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NASA NTRS1989

Recurrent solar wind structures in the outer heliosphere

The paper presents recent work on evolution of recurrent solar wind structures in the outer heliosphere. Corotating shocks, corotating interaction regions, and merged interaction regions are studied, and an MHD simulation model in which the jump conditions at all shock crossings satisfy the Rankine-Hugoniot solution is examined. Simulation results which describe the evolution of idealized recurrent solar wind structures between 14 AU and the termination shock are reported which show that merged interaction regions belonging to two to four consecutive solar rotations coalesce with each other between 30 AU and the termination shock to reshape the heliospheric structures. In the outer heliosphere, forward and reverse interplanetary shocks which continuously heat the solar wind plasma are investigated.

Whang, Y. C.↗

NASA NTRS1990

Nova LMC 1990 no. 2: The first extragalactic recurrent nova

International Ultraviolet Explorer (IUE) observations of Nova LMC (Large Magellanic Cloud) 1990 number 2 are presented. It is the first recurrent nova to be observed outside the Galaxy. Earlier optical outburst of the nova was observed in 1968, when it reached a maximum of about V = 10.2. The first observations caught the nova at UV maximum, and IUE spectra were obtained between 16 Feb. and 26 Mar. 1990, with 8 hour resolution during the first day of observations. No P Cyg profiles developed on the resonance lines, suggesting that very little mass was ejected and that the outburst was not spherical. The integrated UV luminosity was then 3.4 x 10(exp 38) erg/s which is above the Eddington limit for a one solar mass white dwarf (assuming E (B - V) = 0.15 and a distance of 55 kpc to the LMC). This is the first time that the luminosity of a recurrent nova has been directly obtained.

Shore, Steven N.↗

NASA NTRS1990

Simulation of period doubling of recurrent solar wind structures

Based on satellite observations of a recurrent solar wind structure conducted in 1974, an MHD simulation model, and input functions generated from plasma and magnetic field data, the continuing evolution of the solar wind structure outside 5 AU is studied. The model uses the Rankine-Hugoniot relations to describe the jumps in flow properties across the shocks, and it treats shocks as surfaces of discontinuity with zero thickness. Two interaction processes (the collision and the merging of shocks) play important roles in restructuring the solar wind in the outer heliosphere. The simulation result shows that period doubling occurs between 5 and 10 AU. The recurrent solar wind appears to be a persistent new structure between 10 and 20 AU, and it consists of one merged interaction region per solar rotation.

Whang, Y. C.↗

NASA NTRS1992

An implementation of the QMR method based on coupled two-term recurrences

The authors have proposed a new Krylov subspace iteration, the quasi-minimal residual algorithm (QMR), for solving non-Hermitian linear systems. In the original implementation of the QMR method, the Lanczos process with look-ahead is used to generate basis vectors for the underlying Krylov subspaces. In the Lanczos algorithm, these basis vectors are computed by means of three-term recurrences. It has been observed that, in finite precision arithmetic, vector iterations based on three-term recursions are usually less robust than mathematically equivalent coupled two-term vector recurrences. This paper presents a look-ahead algorithm that constructs the Lanczos basis vectors by means of coupled two-term recursions. Implementation details are given, and the look-ahead strategy is described. A new implementation of the QMR method, based on this coupled two-term algorithm, is described. A simplified version of the QMR algorithm without look-ahead is also presented, and the special case of QMR for complex symmetric linear systems is considered. Results of numerical experiments comparing the original and the new implementations of the QMR method are reported.

Freund, Roland W.↗

NASA NTRS1994

Substorm recurrence during steady and variable solar wind driving: Evidence for a normal mode in the unloading dynamics of the magnetosphere

Farrugia et al (1993) have recently studied substorm activity driven by the passage of an interplanetary magnetic cloud during which the interplanetary magnetic field turned southward for approximately 18 hours. It was shown that both the epsilon and the VB(sub s) parameters varied slowly on the timescale of a substorm but changed considerably over the interval as a whole. The substorm occurrence rate did not reflect the variation in magnetospheric energy loading rate as measured by these parameters but, rather, remained roughly constant with a 50-min average period. Klimas et al. (1992) showed that the Faraday loop analog model of geomagnetic activity predicts this single unloading rate under various constant loading rates. However, various model parameters were adjusted to yield a 1-hour unloading period in agreement with the Bargatze et al. (1985) linear prediction filters and in approximate agreement with the Farrugia et al. (1993) results. It has since been found necessary to add a slow relaxation mechanism to the Faraday loop model to allow for its approach to a ground state during long periods of inactivity. It is proposed that the relaxation mechanism is provided by slow convection of magnetic flux out of the magnetotail to the dayside magnetosphere. In addition, a rudimentary representation of magnetotail-ionosphere coupling has been added to enable comparison of model output to measured AL. The present study is of the modified Faraday loop model response to solar wind input from the Bargatze et al. data set with comparison of its output to concurrent AL. This study has removed the degree of freedom in parameter choice that had earlier allowed adjustments toward the 1-hour unloading period and has, instead, yielded the 1-hour unloading period under various constant loading rates. It is demonstrated that the second peak of the bimodal Bargatze et al. linear prediction filters at approximately equal 1-hour lag and the approximately constant substorm recurrence rate observed by Farrugia et al. can be interpreted as both being due to the existence of a normal unloading recurrence period in the dynamics of the magnetosphere.

Klimas, A. J.↗

NASA NTRS1995

RX J0045.4+4154: A recurrent supersoft x-ray transient in M31

Using data extracted from the ROSAT archive we have discovered a recurrent supersoft X-ray transient RX J0045.4+4154 in M31. The first outburst began on 1992 February 2 and continued for at least 4 days, until the end of the observation sequence. A second outburst that lasted more than 6 days was seen to begin on 1993 January 7. The X-ray spectrum on both occasions yields a characteristic blackbody temperature of approximately 90 eV. For a range of plausible continuum models, the hydrogen column density is (0.8-1.5) x 10(exp 21)/sq cm and is consistent with the source being located in M31. This implies an unabsorbed 0.1-2.0 keV peak luminosity of approximately 10(exp 38) ergs/sec. This is the first recurrent X-ray transient to be found in M31 and is particularly notable because it is much softer than the bright X-ray transients seen in our Galaxy. The spectrum is characteristic of the supersoft class of X-ray sources, which are thought to be accreting white dwarfs that have a hydrogen-burning surface layer. A fit to a white dwarf model atmosphere gives a temperature of 10(exp 6)K, the hottest found so far. This high temperature is consistent with a white dwarf mass of 1.3-1.4 solar mass, approaching the Chandrasekhar limit, and burning close to the nuclear stability limit.

White, N. E.↗

NASA NTRS2004

African Equatorial and Subtropical Ozone Plumes: Recurrences Timescales of the Brown Cloud Trans-African Plumes and Other Plumes

We have found repeated illustrations in the maps of Total Tropospheric Ozone (TTO) of apparent transport of ozone from the Indian Ocean to the Equatorial Atlantic Ocean. Most interesting are examples that coincide with the INDOEX observations of late northern winter, 1999. Three soundings associated with the SHADOZ (Southern Hemisphere Additional Ozonesondes) network help confirm and quantify degree of influence of pollution, lightning, and stratospheric sources, suggesting that perhaps 40% of increased Atlantic ozone could be Asian pollution during periods of maximum identified in the TTO maps. We outline recurrent periods of apparent ozone transport from Indian to Atlantic Ocean regions both during and outside the late-winter period. These are placed in the context of some general observations about factors controlling recurrence timescales for the expression of both equatorial and subtropical plumes. Low-level subtropical plumes are often controlled by frontal systems approaching the Namib coast; these direct mid-level air into either easterly equatorial plumes or westerly mid- troposphere plumes. Equatorial plumes of ozone cross Africa on an easterly path due to the occasional coincidence of two phenomena: (1) lofting of ozone to mid and upper levels, often in the Western Indian Ocean, and (2) the eastward extension of an Equatorial African easterly jet.

Chatfield, Robert B.↗

NASA NTRS2017

Thermonuclear Bursts with Short Recurrence Times from Neutron Stars Explained by Opacity-Driven Convection

Thermonuclear flashes of hydrogen and helium accreted onto neutron stars produce the frequently observed Type I X-ray bursts. It is the current paradigm that almost all material burns in a burst, after which it takes hours to accumulate fresh fuel for the next burst. In rare cases, however, bursts are observed with recurrence times as short as minutes. We present the first one-dimensional multi-zone simulations that reproduce this phenomenon. Bursts that ignite in a relatively hot neutron star envelope leave a substantial fraction of the fuel unburned at shallow depths. In the wake of the burst, convective mixing events driven by opacity bring this fuel down to the ignition depth on the observed timescale of minutes. There, unburned hydrogen mixes with the metal-rich ashes, igniting to produce a subsequent burst. We find burst pairs and triplets, similar to the observed instances. Our simulations reproduce the observed fraction of bursts with short waiting times of approximately 30%, and demonstrate that short recurrence time bursts are typically less bright and of shorter duration.

nucleosynthesis↗

NASA NTRS1993

Discrete Recurrent Neural Networks for Grammatical Inference

Recurrent neural networks have recently been shown to have the ability to learn regular and context-free grammars from examples. We show that while conventional analog recurrent networks try to form clusters in activation space to represent discrete states of the grammars during learning, and can be successful in doing so, the clusters so formed tend to become unstable as longer and longer test input strings are presented to the network.

Neural↗

NASA NTRS2019

Forbush Decreases and < 2 Day GCR Flux Non-recurrent Variations Studied with LISA Pathfinder

Non-recurrent short-term variations of the galactic cosmic-ray (GCR) flux above 70 MeV n−1 were observed between 2016 February 18 and 2017 July 3 on board the European Space Agency LISA Pathfinder (LPF) mission orbiting around the Lagrange point L1 at 1.5 × 106 km from Earth. The energy dependence of three Forbush decreases is studied and reported here. A comparison of these observations with others carried out in space down to the energy of a few tens of MeV n−1 shows that the same GCR flux parameterization applies to events of different intensity during the main phase. FD observations in L1 with LPF and geomagnetic storm occurrence are also presented. Finally, the characteristics of GCR flux non-recurrent variations (peaks and depressions) of duration <2 days and their association with interplanetary structures are investigated. It is found that, most likely, plasma compression regions between subsequent corotating high-speed streams cause peaks, while heliospheric current sheet crossing causes the majority of the depressions.

M. Armano↗

NASA NTRS2019

Forbush Decreases and <2 Day GCR Flux Non-Recurrent Variations Studied with LISA Pathfinder

Non-recurrent short-term variations of the galactic cosmic-ray (GCR) flux above 70 MeV n(exp −1) were observed between 2016 February 18 and 2017 July 3 on board the European Space Agency LISA Pathfinder (LPF) mission orbiting around the Lagrange point L1 at 1.5 × 10 (exp 6) km from Earth. The energy dependence of three Forbush decreases is studied and reported here. A comparison of these observations with others carried out in space down to the energy of a few tens of MeV n(exp −1) shows that the same GCR flux parameterization applies to events of different intensity during the main phase. FD observations in L1 with LPF and geomagnetic storm occurrence are also presented. Finally, the characteristics of GCR flux non-recurrent variations (peaks and depressions) of duration <2 days and their association with interplanetary structures are investigated. It is found that, most likely, plasma compression regions between subsequent corotating high-speed streams cause peaks, while heliospheric current sheet crossing causes the majority of the depressions.

Cosmic rays↗

NASA NTRS2023

Recurrent 3he-Rich Solar Energetic Particle Injections Observed By Solar Orbiter at ∼0.5 Au

We report Solar Orbiter observations of six recurrent solar energetic particle injections in 2022 March 3−6 at ∼0.5 au. All but one were associated with jets emanating from a plage near a large sunspot in active region 12 957. We saw large jets in injections with high 3He and Fe enrichments and minor jets in injections with no or lower enrichments. Furthermore, the event with the highest enrichment showed a more compact configuration of the underlying photospheric magnetic field. The higher fluences as well as harder spectra were seen in the event with a simultaneous jet and wider eruption. However, in this case, the energy buildup time in the source might be required to produce such spectra. Extreme-ultraviolet images from Solar Orbiter revealed a number of intersecting loops at the base of jets not seen from 1 au that might be a precondition for the recurrent events.

Sun: particle emission↗

DOE OSTI2025

Hydrodynamic Predictions for the Next Outburst of T Coronae Borealis: It Will Be the Brightest Classical or Recurrent Nova Ever Observed in X-Rays*

T Coronae Borealis (TCrB) is a recurrent nova with recorded outbursts in 1866 and 1946 and possible outbursts in 1217 and 1787. It is predicted to explode again in 2025 or 2026 based on multiple observational studies. The system consists of a massive ( M wd ≳ 1.35 M ⊙ ) white dwarf (WD) and a red giant (M3–M4 III). We have performed 1D hydrodynamic simulations with NOVA to predict the behavior of the next outburst. These simulations consist of a range of mass accretion rates onto ∼1.35 M ⊙ WDs, designed to bound the conditions necessary to achieve ignition of an explosion after an ≈80 yr interoutburst period. We have used both carbon–oxygen and oxygen–neon initial compositions, in order to include the possible ejecta abundances to be measured in the observations of the next outburst. As the WD in the TCrB system is observed to be massive, theoretical predictions reported here imply that the WD is growing in mass as a consequence of the thermonuclear runaway. Therefore, the secular evolution of the WD may allow it to approach the Chandrasekhar limit and either explode as a Type Ia supernova or undergo accretion-induced collapse, depending on its underlying composition. We have followed the evolution of just the WD, after removing the ejected matter from the surface layers. Our intent is to illuminate the mystery of the unique, second maximum in the two well-observed outbursts and we have found conditions that bracket the predictions.

Cataclysmic variable stars↗

DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

42 ENGINEERING↗

DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗

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↗