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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1987

A space-time stochastic model of rainfall for satellite remote-sensing studies

A model of the spatial and temporal distribution of rainfall is described that produces random spatial rainfall patterns with these characteristics: (1) the model is defined on a grid with each grid point representing the average rain rate over the surrounding grid box, (2) rain occurs at any one grid point, on average, a specified percentage of the time and has a lognormal probability distribution, (3) spatial correlation of the rainfall can be arbitrarily prescribed, and (4) time stepping is carried out so that large-scale features persist longer than small-scale features. Rain is generated in the model from the portion of a correlated Gaussian random field that exceeds a threshold. The portion of the field above the threshold is rescaled to have a lognormal probability distribution. Sample output of the model designed to mimic radar observations of rainfall during the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), is shown. The model is intended for use in evaluating sampling strategies for satellite remote-sensing of rainfall and for development of algorithms for converting radiant intensity received by an instrument from its field of view into rainfall amount.

Bell, Thomas L.↗
NASA NTRS2014

Development and Transition of the Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) Toolset

We outline a plan to develop and transition a physics based predictive toolset called The Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) to describe the interplanetary energetic particle and radiation environment throughout the inner heliosphere, including at the Earth. To forecast and "nowcast" the radiation environment requires the fusing of three components: 1) the ability to provide probabilities for incipient solar activity; 2) the use of these probabilities and daily coronal and solar wind observations to model the 3D spatial and temporal heliosphere, including magnetic field structure and transients, within 10 Astronomical Units; and 3) the ability to model the acceleration and transport of energetic particles based on current and anticipated coronal and heliospheric conditions. We describe how to address 1) - 3) based on our existing, well developed, and validated codes and models. The goal of RISCS toolset is to provide an operational forecast and "nowcast" capability that will a) predict solar energetic particle (SEP) intensities; b) spectra for protons and heavy ions; c) predict maximum energies and their duration; d) SEP composition; e) cosmic ray intensities, and f) plasma parameters, including shock arrival times, strength and obliquity at any given heliospheric location and time. The toolset would have a 72 hour predicative capability, with associated probabilistic bounds, that would be updated hourly thereafter to improve the predicted event(s) and reduce the associated probability bounds. The RISCS toolset would be highly adaptable and portable, capable of running on a variety of platforms to accommodate various operational needs and requirements. The described transition plan is based on a well established approach developed in the Earth Science discipline that ensures that the customer has a tool that meets their needs

Spann, James F.↗
DOE OSTI2016

Quantifying spatially and temporally explicit CO 2 fertilization effects on global terrestrial ecosystem carbon dynamics

Abstract Current terrestrial ecosystem models are usually driven with global average annual atmospheric carbon dioxide ( CO 2 ) concentration data at the global scale. However, high‐precision CO 2 measurement from eddy flux towers showed that seasonal, spatial surface atmospheric CO 2 concentration differences were as large as 35 ppmv and the site‐level tests indicated that the CO 2 variation exhibited different effects on plant photosynthesis. Here we used a process‐based ecosystem model driven with two spatially and temporally explicit CO 2 data sets to analyze the atmospheric CO 2 fertilization effects on the global carbon dynamics of terrestrial ecosystems from 2003 to 2010. Our results demonstrated that CO 2 seasonal variation had a negative effect on plant carbon assimilation, while CO 2 spatial variation exhibited a positive impact. When both CO 2 seasonal and spatial effects were considered, global gross primary production and net ecosystem production were 1.7 Pg C·yr −1 and 0.08 Pg C·yr −1 higher than the simulation using uniformly distributed CO 2 data set and the difference was significant in tropical and temperate evergreen broadleaf forest regions. This study suggests that the CO 2 observation network should be expanded so that the realistic CO 2 variation can be incorporated into the land surface models to adequately account for CO 2 fertilization effects on global terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Modeling Modern Methane Emissions from Natural Wetlands: Model Description and Results - 1

Methane is an important greenhouse gas which contributes about 22 percent to the present greenhouse effect. Natural wetlands currently constitute the biggest methane source and were the major source in preindustrial times. Wetland emissions depend highly on the climate, i.e., on soil temperature and water table. To investigate the response of methane emissions from natural wetlands to climate variations, a process-based model that derives methane emissions from natural wetlands as a function of soil temperature, water table, and net primary productivity is used. For its application on the global scale, global data sets for all model parameters are generated. In addition, a simple hydrologic model is developed in order to simulate the position of the water table in wetlands. The hydrologic model is tested against data from different wetland sites, and the sensitivity of the hydrologic model to changes in precipitation is examined. The global methane­ hydrology model constitutes a tool to study temporal and spatial variations in methane emissions from natural wetlands. The model is applied using high-frequency atmospheric forcing fields from European Center for Medium-range Weather Forecasts (ECMWF) re-analyses of the period from 1982 to 1993. We calculate global annual methane emissions from wetlands to be 260 teragrams per year. Twenty-five percent of these methane emissions originate from wetlands north of 30 degrees North Latitude. Only 60 percent of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands the seasonality of simulated and observed methane emissions agrees well.

Biogeochemical processes↗
DOE OSTI2020

Multi-Modal Energy Analysis for Freight

This project addresses the impact of emerging technologies on inter-city freight energy demand, shipping time, and cost. Technologies addressed include electrified long-haul trucks, connectivity and efficient operations, and platooning. The research questions are addressed using national level analysis, a multi-modal inter-city freight energy model, and development of a Freight Mobility Energy Productivity (F-MEP) metric. This project improves on prior analysis and modeling by increasing the geographic and temporal resolution of freight flows. The inter-city freight energy model uses a bi-level optimization approach to minimize total network cost (lower level) and energy (upper level). Two new F-MEP frameworks are presented, one for intra-city freight mobility and one for inter-city freight mobility.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2018

Longer TOMM40 poly-T variants associated with higher FDDNP-PET medial temporal tau and amyloid binding

The translocase of outer mitochondrial membrane 40 (TOMM40), which lies in linkage disequilibrium with the apolipoprotein E (APOE) gene, has been implicated in Alzheimer’s disease (AD). TOMM40 influences AD pathology through mitochondrial neurotoxicity, and the medial temporal lobe (MTL) is the most likely brain region for identifying early manifestations of AD-related morphology changes. While early reports indicated that the longer length poly-T allele of TOMM40 increases risk for AD, these findings have not been consistently replicated in further studies. We examined the effect of TOMM40 and APOE on regional brain positron emission tomography (PET) 2-(1-{6-[(2 [F18]fluoroethyl) (methyl) amino]-2-naphthyl}ethylidene)malononitrile (FDDNP) binding values in MTL.A total of 73 non-demented older adults (42 females; mean age: 62.9(10.9) completed genotyping for both APOE and TOMM40 and received FDDNP-PET scans. For TOMM40, the lengths of the poly-T sequence were classified as short (14–20 repeats; S), long (21–29 repeats, L) or very long (>29 repeats, VL). Using general linear models, we examined medial temporal lobe FDDNP binding and cognitive functioning between TOMM40 and APOE-4 groups, with age, sex, and education as covariates. Data from 30 individuals with APOE-4 and L TOMM40 poly-T length, 11 non E4 TOMM40 S/S, 14 non E4 TOMM40 S/VL and 13 non E4 TOMM40 VL/VL were analyzed. Medial temporal FDDNP binding differed significantly between TOMM40/APOE groups (F(3,62) = 3.3,p = .03). Participants with TOMM40 S/S exhibited significantly lower binding compared to TOMM40 S/VL and APOE-4 carriers. We did not find a significant relationship between TOMM40 poly-T lengths/APOE risk groups and cognitive functioning. This is the first report to demonstrate a significant association between longer TOMM40 poly-T lengths and higher medial temporal plaque and tangle burden in non-demented older adults. Identifying biomarkers that are risk factors for AD will enhance our ability to identify subjects likely to benefit from novel AD treatments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2021

Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC/yr for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC/yr). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

land use and land cover change↗
NASA NTRSDate not supplied

Multi-Domain Routing in Delay Tolerant Networks

The goal of Delay Tolerant Networking (DTN) is to provide the missing ingredient for the ever-growing collection of communicating nodes in our solar system to become a Solar System Internet (SSI). Great strides have been made in modeling particular types of DTNs, such as schedule- or discovery-based. Now, analogously to the Internet, these smaller DTNs can be considered routing domains which must be stitched together to form the overall SSI. In this paper, we propose a framework for cross-domain routing in DTNs as well as methodologies for detecting these sub-domains. Example time-varying networks are given to demonstrate the techniques proposed. A basic component is the mathematical theory of sheaves, which unifies the underlying model of DTN routing algorithms, by giving rise to routing sheaves – these can be defined for the dynamic and scheduled networks as noted above, and can also be used to define the interfaces between these domains in order to route across them. An immediate application would be routing across discovery-based networks connected by scheduled networks. These DTN subdomains remain elusive, however, and need to become well-defined and properly sized for tractable computability. In particular, a balance must be determined between areas that are too large (i.e. large matrix computations) versus areas that are too small (i.e. “many” single-noded domains). Moreover, the connections between the domains should, at least locally, be chosen to optimize data flow and connectivity: we address this in three ways. First, tools from persistent homology are given to understand underlying structures, reminiscent of hierarchies in the Internet Protocol (IP) addressing. Second, we construct a notion of temporal graph curvature based on network geometry to analyze flows induced by dynamical processes on these networks. Finally, Schrodinger Bridges, a tool arising from statistical physics, are proposed as a method of constructing flows on time-evolving networks with desirable properties such as speed, robustness, and load sensitivity. We construct an approach to temporal hypergraphs to simultaneously model unicast, multicast, and broadcast, using the language of scheme theory, and then consider DTN network coding as a way to achieve network-level computation and organization. The paper concludes with a discussion and ideas for future work.

Alan Hylton↗
DOE OSTI2021

Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Abstract. Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Data integration reveals key homeostatic mechanisms following low dose radiation exposure

The goal of this study was to define pathways regulated by low dose radiation to understand how biological systems respond to subtle perturbations in their environment and prioritize pathways for human health assessment. Using an in vitro 3-D human full thickness skin model, we have examined the temporal response of dermal and epidermal layers to 10 cGy X-ray using transcriptomic, proteomic, phosphoproteomic and metabolomic platforms. Bioinformatics analysis of each dataset independently revealed potential signaling mechanisms affected by low dose radiation, and integrating data shed additional insight into the mechanisms regulating low dose responses in human tissue. We examined direct interactions among datasets (top down approach) and defined several hubs as significant regulators, including transcription factors (YY1, MYC and CREB1), kinases (CDK2, PLK1) and a protease (MMP2). These data indicate a shift in response across time — with an increase in DNA repair, tissue remodeling and repression of cell proliferation acutely (24–72 h). Pathway-based integration (bottom up approach) identified common molecular and pathway responses to low dose radiation, including oxidative stress, nitric oxide signaling and transcriptional regulation through the SP1 factor that would not have been identified by the individual data sets. Significant regulation of key downstream metabolites of nitrative stress was measured within these pathways. Among the features identified in our study, the regulation of MMP2 and SP1 was experimentally validated. Our results demonstrate the advantage of data integration to broadly define the pathways and networks that represent the mechanisms by which complex biological systems respond to perturbation. - Highlights: • Low dose ionizing radiation altered homeostasis in 3D skin tissue model. • Global gene/protein/metabolite data integrated using complementary statistical approaches • Time and location-specific change in matrix regulation, ROS/RNS and DNA repair pathways detected • Low dose exposure alters metabolites involved in nitric oxide biosynthesis and wound healing. • Computationally predicted regulators of primary mechanisms were experimentally validated.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Temporally and spatially resolved characterization of microwave induced argon plasmas: Experiment and modeling

Experiments and modeling of the plasma-microwave interaction have been performed in a coaxial microwave plasma source at a field frequency of 2.45 GHz generating argon plasmas at pressures of 20 and 40 millibars and a ratio of flow rate to pressure of 0.125 sccm/Pa. The incident microwave power between 100 W and 300 W is supplied in a regime of a pulse-width modulation with cycle duration of 110 ms and a power-on time of 23 ms. The experiments are based on heterodyne reflectometry and microwave interferometry at 45.75 GHz. They provide the temporal behaviour of the complex reflection coefficient, the microwave power in the plasma, as well as the electron density in the afterglow zone of the discharge. The self-consistent spatially two-dimensional and time-dependent modeling complements the analysis of the plasma-microwave interaction delivering the plasma and electromagnetic field parameters. The consolidating experimental observations and model predictions allow further characterizing the plasma source. The generated plasma has a core occupying the region close to the end of the inner electrode, where maximum electron densities above 10{sup 20} m{sup −3} and electron temperatures of about 1 eV are observed. Due to a longer outer electrode of the coaxial structure, the plasma region is extended and fills the volume comprised by the outer electrode. The electron density reaches values of the order of 10{sup 19} m{sup −3}. The heating of the gas occurs in its great part due to elastic collisions with the plasma electrons. However, the contribution of the convective heating is important especially in the extended plasma region, where the gas temperature reaches its maximum values up to approximately 1400 K. The temporally and spatially resolved modeling enables a thorough investigation of the plasma-microwave interaction which clearly shows that the power in-coupling occurs in the region of the highest electron density during the early stage of the discharge. In the steady state phase, however, the power in-coupling occurs close to the source walls where the electron density is significantly lower than on the discharge axis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Windowed least-squares model reduction for dynamical systems

Here we propose a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Parametric dynamic mode decomposition for reduced order modeling

Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value decomposition of the temporal data sets. For parameter-dependent models, as found in many multi-query applications such as uncertainty quantification or design optimization, the only parametric DMD technique developed was a stacked approach, with data sets at multiple parameter values were aggregated together, increasing the computational work needed to devise low-rank dynamical reduced-order models. Here in this paper, we present two novel approach to carry out parametric DMD: one based on the interpolation of the reduced-order DMD eigen-pair and the other based on the interpolation of the reduced DMD (Koopman) operator. Numerical results are presented for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. All three parametric DMD approaches are compared.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Accretion flow properties of MAXI J1543-564 during 2011 outburst from the TCAF solution

We derive accretion flow properties of the transient black hole candidate (BHC) MAXI J1543–564 using the RXTE data. We use the two-component advective flow (TCAF) solution to fit the data of the initial rising phase of outburst (from 2011 May 10 to 15). The 2.5–25 keV spectra are fitted using the TCAF solution fits file as a local additive table model in XSPEC. We extract physical flow parameters such as the two-component (Keplerian disk and sub-Keplerian halo) accretion rates and size and the property of the Compton cloud (post-shock region close to a black hole). Similar to other classical transient BHCs, monotonic evolution of low-frequency quasi-periodic oscillations (QPOs) is observed during the rising phase of the outburst, which is fitted with the propagating oscillatory shock (POS) model, which describes how the Compton cloud properties change from day to day. From the nature of variations of TCAF model fitted physical flow parameters and QPOs, we only found hard-intermediate and soft-intermediate spectral states during this phase of the outburst under study. We also calculated the frequency of the dominating QPOs from the TCAF model fitted shock parameters and found that they roughly match with the observed and POS model fitted values. From our spectro-temporal study of the source with TCAF and POS models, the most probable mass of the BHC is found to be 12.6–14.0 M {sub ⊙}, or 13{sub −0.4}{sup +1.0} M{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

Distribution of aurora and ionospheric currents observed simultaneously on a global scale

The instantaneous spatial distribution of auroral emissions is observed with auroral imaging photometers on board the spacecraft Dynamics Explorer 1 (DE 1) as ground-based meridian chains of magnetometers simultaneously detect the magnetic signatures of ionospheric and field-aligned currents flowing at northern polar latitudes. Ionospheric conductivities at nighttime auroral latitudes are estimated from the measured auroral luminosities and used with the measured polar magnetic variations to compute model distributions of ionospheric and field-aligned currents. Temporal resolution for the coordinated observations and model calculations is 12 minutes. Model ionospheric and field-aligned current distributions are overlayed on global auroral images to illustrate spatial relations on a global scale at the maximum epoch of an auroral substorm. Eccentric-dipole-latitude and magnetic-local-time coordinates are used. A model field-aligned current distribution is compared quantitatively with the distribution of field-aligned currents inferred from simultaneous observations by the DE-2 magnetometer.

Craven, J. D.↗