Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “recurrence”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Importance of mitral valve repair associated with left ventricular reconstruction for patients with ischemic cardiomyopathy: a real-time three-dimensional echocardiographic study

BACKGROUND: Left ventricular (LV) reconstruction surgery leads to early improvement in LV function in ischemic cardiomyopathy (ICM) patients. This study was designed to evaluate the impact of mitral valve (MV) repair associated with LV reconstruction on LV function 1-year after surgery in ICM patients assessed by real-time 3-dimensional echocardiography (3DE). METHODS AND RESULTS: Sixty ICM patients who underwent the combination surgery (LV reconstruction in 60, MV repair in 30, and revascularization in 52 patients) were studied. Real-time 3DE was performed and LV volumes were obtained at baseline, discharge, 6-month and >or=12-month follow-up. Reduction in end-diastolic volumes (EDV) by 29% and in end-systolic volumes by 38% were demonstrated immediately after surgery and remained at subsequent follow-up (P<0.0001). The LV ejection fraction significantly increased by about 10% at discharge and was maintained >or=12-month (P<0.0001). Although the LV volumes were significantly larger in patients with MV repair before surgery (EDV, 235+/-87 mL versus 193+/-67 mL, P<0.05), they were similar to LV volumes of the patients without MV repair at subsequent follow-ups. However, the EDV increased from 139+/-24 mL to 227+/-79 mL (P<0.01) in 7 patients with recurrent mitral regurgitation (MR). Improvement in New York Heart Association functional class occurred in 81% patients during late follow-up. CONCLUSIONS: Real-time 3DE demonstrates that LV reconstruction provides significant reduction in LV volumes and improvement in LV function which is sustained throughout the 1-year follow-up with 84% cardiac event free survival. If successful, MV repair may prevent LV redilation, while recurrent MR is associated with increased LV volumes.

Clinical Trial↗
NASA NTRS2005

Outbursts in Symbiotic Binaries

A major question for symbiotic stars concerns the nature and cause of their outbursts. A small subset of symbiotics, the slow novae are fairly well established as thermonuclear events that last on the order of decades. The several symbiotic recurrent novae, which are much shorter and last on the order of months, are also thought to be thermonuclear runaways. Yet the majority of symbiotics are neither slow novae nor recurrent novae. These are the so-called classical symbiotics, many of which show outbursts whose cause is not well understood. In some cases, jets are produced in association with an outburst, therefore an investigation into the causes of outbursts will yield important insights into the production of collimated outflows. To investigate the cause and nature of classical symbiotic outbursts, we initiated a program of multiwavelength observations of these events. In FUSE Cycle 2, we obtained six observational epochs of the 2000-2002 classic symbiotic outburst in the first target of our campaign - class prototype, Z Andromedae. That program was part of a coordinated multi-wavelength Target-of-Opportunity (TOO) campaign with FUSE, XMM, Chandra, MERLIN, the VLA, and ground-based spectroscopic and high time-resolution photometric observations. Our campaign proved the concept, utility, and need for coordinated multi-wavelength observations in order to make progress in understanding the nature of the outburst mechanisms in symbiotic stars. Indeed, the FUSE data were the cornerstone of this project

Sonneborn, George↗
NASA NTRS2005

Outbursts in Symbiotic Binaries: Z and Continued Observation

A major question for symbiotic stars concerns the nature and cause of their outbursts. A small subset of symbiotics, the "slow novae" are fairly well established as thermonuclear events that last on the order of decades. The several symbiotic "recurrent novae", which are much shorter and last on the order of months, are also thought to be thermonuclear runaways. Yet the majority of symbiotics are neither slow novae nor recurrent novae. These are the so-called "classical symbiotics," many of which show outbursts whose cause is not well understood. In some cases, jets are produced in association with an outburst, therefore an investigation into the causes of outbursts will yield important insights into the production of collimated outflows. To investigate the cause and nature of classical symbiotic outbursts, we initiated a program of multi- wavelength observations of these events. First of all in FUSE Cycle 2, we obtained six observational epochs of the 2000-2002 classic symbiotic outburst in the first target of our campaign - class prototype, Z Andromedae. That program was part of a coordinated multi-wavelength Target-of-Opportunity (TOO) campaign with FUSE, XMM, Chandra, MERLIN, the VLA, and ground-based spectroscopic and high time-resolution photometric observations. Our campaign proved the concept, utility, and need for coordinated multi-wavelength observations in order to make progress in understanding the nature of the outburst mechanisms in symbiotic stars. Indeed, the FUSE data were the cornerstone of this project. The present program is a continuation of that cycle 2 effort. Indeed, the observations acquired in this program are vital to the proper interpretation of the material acquired in cycle 2 as the new data cover the critical time period when the star continues to decline from outburst and actually returns to quiescence. The utilization of these data have allowed us to refine and complete description of our new model for classical symbiotic system outbursts.

Sonneborn, George↗
NASA NTRS2004

Cluster observations of quasi-periodic impulsive signatures in the dayside northern lobe: High-latitude flux transfer events?

We report on a series of quasi-periodic reversals in GSM B(sub Z) observed by the four Cluster spacecraft in the northern dayside lobe poleward of the cusp on 23 February 2001. During an interval of about 35 min, multiple reversals (negative to positive) in B(sub Z) of approximately 1-min duration with an approximate 8-min recurrence time were observed. The individual structures do not resemble low-latitude flux transfer events (FTE) [Russell and Elphic, 1979] but the 8-min recurrence frequency suggests that intermittent reconnection may be occurring .Measurements (appropriately lagged) of the solar wind at ACE show that the IMF was southward-oriented with a strong B(sub X) and that a modest dynamic pressure increased as the events started. The multi-point observations afforded by the Cluster spacecraft were used to infer the motion (direction and speed) of the observed magnetic field reversals. The associated currents were also calculated and they are consistent with the spatial confinement of the observed magnetic field reversals. We propose that the observed reversals are due to flux tubes reconnecting with closed field lines on the dayside. Ancillary data from the Cluster Ion Spectrometry (CIS) and Plasma Electron And Current Experiment (PEACE) instruments were used to develop a physical picture of the reversals.

Thompson, S. M.↗
NASA NTRS2007

IMP 8 GME Particle Observations Over Three Solar Cycles

The Goddard Medium Energy experiment on the IMP 8 spacecraft has made nearly continuous observations of the near-Earth energetic particle environment from its launch in October, 1973 until near present. We summarize several aspects of these observations, including solar energetic particle events, CIR-associated events, and cosmic ray modulations. In particular, we note that, as expected fiom the pattern of smaller recurrent (27 day) cosmic ray modulations seen in the mid 1980's A less than 0 solar minimum compared to the previous and following (A greater than 0) minima, recurrent modulations are again reduced in the current solar minimum.

Richardson, Ian↗
NASA NTRS2008

Spatial and Alignment Analyses for a field of Small Volcanic Vents South of Pavonis Mons Mars

The Tharsis province of Mars displays a variety of small volcanic vent (10s krn in diameter) morphologies. These features were identified in Mariner and Viking images [1-4], and Mars Orbiter Laser Altimeter (MOLA) data show them to be more abundant than originally observed [5,6]. Recent studies are classifying their diverse morphologies [7-9]. Building on this work, we are mapping the location of small volcanic vents (small-vents) in the Tharsis province using MOLA, Thermal Emission Imaging System, and High Resolution Stereo Camera data [10]. Here we report on a preliminary study of the spatial and alignment relationships between small-vents south of Pavonis Mons, as determined by nearest neighbor and two-point azimuth statistical analyses. Terrestrial monogenetic volcanic fields display four fundamental characteristics: 1) recurrence rates of eruptions,2 ) vent abundance, 3) vent distribution, and 4) tectonic relationships [11]. While understanding recurrence rates typically requires field measurements, insight into vent abundance, distribution, and tectonic relationships can be established by mapping of remotely sensed data, and subsequent application of spatial statistical studies [11,12], the goal of which is to link the distribution of vents to causal processes.

Bleacher, J. E.↗
NASA NTRS2011

Magnetotail Reconnection and Flux Circulation: Jupiter and Saturn Compared

The Jovian magnetosphere has been visited by eight spacecraft, and the magnetometer data have been used to identify dozens of plasmoids and ~250 field dipolarizations associated with magnetic reconnection in the tail [e.g. Vogt et al., 2010]. Since the arrival of the Cassini spacecraft at Saturn in 2004, the magnetometer instrument has also been used to identify reconnection signatures. The deepest magnetotail orbits were in 2006, and during this time 34 signatures of plasmoids were identified. In this study we compare the statistical properties of plasmoids at Jupiter and Saturn such as duration, size, location, and recurrence period. Such parameters can be influenced by many factors, including the different Dungey cycle timescales and cross-magnetospheric potential drops at the two planets. We present superposed epoch analyses of plasmoids at the two planets to determine their average properties and to infer their role in the reconfiguration of the nightside of the magnetosphere. We examine the contributions of plasmoids to the magnetic flux transfer cycle at both planets. At Jupiter, there is evidence of an extended interval after reconnection where the field remains northward (analogous to the terrestrial post-plasmoid plasma sheet). At Saturn we see a similar feature, and calculate the amount of flux closed on average in reconnection events, leading us to an estimation of the recurrence rate of plasmoid release.

Jackman, C. M.↗
NASA NTRS2011

Cassini Observations of Plasmoid Structure and Dynamics: Implications for the Role of Magnetic Reconnection in Magnetospheric Circulation at Saturn

We survey the Cassini magnetometer data during the deep tail orbits in 2006, and find 34 direct encounters with plasmoids. They occur as single, isolated events but also in groups of two or more plasmoids as is frequently observed at Earth . We show a case study example of three such plasmoids over three hours, where we estimate an upper limit of 5.68 GWb of flux closure, and derive a reconnection rate over this interval of 526 kV. We show the results of a superposed epoch analysis of al1 34 plasmoids indicating that, on average, plasmoids at Saturn are approix.8 min in duration and they tend toward a loop-like, as opposed to flux rope-like topology, with little or no axial core magnetic field. Our analysis shows that plasmoids at Saturn are followed by an extended interval of the post-plasmoid plasma sheet (PPPS) lasting approx.58 min. The average open magnetic flux disconnected by the continued reconnet:tion following plasmoid formation that creates the PPPS is approx.3 GWb. We calculate expected recurrence rates for plasmoids, and compare these with a derived observational recurrence rate of one plasmoid every approx.2.4 days, explaining the reasons why the spacecraft has not observed as many plasmoids as we predict will be released. We conclude that the Cassini magnetometer measurements require a combination of Vasyliunas-type closed-flux plasma sheet and Dungey-type open-flux lobe reconnection to account for the observed properties of the plasmoids and PPPS in Saturn's magnetotail.

Jackman, C. M.↗
NASA NTRS2015

Constraining the Physics of AM Canum Venaticorum Systems with the Accretion Disk Instability Model

Recent work by Levitan et al. has expanded the long-term photometric database for AM CVn stars. In particular, their outburst properties are well correlated with orbital period and allow constraints to be placed on the secular mass transfer rate between secondary and primary if one adopts the disk instability model for the outbursts. We use the observed range of outbursting behavior for AM CVn systems as a function of orbital period to place a constraint on mass transfer rate versus orbital period. We infer a rate approximately 5 x 10(exp -9) solar mass yr(exp -1) ((P(sub orb)/1000 s)(exp -5.2)). We show that the functional form so obtained is consistent with the recurrence time-orbital period relation found by Levitan et al. using a simple theory for the recurrence time. Also, we predict that their steep dependence of outburst duration on orbital period will flatten considerably once the longer orbital period systems have more complete observations.

accretion↗
NASA NTRS2021

The X-ray Bursts of XTE J1739-285: A NICER Sample

In this work, we report on observations with the Neutron Star Interior Composition Explorer of the known neutron star X-ray transient XTE J1739–285. We observed the source in 2020 February and March, finding it in a highly active bursting state. Across a 20 day period, we detected 32 thermonuclear X-ray bursts, with an average burst recurrence time of-+2.0 hr0.30.4. A timing and spectral analysis of the ensemble of X-ray bursts reveals homogeneous burst properties, evidence for short-recurrence time bursts, and the detection of a 386.5 Hz burst oscillation candidate. The latter is especially notable, given that a previous study of this source claimed a 1122 Hz burst oscillation candidate. We did not find any evidence of variability near 1122 Hz and instead find that the 386.5 Hz oscillation is the more prominent signal of the two burst oscillation candidates. Hence, we conclude it is unlikely that XTE J1739–285 has a submillisecond rotation period.

X-ray burst↗
NASA NTRS2022

A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning

As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting current communications resources to accommodate aviation operations. Ongoing research proposes a paradigm of airspace communications, such that resources are autonomously and dynamically allocated via intelligent agents; this allocation requires accurate representations of the NAS, including the predicted positions of aircraft. State-of-the-art research emphasizes the importance of a hybrid-recurrent framework for trajectory prediction and compares the impact of commonly considered weather data on prediction accuracy. However, current research has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. This article considers the challenges of generalizing hybrid-recurrent predictive models for flight trajectories. Results illustrate an increase in error variance when identical models are trained over a generalized set of flights; this may be mitigated with careful tuning of hyperparameters, both in the network structure and optimization algorithms. Even so, an irreducible vertical error was identified, resulting from the complex takeoff and landing procedures which can not be correlated to functions of weather or additional assumptions of aircraft behavior. Finally, the use of a test route indicates that generalized models still do not possess sufficient knowledge for general aircraft predictions, with mean error increases ranging from 70-500%. These results illustrate the need for continued efforts on improving model versatility, as well as potential limitations for spectrum allocation near airports and other centers.

Nathan Schimpf↗
DOE OSTI2024

Third-integer Resonant Extraction Regulation System for Mu2e

A third-integer resonant slow extraction system is being developed for Fermilab's Delivery Ring to deliver protons to the upcoming Mu2e experiment. The timescale of the extraction (or spill) duration is 43 milliseconds, which is extremely short and unprecedented. Additionally, the experiment's strict and challenging requirements on the quality of the spill at this time scale has led to the development of a new Spill Regulation System (SRS) design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content suppressor. Contributions to the first two components of the SRS, i.e., Slow Regulation and Fast Regulation subsystems, will be presented in which new adaptive learning algorithm schemes for the slow regulation of the spill -- validated using particle tracking simulations -- shall be described. In addition to these novel methods for the enhancement of the spill regulation system, results of employing Machine Learning in enhancing the performance of the resonant extraction are also presented. At the forefront of applying ML techniques to solve non-linear accelerator control problems, this work includes optimizing the PID gains as well as the replacement of the traditional PID controller using Recurrent Neural Networks and Gated Recurrent Unit (GRU) ML models to achieve efficiencies greater than a PID controller. Cutting-edge on-going Reinforcement Learning efforts, including an actor-critic family of learning algorithms, to regulate the spill rate will be reviewed, as well as present analytical calculations pertaining the transit time of particles in a third-integer resonant extraction. Detailed numerical investigations and validations of such calculations, the model of which could be exported and reliably used in future analytical modeling of any resonant extraction, are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics-informed machine learning modeling for predictive control using noisy data

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

This work examines the frame-invariance (and the lack thereof) exhibited in simulated anisotropic elasto-plastic responses generated from supervised machine learning of classical multi-layer and informed-graph-based neural networks, and proposes different remedies to fix this drawback. The inherent hierarchical relations among physical quantities and state variables in an elasto-plasticity model are first represented as informed, directed graphs, where three variations of the graph are tested. While feed-forward neural networks are used to train path-independent constitutive relations (e.g., elasticity), recurrent neural networks are used to replicate responses that depends on the deformation history, i.e. or path dependent. In dealing with the objectivity deficiency, we use the spectral form to represent tensors and, subsequently, three metrics, the Euclidean distance between the Euler Angles, the distance from the identity matrix, and geodesic on the unit sphere in Lie algebra, can be employed to constitute objective functions for the supervised machine learning. In this, the aim is to minimize the measured distance between the true and the predicted 3D rotation entities. Following this, we conduct numerical experiments on how these metrics, which are theoretically equivalent, may lead to differences in the efficiency of the supervised machine learning as well as the accuracy and robustness of the resultant models. Neural network models trained with tensors represented in component form for a given Cartesian coordinate system are used as a benchmark. Our numerical tests show that, even given the same amount of information and data, the quality of the anisotropic elasto-plasticity model is highly sensitive to the way tensors are represented and measured. The results reveal that using a loss function based on geodesic on the unit sphere in Lie algebra together with an informed, directed graph yield significantly more accurate rotation prediction than the other tested approaches.

42 ENGINEERING↗
DOE OSTI2020

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU

Synchrophasor measurement devices (SMDs) have been widely deployed to support real-time monitoring and control of power systems. In the meantime, data spoofing has emerged in recent years. Therefore, it is of great importance to study data authentication algorithms for detecting and defending the data spoofing effectively. Here, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract temporal signatures hidden in frequency, voltage angle and amplitude data; then the gated recurrent unit (GRU) employs these temporal signatures for data source authentication. In case studies, the performances of different algorithms are tested in large-scale power systems with numerous SMDs for the first time, and comparisons among different algorithms show that the proposed algorithm can achieve a higher accuracy of data source authentication with a shorter time window.

47 OTHER INSTRUMENTATION↗