to be much higher than that of Mercury consistent with prior measurements, but the normal albedo of the lunar maria is similar to that of Mercury suggesting a similar abundance of space weathering products. Regions within permanent shadow in the polar regions are found to be more reflective than polar surfaces that are sometimes illuminated. Limiting analysis to data with slopes less than 10 deg eliminates variations in reflectance due to mass wasting and shows a similar increased reflectivity within permanent polar shadow. Steep slopes within permanent shadow are also more reflective than similar slopes that experience at least some illumination. Water frost and a reduction in effectiveness of space weathering are offered as possible explanations for the increased reflectivity of permanent shadow; porosity is largely ruled out as the sole explanation. The south polar crater Shackleton is found to be among the most reflective craters in its size range globally but is not the most reflective, so mass wasting cannot be ruled out as a cause for the crater's anomalous reflectance. Models of the abundance of ice needed to account for the reflectance anomaly range from 3 to 14% by weight or area depending on assumptions regarding the effects of porosity on reflectance and whether ice is present as patches or is well mixed in the regolith. If differences in nanophase iron abundances are responsible for the anomaly, the permanently shadowed regions have between 50 and 80% the abundance of nanophase iron in mature lunar soil.

PSR↗
NASA NTRS2011

High Frequency Acoustic Response Characterization and Analysis of the Deep Throttling Common Extensible Cryogenic Engine

The Common Extensive Cryogenic Engine program demonstrated the operation of a deep throttling engine design. The program, spanning five years from August 2005 to July 2010, funded testing through four separate engine demonstration test series. Along with successful completion of multiple objectives, a discrete response of approximately 4000 Hz was discovered and explored throughout the program. The typical low-amplitude acoustic response was evident in the chamber measurement through almost every operating condition; however, at certain off-nominal operating conditions, the response became discrete with higher amplitude. This paper summarizes the data reduction, characterization, and analysis of the 4,000 Hz response for the entire program duration, using the large amount of data collected. Upon first encountering the response, new objectives and instrumentation were incorporated in future test series to specifically collect 4,000 Hz data. The 4,000 Hz response was identified as being related to the first tangential acoustic mode by means of frequency estimation and spatial decomposition. The latter approach showed that the effective node line of the mode was aligned with the manifold propellant inlets with standing waves and quasi-standing waves present at various times. Contour maps that contain instantaneous frequency and amplitude trackings of the response were generated as a significant improvement to historical manual approaches of data reduction presentation. Signal analysis and dynamic data reduction also uncovered several other features of the response including a stable limit cycle, the progressive engagement of subsequent harmonics, the U-shaped time history, an intermittent response near the test-based neutral stability region, other acoustic modes, and indications of modulation with a separate subsynchronous response. Although no engine damage related to the acoustic mode was noted, the peak-to-peak fluctuating pressure amplitude achieved 12.1% of the mean chamber pressure at its highest. The identification of this response in terms of an instability is also discussed.

Casiano, M. J.↗
DOE OSTI2015

Fact Sheet: Best Practices Handbook for the Collection and Use of Solar Energy Applications

This fact sheet presents highlights from the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, which provides detailed information about solar resource data and the resulting data products needed for planning each stage of large concentrating solar power systems, from initial site selection to system operations.

14 SOLAR ENERGY↗
NASA NTRS1984

Determination of rainfall and condensational heating in the South Pacific convergence zone during FGGE SOP-1

The role of cloud related diabatic processes in maintaining the structure of the South Pacific Convergence Zone is discussed. The method chosen to evaluate the condensational heating is a diagnostic cumulus mass flux technique which uses GOES digital IR data to characterize the cloud population. This method requires as input an estimate of time/area mean rainfall rate over the area in question. Since direct observation of rainfall in the South Pacific is not feasible, a technique using GOES IR data is being developed to estimate rainfall amounts for a 2.5 degree grid at 12h intervals.

Robertson, F. R.↗
DOE OSTI2021

Generalization Equations for Machine Learners Based on Physical and Abstract Laws

The physical and abstract laws derived from the first principles have been recently exploited to customize and sharpen machine learning (ML) methods and also derive their generalization equations. These laws often encapsulate knowledge that complements datasets and ML models. We present a generic framework that uses these laws to provide ML codes that are transferable across multiple areas, including data transport infrastructures and thermal hydraulics analytics of nuclear reactors. By anchoring on datasets from these areas and the statistical generalization theory, we present a rigorous approach to co-develop ML solutions and the generalization equations that characterize them, by exploiting the structure and constraints from the laws. We present illustrative examples using practical problems from existing literature on the performance characterization of data transport infrastructures, and the sensor error and power level estimation in nuclear reactor systems using sensor measurements of primary and secondary coolant systems, respectively.

Rao, Nageswara↗
DOE OSTI2021

Towards standardized nuclear reactors: Seismic isolation and the cost impact of the earthquake load case

We report nuclear energy has a key role to play in global decarbonization. Impediments to the widespread deployment of reactors are their projected high capital cost and levelized cost of energy, and time required to analyze, design, license, construct, and commission them. The earthquake load case is a key cost driver for a new build nuclear plant, because near-surface soils and seismic hazard are different at each site, requiring site-specific analysis, design, engineering, qualification, licensing, and regulatory review, essentially making every design First-of-a-Kind (FoaK). To enable deployment at the scale needed for deep decarbonization, the cost and time impact of the seismic load case must be significantly mitigated, and plants must be standardized. Seismic base isolation has been proven to considerably reduce the earthquake response of structures and equipment but has yet to be applied to a nuclear power plant in the United States, in part because the financial impacts, positive or negative, are not known. Because there are no recent non-proprietary data to characterize the influence of the seismic load case on capital cost, it is difficult to confidently quantify the financial benefits of seismic isolation. Scheme-level designs of two fundamentally different advanced reactor buildings were developed to assemble cost data on the influence of the seismic load case. Both buildings were equipped with three bespoke pieces of safety-related equipment and analyzed for incremented levels of earthquake shaking to quantify the seismic penalty on equipment, in terms of vessel weights and horizontal accelerations. Using analysis results, a questionnaire was developed and transmitted to nuclear utilities, reactor developers, engineers, and equipment suppliers to collect cost data on engineering and fabrication costs for these unique pieces of safety-class equipment. Synthesis of the cost data showed that the seismic load case significantly affects the capital cost (sum of engineering and fabrication cost) of safety-class equipment, with engineering costs being comparable to fabrication costs. Standardization of safety-class equipment is made possible by seismic isolation, that is, equipment designed for minimal seismic robustness can resist earthquake shaking at a site of much higher seismic hazard. The average reduction in the capital cost of the safety-related equipment, enabled by seismic isolation, is a factor of two for FoaK equipment and a factor of five for standardized equipment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗