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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks

Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network's ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1991

Analog hardware for learning neural networks

This is a recurrent or feedforward analog neural network processor having a multi-level neuron array and a synaptic matrix for storing weighted analog values of synaptic connection strengths which is characterized by temporarily changing one connection strength at a time to determine its effect on system output relative to the desired target. That connection strength is then adjusted based on the effect, whereby the processor is taught the correct response to training examples connection by connection.

Eberhardt, Silvio P.↗
NASA NTRS1995

Recurrent structures of the interplanetary magnetic field observed by Ulysses

Since its launch in October 1990, Ulysses has provided good quality magnetic field data, practically covering the whole time interval until now. We have studied the very long time scale evolution of the interplanetary magnetic field, in particlular, we have search for recurrent disturbances in the magnetic field. The magnetic field vectors have been mapped back to the Sun along Parker spirals, in order to determine the Heliographic longitude of the source regions in the corona. It was found that the position of many high field sources drifts systematically relative to the corona assumed to rotate with the equatorial rotation period of the Sun. The results are compared to similar observations on the eastward drift of magnetic sectors observed after about June 1992. Changes associated with both the declining phase of the solar cycle and the latitudinal excursion of Ulysses are also discussed.

Erdos, G.↗
DOE OSTI2020

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

A multiprocessor computer simulation model employing a feedback scheduler/allocator for memory space and bandwidth matching and TMR processing

A computer simulation model for a multiprocessor computer is developed that is useful for studying the problem of matching multiprocessor's memory space, memory bandwidth and numbers and speeds of processors with aggregate job set characteristics. The model assumes an input work load of a set of recurrent jobs. The model includes a feedback scheduler/allocator which attempts to improve system performance through higher memory bandwidth utilization by matching individual job requirements for space and bandwidth with space availability and estimates of bandwidth availability at the times of memory allocation. The simulation model includes provisions for specifying precedence relations among the jobs in a job set, and provisions for specifying precedence execution of TMR (Triple Modular Redundant and SIMPLEX (non redundant) jobs.

Bradley, D. B.↗
DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2008

First Science with the Keck Interferometer Nuller High Spatial Resolution N-Band Observations of the Recurrent Nova RS Ophiuchi

We report observations of the nova RS 0phiuchi using the Keck Interferometer Nuller (KIN) taken approximately 3.8 days following the most recent outburst that occurred on 2006 February 12. The KIN operates in N-band from approx. 8 to 12.5 microns in a nulling mode - the sparse aperture equivalent of the conventional coronagraphic technique used in filled aperture te1escopes. In this mode the stellar light is suppressed by a destructive fringe, effectively enhancing the contrast of the circumstellar material located near the star. In a second, constructive-fringe mode, the instrument detects primarily the light from the central, bright source. These are the outer and inner spatial regimes, resprectively. We will describe the capabilities of the KIN, including these unique modes, and outline how they were key in our discovery that dust does not appear to be created in the outburst as in previous models, but instead was created between nova events. We also show how these first results from the KIN are consistent with Spitzer data. The KIN data show evidence of enhanced neutral atomic hydrogen emission and atomic metals including silicon located in the inner spatial regime near the white dwarf (WD) relative to the outer regime. There are also nebular emission lines and evidence of hot silicate dust in the outer spatial region, centered at approximately 17 AU from the WD, that are not found in the inner regime. The KIN and Spitzer data suggest that these emissions were excited by the nova flash in the outer spatial regime before the blast wave reached these regions. We describe the present results in terms of a new model for dust creation in recurrent novae that includes an increase in density in the plane of the orbit of the two stars created by a spiral shock wave caused by the motion of the stars through the cool wind of the red giant star. These data show the power and potential of the nulling technique which has been developed for the detection of Earth-like planets around nearby stars for the Terrestrial Planet Finder and Darwin missions.

Barry, Richard K.↗
DOE OSTI2020

Interpreting pre-vegetation landscape dynamics: The Cambrian Lower Mount Simon Sandstone, Illinois, U.S.A.

ABSTRACT The Cambrian Mount Simon Sandstone has been the subject of extensive study and multiple industrial-scale carbon storage demonstrations at Decatur, Illinois, USA. The development of a reliable paleoenvironmental model is critical to successful large-scale carbon dioxide (CO2) storage, but is complicated by the need to interpret pre-vegetation sedimentation processes. The present study presents a paleoenvironmental model of the Lower Mount Simon Sandstone, based on analysis of primary sedimentary structures in two cores and four complete high-resolution resistivity logs (FMI). The Lower Mount Simon Sandstone represents a vertical “drying-up” sequence composed of three associated depositional units: a north–south oriented coastal system at the base, an eastward-directed fluvial unit in the middle, and a westward-directed eolian system at the top that recycled medium- and fine-grained sand in the basin. Quantitative analysis of fluvial cross-strata indicates that the perennial river system was shallow (c. 1 m deep) with relatively narrow channel belts (c. 1 km). Adjacent sandy eolian-floodplain deposits contain abundant thin, crinkly planar laminae that are enriched in fines and are interpreted as cementation surfaces, likely of biological origin. Deflation lags and wind-ripple strata are commonly interbedded with the crinkly strata, suggesting that the recurrence of erosion and deposition that controlled sedimentary preservation on the floodplain were dominated by eolian transport, re-wetting, and (bio-) cementation. Such a prominent role of exposure to the wind, basin-scale sediment recycling, and eolian removal of fine-grained sediment would have ceased to exist for most climates after the development of vegetation on land, yet, may well be key to understanding the environmental context for early life on Earth.

Geology↗
NASA NTRS1972

Lava filled craters and the thermal history of the lunar surface.

Discussion of a hypothesis according to which two separate phases of lunar crust heating could be responsible for the formation of Mare Smythii and Tsiolkovsky crater, two lava-filled relatively recent lunar features which cannot be easily explained by other hypotheses. A small-crater count in eight flat floored larger craters is conducted in an attempt to support the two-phase heating hypothesis, on a theory that the distribution of the smaller craters by size may indicate a recurrence of lunar crust heating. Further counts are suggested for a more substantiated conclusion.

Beals, C. S.↗
DOE OSTI2022

Hybrid Recurrent Neural Network Modeling for Traffic Delay Prediction at Signalized Intersections Along an Urban Arterial

This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation. These ML models tend to be very complex and non-linear in nature, making it challenging to achieve fast inferences and are computationally heavy for real-time signal control implementation. As such, in this paper, a simple yet accurate hybrid modeling method is proposed to predict traffic delay one-step ahead with the model made suitable for real-time implementation to control traffic flow. Since real-time road-side measurements are recorded in unstructured form, the paper also discusses other issues related to data extraction and the pre-processing process. Finally, a simple signal control loop is developed to demonstrate the proposed modeling approach, which has shown advantages in model accuracy and computation efficiency compared against several existing modeling methods.

42 ENGINEERING↗
NASA NTRS1982

A model for the 1979 outburst of the recurrent nova U Scorpii

A series of ultraviolet spectra were obtained of the recurrent nova U Sco during its recent outburst with the IUE satellite. These spectra were analyzed and found to consist primarily of emission lines although broad resonance absorption is present during the first week. These data, in combination with the optical data, show that the nova ejecta is very depleted in hydrogen relative to helium and is rich in nitrogen. An optical spectrum, obtained nine months after the outburst, shows predominantly He II emission lines, indicating that the gas being transferred from the secondary is very helium rich and that the secondary is highly evolved. These data are interpreted to imply that the outburst is associated with the accretion of helium rich material by a massive white dwarf in a close binary system. Neither the observational data nor the theoretical calculations, permit differentiation between a thermonuclear runway or an accretion event as the cause of the outburst. In both cases, an equal amount of material is ejected at equal speeds.

Starrfield, S.↗
DOE OSTI2021

CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia

Abstract The ability to deduce carcinogenic potential from subchronic, repeat dose rodent studies would constitute a major advance in chemical safety assessment and drug development. This study investigated an error-corrected NGS method (CarcSeq) for quantifying cancer driver mutations (CDMs) and deriving a metric of clonal expansion predictive of future neoplastic potential. CarcSeq was designed to interrogate subsets of amplicons encompassing hotspot CDMs applicable to a variety of cancers. Previously, normal human breast DNA was analyzed by CarcSeq and metrics based on mammary-specific CDMs were correlated with tissue donor age, a surrogate of breast cancer risk. Here we report development of parallel methodologies for rat. The utility of the rat CarcSeq method for predicting neoplastic potential was investigated by analyzing mammary tissue of 16-week-old untreated rats with known differences in spontaneous mammary neoplasia (Fischer 344, Wistar Han, and Sprague Dawley). Hundreds of mutants with mutant fractions ≥ 10−4 were quantified in each strain, most were recurrent mutations, and 42.5% of the nonsynonymous mutations have human homologs. Mutants in the mammary-specific target of the most tumor-sensitive strain (Sprague Dawley) showed the greatest nonsynonymous/synonymous mutation ratio, indicative of positive selection consistent with clonal expansion. For the mammary-specific target (Hras, Pik3ca, and Tp53 amplicons), median absolute deviation correlated with percentages of rats that develop spontaneous mammary neoplasia at 104 weeks (Pearson r = 1.0000, 1-tailed p = .0010). Therefore, this study produced evidence CarcSeq analysis of spontaneously occurring CDMs can be used to derive an early metric of clonal expansion relatable to long-term neoplastic outcome.

McKim, Karen L.↗
NASA NTRS1975

Improving estimates of streamflow characteristics using LANDSAT-1 (ERTS-1) imagery

The author has identified the following significant results. Streamflow characteristics in the Delmarva Peninsula derived from the records of daily discharge of 20 gaged basins are representative of the full range in flow conditions and include all of those commonly used for design or planning purposes. They include annual flood peaks with recurrence intervals of 2, 5, 10, 25, and 50 years, mean annual discharge, standard deviation of the mean annual discharge, mean monthly discharges, standard deviation of the mean monthly discharges, low-flow characteristics, flood volume characteristics, and the discharge equalled or exceeded 50 percent of the time. Streamflow and basin characteristics were related by a technique of multiple regression using a digital computer. A control group of equations was computed using basin characteristics derived from maps and climatological records. An experimental group of equations was computed using basin characteristics derived from LANDSAT imagery as well as from maps and climatological records. Based on a reduction in standard error of estimate equal to or greater than 10 percent, the equations for 12 stream flow characteristics were substantially improved by adding to the analyses basin characteristics derived from LANDSAT imagery.

Hollyday, E. F.↗
NASA NTRS1978

Vortices in magnetospheric plasma flow

Vortical motion in the early morning sector of the plasma sheet was detected by means of two-dimensional and three-dimensional plasma measurements obtained by the LASL/MPI analyzers on the ISEE 1 and 2 satellites. The vortices, when present, are manifested as a recurrent or continuing rotation of the bulk flow vector in a plane sometimes moderately inclined with respect to the ecliptic plane. The preferred sense of the rotation is clockwise when viewed from above the ecliptic plane. The vortex rotation period ranges from 5 to 20 min., and several rotations can occur in a relatively uninterrupted sequence. The plasma vortices are estimated to be several earth radii in size. The possibility of a 'vortex street' convecting earthward along the axis of the magnetotail is considered.

Hones, E. W., Jr.↗
NASA NTRS2006

Black Hole Spectral States and Physical Connections

The dramatic changes seen in the X-ray spectral and timing properties of accreting black hole candidates (BHCs) provide important clues about the accretion and jet formation processes that occur in these systems. Dividing the different source behaviors into spectral states provides a framework for studying BHCs. To date, there have been three main classification schemes with Luminosity-based, Component-based, or Transition-based criteria. The canonical, Luminosity-based criteria and physical models that are based on this concept do not provide clear explanations for several phenomena, including hysteresis of spectral states and the presence of jets. I discuss the re-definitions of states, focusing on an application of the Component-based states to more than 400 RXTE observations of the recurrent BHC 4U 1630^17. We compare the X-ray properties for the recent 2002-2004 outburst to those of an earlier (1998) outburst, during which radio jets were observed. The results suggest a connection between hysteresis of states and major jet ejections, and it is possible that both of these are related to the evolution of the inner radius of the optically thick accretion disk.

Tomsick, John A.↗
NASA NTRS2005

Black Hole Spectral States and Physical Connections

The dramatic changes seen in the X-ray spectral and timing properties of accreting black hole candidates (BHCs) provide important clues about the accretion and jet formation processes that occur in these systems. Dividing the different source behaviors into spectral states provides a framework for studying BHCs. To date, there have been three main classification schemes with Luminosity-based, Component-based, or Transition-based criteria. The canonical, Luminosity-based criteria and physical models that are based on this concept do not provide clear explanations for several phenomena, including hysteresis of spectral states and the presence of jets. I discuss the re-definitions of states, focusing on an application of the Component-based states to more than 400 RXTE observations of the recurrent BHC 4U 1630-47. We compare the X-ray properties for the recent 2002-2004 outburst to those of an earlier (1998) outburst, during which radio jets were observed. The results suggest a connection between hysteresis of states and major jet ejections, and it is possible that both of these are related to the evolution of the inner radius of the optically thick accretion disk.

Tomsick, John A.↗
NASA NTRS2010

An Examination of the Hadley Sea-Surface Temperature Time Series for the Nino 3.4 Region

The Hadley sea-surface temperature (HadSST) dataset is investigated for the interval 1871-2008. The purpose of this investigation is to determine the degree of success in identifying and characterizing El Nino (EN) southern (ENSO) extreme events, both EN and La Nina (LN) events. Comparisons are made against both the Southern Oscillation Index for the same time interval and with published values of the Oceanic Nino Index for the interval since 1950. Some 60 ENSO extreme events are identified in the HadSST dataset, consisting of 33 EN and 27 LN events. Also, preferential associations are found to exist between the duration of ENSO extreme events and their maximum anomalous excursion temperatures and between the recurrence rate for an EN event and the duration of the last known EN event. Because the present ongoing EN is a strong event, it should persist 11 months or longer, inferring that the next EN event should not be expected until June 2012 or later. Furthermore, the decadal sum of EN-related months is found to have increased somewhat steadily since the decade of 1920-1929, suggesting that the present decade (2010-2019) possibly will see about 3-4 EN events, totaling about 37 +/- 3 EN-related months (i.e., months that meet the definition for the occurrence of an EN event).

Wilson, Robert M.↗