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Results for “dynamic inference”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Observed currents on the earth's high-latitude magnetopause

A survey of electrical currents of the earth's magnetosphere, principally at high latitudes, as inferred from magnetic vector data acquired by the Hawkeye 1 satellite, is reported. A total of 536 candidate crossings of the magnetopause were examined. A reduced data set of 139 selected cases was analyzed in detail though solar wind dynamic pressure data were available for only 117 of these cases. Inferred values of the lineal current densities on the magnetopause are in the range 5.5 to 157.5 mA/m over a wide range of solar wind dynamic pressure from 1.17 to 16.1 nPa. The apparent normal thickness of the magnetopause current sheet ranges from 30 to 850 km with mean and median values of 185 and 158 km, respectively. It is argued that the radial rate of motion of the magnetopause is of the order of 2 km/s and hence that its true thickness is of similar magnitude. The relationship of these results to models of the geomagnetic field and to other related work is discussed.

Van Allen, J. A.↗
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exploring extreme magnetization phenomena in directly driven imploding cylindrical targets

Abstract This paper uses extended-magnetohydrodynamics (MHD) simulations to explore an extreme magnetized plasma regime realizable by cylindrical implosions on the OMEGA laser facility. This regime is characterized by highly compressed magnetic fields (greater than 10 kT across the fuel), which contain a significant proportion of the implosion energy and induce large electrical currents in the plasma. Parameters governing the different magnetization processes such as Ohmic dissipation and suppression of instabilities by magnetic tension are presented, allowing for optimization of experiments to study specific phenomena. For instance, a dopant added to the target gas-fill can enhance magnetic flux compression while enabling spectroscopic diagnosis of the imploding core. In particular, the use of Ar K-shell spectroscopy is investigated by performing detailed non-LTE atomic kinetics and radiative transfer calculations on the MHD data. Direct measurement of the core electron density and temperature would be possible, allowing for both the impact of magnetization on the final temperature and thermal pressure to be obtained. By assuming the magnetic field is frozen into the plasma motion, which is shown to be a good approximation for highly magnetized implosions, spectroscopic diagnosis could be used to estimate which magnetization processes are ruling the implosion dynamics; for example, a relation is given for inferring whether thermally driven or current-driven transport is dominating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Constraining physical models at gigabar pressures

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative infor mation. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA Laser System using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a 1-D hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. As a result, the goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Constraining Physical Models at Gigabar Pressures

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative information. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA Laser System using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a 1-D hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. The goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

A determination of the mass of Sagittarius A* from its radio spectral and source size measurements

There is growing evidence that Sgr A* may be a million solar mass black hole accreting from the Galactic center wind. A consideration of the spectral and source size characteristics associated with this process can offer at least two distinct means of inferring the mass M, complementing the more traditional dynamical arguments. We show that M is unmistakably correlated with both the radio spectral index and the critical wavelength below which the intrinsic source size dominates over the angular broadening due to scattering in the interstellar medium. Current observations can already rule out a mass much in excess of 2 x 10 exp 6 solar masses and suggest a likely value close to 1 x 10 exp 6 solar masses, in agreement with an earlier study matching the radio and high-energy spectral components. We anticipate that such a mass may be confirmed with the next generation of source-size observations using milliarcsecond angular resolution at 0.5 - 1 cm wavelengths.

Melia, Fulvio↗
NASA NTRS1992

Convection of tin in a Bridgman system. I - Flow characterization by effective diffusivity measurements

An electrochemical titration method was used to investigate the dynamic states in a cylindrical layer of convecting tin. The liquid tin was contained in a cell, with curved boundaries made of quartz and flat boundaries made of a solid state electrolyte - yttria-stabilized zirconia (YSZ). The electrolyte acted as a window through which a trace amount of oxygen could be pumped in or out by the application of a constant voltage. The concentration at the YSZ interface was monitored by operating the electrochemical cell in the galvanic mode. Experimentally determined effective diffusivities of oxygen were compared with the molecular diffusivity. Dynamic states in the convective flow were thus inferred. Temperature measurements were simultaneously made in order to identify the onset of oscillations from a steady convective regime. The experiments were conducted for two different aspect ratios for various imposed temperature gradients and two different orientations with respect to gravity. Transcritical states were identified and comparison to two-dimensional numerical models were made.

Sears, B.↗
NASA NTRS2000

The nu Andromedae System: Models and Stability

Radial velocity observations of the F8 V star nu Andromedae taken at Lick and at Whipple Observatories have revealed evidence of three periodicities in the line-of-sight velocity of the star. These periodicities have been interpreted as evidence for at least three low-mass companions (LMCs) revolving around nu Andromedae. The mass and orbital parameters inferred for these companions raise questions about the dynamical stability of the system. We report here results from our independent analysis of the published radial velocity data, as well as new unpublished data taken at Lick Observatory. Our results confirm the finding of three periods in the data. Our best fits to the data, on the assumption that these periods arise from the gravitational perturbations of companions in Keplerian orbits, are also generally in agreement but with some differences from the earlier findings. We find that the available data do not constrain well the orbital eccentricity of the middle companion in a three-companion model of the data. We also find that in order for our best-fit model to the Lick data to be dynamically stable over the lifetime of the star (approximately 2 billion years), the system must have a mean inclination to the plane of the sky greater than 13 deg. The corresponding minimum inclination for the best fit to the Whipple data set is 19 deg. These values imply that the maximum mass for the outer companion can be no greater than about 20 Jupiter masses. Our analysis of the stability of the putative systems also places constraints on the relative inclinations of the orbital planes of the companions. We comment on global versus local (i.e., method of steepest descent) means of finding best-fit orbits from radial velocity data sets.

Stepinski, Tomasz F.↗
NASA NTRS2003

Physics of Regolith Impacts in Microgravity Experiment (PRIME)

Collisions between planetary ring particles and in some protoplanetary disk environments occur at low impact velocities (v less than 1 m/s) . In some regions of Saturn s rings, for example, the typical collision velocity inferred from observations by the Voyager spacecraft and dynamical modeling is a fraction of a centimeter per second. Although no direct observations of an individual ring particle exist, the abundance of dust in planetary rings and protoplanetary disks suggests that larger ring and disk particles are coated with a layer of smaller particles and dust - the "regolith". Because the ring particles and proto-planetesimals are small (cm to m-sized), the regolith is only weakly bound to the surface by gravity. Similarly, secondary impacts on asteroids by large blocks of ejecta from high velocity cratering events result in low velocity impacts into the asteroid regolith, which is also weakly bound by the asteroid s gravity. At the current epoch and throughout their history, low velocity collisions have played an important role in sculpting planetary systems. In a one-Earth-gravity environment, it is not possible to experimentally determine the behavior of impact eject from such low velocity collisions. Impacts typically occur at speeds exceeding the mutual escape velocity of the two bodies. Thus, impacts at speeds on the order of 10 m/sec or less involve objects that are tens of meters across, or smaller. This research program is an experimental study of such low velocity collisions in a microgravity environment. The experimental work builds on the Collisions Into Dust Experiment (COLLIDE), which has flown twice on the space shuttle. The PRIME experimental apparatus is a new apparatus designed specifically for the environment provided on the NASA KC- 135 reduced gravity aircraft.

Motil, Brian↗
NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗
DOE OSTI2024

A Generation-Storage Coordination Dispatch Strategy for Power System Based on Causal Reinforcement Learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model - the crux of model-based reinforcement learning - thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

causal reinforcement learning↗
DOE OSTI2022

Dissociating the phononic, magnetic and electronic contributions to thermal conductivity: a computational study in alpha-iron

Computational tools to study thermodynamic properties of magnetic materials have, until recently, been limited to phenomenological modeling or to small domain sizes limiting our mechanistic understanding of thermal transport in ferromagnets. Herein, we study the interplay of phonon and magnetic spin contributions to the thermal conductivity in a-iron utilizing non-equilibrium molecular dynamics simulations. It was observed that the magnetic spin contribution to the total thermal conductivity exceeds lattice transport for temperatures up to two-thirds of the Curie temperature after which only strongly coupled magnon-phonon modes become active heat carriers. Characterizations of the phonon and magnon spectra give a detailed insight into the coupling between these heat carriers, and the temperature sensitivity of these coupled systems. Comparisons to both experiments and ab initio data support our inferred electronic thermal conductivity, supporting the coupled molecular dynamics/spin dynamics framework as a viable method to extend the predictive capability for magnetic material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2023

Hindered segmental dynamics in associative protein hydrogels studied by neutron spin-echo spectroscopy

Transient binding between associating macromolecules can cause qualitative changes to chain dynamics, including modes of conformational relaxation and diffusion, through tethering effects imparted by long-range connectivity. Here, in this work, the role of binding on short-time segmental dynamics in associative polymer gels is investigated by neutron spin-echo (NSE) measurements on a class of model artificial coiled-coil proteins with a systematically varied architecture, probing timescales of 0.1–130 ns, and length scales close to the molecular radius of gyration. The results illustrate effects of transient cross-linking on chain dynamics on different timescales, manifested in changes in segmental relaxation behavior with variations in strand length, chain concentration, and sticker distribution (endblock- vs midblock-functionalized). In all gels, a short-time cooperative diffusion mode is seen over all wave vectors, analogous to a semidilute solution, with no transitions seen at any known structural length scale. However, the diffusion coefficients are found to decrease with increasing junction density across all gels, with the strand length and number of stickers per chain in each gel appearing to play a relatively minor role. The slowing of cooperative diffusion with junction density contrasts with classical predictions of a greater restoring force for fluctuation dissipation due to the increased elasticity, suggesting additional effects of the coiled-coil junctions such as an enhancement in local viscosity that slows dynamics. Notably, the relaxation rates for all gels can be rescaled by the interjunction spacing inferred from small-angle neutron scattering, where they collapse onto a master curve suggestive of self-similar dynamics even in networks with different strand lengths and chain architectures. On long timescales (but shorter than the junction exchange time), a slowing of network relaxation is observed, resulting in a nondecaying plateau in the spin-echo amplitude attributed to a freezing of chain dynamics due to tethering. A characteristic length scale corresponding to the extent of dynamic fluctuations is estimated for each gel, which appears to be smaller than the interjunction spacing but similar to the correlation blob size of the overlapping strands. The results indicate an important role of transient binding on molecular-scale dynamics in associative polymer gels, even on timescales shorter than the junction exchange time, in addition to its effects on long-range self-diffusion previously observed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mechanical Resonant Sensing of Spin Texture Dynamics in a 2D Antiferromagnet

Abstract The coupling between the spin degrees of freedom and macroscopic mechanical motions, including striction, shearing, and rotation, has attracted wide interest with applications in actuation, transduction, and information processing. Experiments so far have established the mechanical responses to the long‐range ordered or isolated single spin states. However, it remains elusive whether mechanical motions can couple to a different type of magnetic structure, the non‐collinear spin textures, which exhibit nanoscale spatial variations of spin (domain walls, skyrmions,etc.) and are promising candidates to realize high‐speed computing devices. Here, collective spin texture dynamics is detected with nanoelectromechanical resonators fabricated from 2D antiferromagnetic (AFM) MnPS 3 with 10 −9 strain sensitivity. By examining radio frequency mechanical oscillations under magnetic fields, new magnetic transitions are identified with sharp dips in resonant frequency. They are attributed to collective AFM domain wall motions as supported by the analytical modeling of magnetostriction and large‐scale spin‐dynamics simulations. Additionally, an abnormally large modulation in the mechanical nonlinearity at the transition field infers a fluid‐like response due to ultrafast domain motion. The work establishes a strong coupling between spin texture and mechanical dynamics, laying the foundation for electromechanical manipulation of spin texture and developing quantum hybrid devices.

Chemistry↗
DOE OSTI2021

Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses

This protocol provides a step-by-step approach to perturb single cells with time-varying stimulation profiles, collect distinct signaling responses, and use these to infer a system of ordinary differential equations to capture and predict dynamics of protein-protein regulation in signal transduction pathways. The models are validated by predicting the signaling activation upon new cell stimulation conditions. In comparison to using standard step-like stimulations, application of diverse time-varying cell stimulations results in better inference of model parameters and substantially improves model predictions. For complete details on the use and results of this protocol, please refer to Jashnsaz et al. (2020).

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Gravity mismodelling on TOPEX orbit determination

Gravity mismodeling limits the dynamic orbit determination accuracy of TOPEX which requires an altitude accuracy of about 15 cm or better. This paper provides an assessment of this effect for a dynamic GPS tracking scheme, and investigates a nondynamic GPS tracking technique that eliminates the problem. Analysis indicates that TOPEX altitude error could be affected by as much as 30-40 cm when dynamic differential GPS tracking is used, although efforts now underway to improve the gravity model could reduce this. With the nondynamic technique, the positional change of TOPEX is inferred from the accurate, continuous GPS carrier phase measurements rather than from a dynamic model which is prone to gravity error. TOPEX altitude accuracy better than 15 cm is attainable with nondynamic tracking over a few hours.

Wu, S.-C.↗