Recurrent geomagnetic phenomena and solar centers <phenomenes geomagnetiques recurrents et centres solaires<
Recurrent geomagnetic storms effected by solar activity centers having emitted type IV radio bursts
SOLAR ACTIVITY↗
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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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Modeling net ecosystem exchange of CO2 with gated recurrent unit neural networks
Not provided.
Agriculture↗
Recurrence in truncated Boussinesq models for nonlinear waves in shallow water
The rapid spatial recurrence of weakly nonlinear and weakly dispersive progressive shallow-water waves is examined using a numerical integration technique on the discretized and truncated form of the Boussinesq equations. This study primarily examines recurrence in wave fields with Ursell number O(1) and characterizes the sensitivity of recurrence to initial spectral shape and number of allowed frequency modes. It is shown that the rapid spatial recurrence is not an inherent property of the considered Boussinesq systems for evolution distances of 10-50 wavelengths. The main result of the study is that highly truncated Boussinesq models of resonant shallow-water ocean surface gravity waves predict rapid multiple recurrence cycles, but that this is an artifact dependent on the number of allowed modes. For initial conditions consisting of essentially all energy concentrated in a single mode, damping of the recurrence cycles increases as the number of low-power background modes increases. When more than 32 modes are allowed, the recurrence behavior is relatively insensitive to the number of allowed modes.
Elgar, Steve↗
IUE observations of galactic and extragalactic recurrent novae
A collection of International Ultraviolet Explorer (IUE) light curves and sample spectra for the galactic recurrent novae observed in the ultraviolet are presented. These data are compared to that of the Nova LMC (Large Magellanic Cloud) 1990 number 2 which is shown to also be a recurrent novae. Based on the analysis of the outburst of N LMC 1990 number 2 it is suggested that all of the recurrent novae observed in the ultraviolet have luminosities at maximum light which exceed the Eddington limit. V745 Sco and RS Oph, the two recurrent novae with late type giant companions, show strong Mg II throughout the outburst. Those with low luminosity companions, U Sco and V394 CrA do not show Mg II but do show strong He II 1640 earlier in the outburst. Typical emission line velocities are greater than those observed in many of the classical novae. These characteristics suggest that the white dwarfs in recurrent novae systems are close to the Chandrasekhar limit.
Shore, Steven N.↗
Earthquake recurrence on the southern San Andreas modulated by fault-normal stress
Earthquake recurrence data from the Pallett Creek and Wrightwood paleoseismic sites on the San Andreas fault appear to show temporal variations in repeat interval. We investigate the interaction between strike-slip faults and auxiliary reverse and normal faults as a physical mechanism capable of producing such variations. Under the assumption that fault strength is a function of fault-normal stress (e.g. Byerlee's Law), failure of an auxiliary fault modifies the strength of the strike-slip fault, thereby modulating the recurrence interval for earthquakes. In our finite element model, auxiliary faults are driven by stress accumulation near restraining and releasing bends of a strike-slip fault. Earthquakes occur when fault strength is exceeded and are incorporated as a stress drop which is dependent on fault-normal stress. The model is driven by a velocity boundary condition over many earthquake cycles. Resulting synthetic strike-slip earthquake recurrence data display temporal variations similar to observed paleoseismic data within time windows surrounding auxiliary fault failures. Our simple model supports the idea that interaction between a strike-slip fault and auxiliary reverse or normal faults can modulate the recurrence interval of events on the strike-slip fault, possibly producing short term variations in earthquake recurrence interval.
Palmer, Randy↗
Flight Trajectory Prediction Based on Hybrid-Recurrent Networks
The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.
Nathan Schimpf↗
Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.
electric load forecasting↗
Response of soil nutrient pools and microbiomes to recurrent wildfire disturbance and varying burn severities in a mixed conifer forest
Wildfire is a pervasive disturbance in mixed-conifer forests, yet the relative influence of fire recurrence versus burn severity on soil biogeochemistry and microbial communities remains poorly quantified. We examined a natural gradient of fire history (0–3 prior fires) and burn severity (low–high) spanning 50 yr in a mixed-conifer ecosystem to assess how repeated fire shapes soil carbon (C) and nitrogen (N) pools, their isotopic signatures, mineral and particulate fractions, microbial community composition, carbon-use, CO₂ fluxes, and vegetation cover. Successive fires produced progressively higher bare-ground percentages and lower tree cover, which were tightly linked to declines in microbial diversity and reductions bulk %C, and %N. δ 13 C increased with fire frequency, indicating preferential loss of labile C through combustion or enhanced microbial oxidation, thereby explaining the observed net soil-C decline. Conversely, δ 15 N decreased and pH increased as tree density declined, reflecting altered N cycling and reduced acidification in post-fire soils. Fire recurrence, more than severity, corresponded with a marked shift in the bacterial community: for example, Xanthobacteraceae—key N-fixers and C-cyclers—diminished, while N-fixing Bacillaceae increased, underscoring the tightly coupled nature of soil nutrient dynamics and microbiome composition after repeated burns. Our results demonstrate that fire recurrence appears to be a stronger driver of post-fire soil ecosystem responses in this mixed-conifer forest, influencing both abiotic nutrient pools and the functional potential of the soil microbiome. These findings provide a more enhanced assessment and understanding to date of the biogeochemical consequences of repeated wildfire disturbance that can be used to inform management strategies aimed at preserving soil health in fire-prone landscapes.
54 ENVIRONMENTAL SCIENCES↗
Recurrent convolutional neural networks for modeling nonadiabatic dynamics of quantum-classical systems
Recurrent neural networks (RNNs) have recently been extensively applied to model the time evolution in fluid dynamics, weather predictions, and even chaotic systems due to their ability to capture temporal dependencies and sequential patterns in data. Here we present an RNN model based on convolutional neural networks for modeling the nonlinear nonadiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The Physics-Aware Recurrent Convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Here, we apply our RNN approach to learn the space-time evolution of a one-dimensional semiclassical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.
Holstein model↗
The recurrent nature of type 1 noise active regions during 1965 through 1969
The recurrent tendency of type I noise sources in metric frequencies is studied during the period from 1965 to 1969. It is shown that their recurrent period is slightly longer than 27.0 days and that the number of such recurrent trends for those noise sources is generally four. Discussion is given on the close relationship between those sources and the active regions where proton flares occur.
Sakurai, K.↗
Symbolic computation of recurrence equations for the Chebyshev series solution of linear ODE's
If a linear ordinary differential equation with polynomial coefficients is converted into integrated form then the formal substitution of a Chebyshev series leads to recurrence equations defining the Chebyshev coefficients of the solution function. An explicit formula is presented for the polynomial coefficients of the integrated form in terms of the polynomial coefficients of the differential form. The symmetries arising from multiplication and integration of Chebyshev polynomials are exploited in deriving a general recurrence equation from which can be derived all of the linear equations defining the Chebyshev coefficients. Procedures for deriving the general recurrence equation are specified in a precise algorithmic notation suitable for translation into any of the languages for symbolic computation. The method is algebraic and it can therefore be applied to differential equations containing indeterminates.
Geddes, K. O.↗
Gamma-ray burster recurrence time scales
In the decade since gamma-ray bursters (GRBs) were discovered by Klebesadel et al. (1973), many models have been proposed to explain the GRB phenomenon. A difficulty is related to the small number of predictions which would make it possible to evaluate the models. One verifiable prediction is the recurrence time scale, tau-gamma. One method to measure tau-gamma is to look for possible cases of recurrence as indicated by overlapping error boxes. The analysis considered in the present investigation is composed of three procedures. One of these involves a search through known error regions for cases where the error regions overlap. In addition, the number of overlaps expected by chance coincidence alone has been determined, and a calculation has been performed regarding the number of overlaps which are expected due to recurrence for various assumed tau-gamma and luminosity functions.
Schaefer, B. E.↗
The Nazca-South American convergence rate and the recurrence of the great 1960 Chilean earthquake
The seismic slip rate along the Chile Trench estimated from the slip in the great 1960 earthquake and the recurrence history of major earthquakes has been interpreted as consistent with the subduction rate of the Nazca plate beneath South America. The convergence rate, estimated from global relative plate motion models, depends significantly on closure of the Nazca - Antarctica - South America circuit. NUVEL-1, a new plate motion model which incorporates recently determined spreading rates on the Chile Rise, shows that the average convergence rate over the last three million years is slower than previously estimated. If this time-averaged convergence rate provides an appropriate upper bound for the seismic slip rate, either the characteristic Chilean subduction earthquake is smaller than the 1960 event, the average recurrence interval is greater than observed in the last 400 years, or both. These observations bear out the nonuniformity of plate motions on various time scales, the variability in characteristic subduction zone earthquake size, and the limitations of recurrence time estimates.
Stein, S.↗
Observations and simulations of recurrent novae: U Sco and V394 CrA
Observations and analysis of the Aug. 1987 outburst of the recurrent nova V394 CrA are presented. This nova is extremely fast and its outburst characteristics closely resemble those of the recurrent nova U Sco. Hydrodynamic simulations of the outbursts of recurrent novae were performed. Results as applied to the outbursts of V394 CrA and U Sco are summarized.
Starrfield, S.↗
Coalescence of recurrent streams of different sizes and amplitudes
Two corotating streams per solar rotation, separated by the heliospheric plasma sheet, were observed at 1 AU during 1974, and the streams recurred four times during the interval from day 145 to day 255. A single compound stream per solar rotation was observed at 5.5 - 6.0 AU during the corresponding interval from day 165 to day 275, indicating that the two recurrent streams observed at 1 AU coalesced between 1 AU and 6 AU. The average maximum speed of one of the recurrent streams was 805 km/s while that of the other recurrent stream was 705 km/s. The compound stream was not formed by the overtaking of the slow stream by the fast stream. Rather, it was probably formed by a process involving both filtering (due to the fact that the slow stream was 50 percent wider than the fast stream) and a geometrical effect.
Burlaga, L. F.↗
Parameter estimation in space systems using recurrent neural networks
The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
Parlos, Alexander G.↗
Core reactivity estimation in space reactors using recurrent dynamic networks
A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.
Parlos, Alexander G.↗
Recurrent novae
Recurrent novae seem to be a rather inhomogeneous group: T CrB is a binary with a M III companion; U Sco probably has a late dwarf as companion. Three are fast novae; two are slow novae. Some of them appear to have normal chemical composition; others may present He and CNO excess. Some present a mass-loss that is lower by two orders of magnitude than classical novae. However, our sample is too small for saying whether there are several classes of recurrent novae, which may be related to the various classes of classical novae, or whether the low mass-loss is a general property of the class or just a peculiarity of one member of the larger class of classical novae and recurrent novae.
Hack, Margherita↗