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Results for “trajectory 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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Large-scale current systems in the dayside Venus ionosphere

The occasional observation of large-scale horizontal magnetic fields within the dayside ionosphere of Venus by the flux gate magnetometer on the Pioneer Venus orbiter suggests the presence of large-scale current systems. Using the measured altitude profiles of the magnetic field and the electron density and temperature, together with the previously reported neutral atmosphere density and composition, it is found that the local ionosphere can be described at these times by a simple steady state model which treats the unobserved quantities, such as the electric field, as parameters. When the model is appropriate, the altitude profiles of the ion and electron velocities and the currents along the satellite trajectory can be inferred. These results elucidate the configurations and sources of the ionospheric current systems which produce the observed large-scale magnetic fields, and in particular illustrate the effect of ion-neutral coupling in the determination of the current system at low altitudes.

Luhmann, J. G.↗
NASA NTRS2005

Interannual Variations in Tropical Upper-Tropospheric Humidity: Understanding Tropical Convective and Dynamical Processes

Uncertainty remains as to what extent variability in mid to upper tropospheric moisture, especially over the tropics, behaves as constant relative humidity during interannual climate variations associated with ENSO. Systematic variations in HIRS 6.7 micron and MLS 205 GHz suggest that dry subtropical regions evolving during warm SST events depress relative humidity, but the interpretation of these events is still uncertain. Additional specific concerns have to do with regional signatures of convective processes: How does the origin of dry air in the eastern subtropical N. Pacific differ in ENSO warm versus cold years? The dynamics of Rossby wave forcing by convective heating, subtropical jet stream dynamics, and dynamics driven subsidence all come into play here. How variations in precipitating ice hydrometeors from tropical anvils relate to variations in UTH is also a subject of debate? Do variations in precipitating ice, cloud cover and water vapor behavior show any support for the Iris-hypothesis mechanism? Here we examine historical records of SSM/T-2 data to gain a better physical understanding of the effects of deep convective moisture sources and dynamically-induced vertical circulations on UTH. These high frequency microwave measurements (183.3 GHz) take advantage of far less sensitivity to cloud hydrometeors than the 6.7 micron data to yield a record of upper tropospheric relative humidity. Furthermore, signatures of precipitating ice from these channels facilitate comparisons to TRMM hydrometeors detected by radar. In analyzing these observations, we isolate water vapor and temperature change components that affect brightness temperatures and the inferred relative humidity. Trajectory modeling is also used to understand interannual humidity anomalies in terms of outflow fbm convective regions and history of diabatically-driven sinking which modifies relative humidity.

Robertson, Franklin R.↗
DOE OSTI2021

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

Here we propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in ~ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.

42 ENGINEERING↗
DOE OSTI2023

When action is not least for systems with action-dependent Lagrangians

The dynamics of some non-conservative and dissipative systems can be derived by calculating the first variation of an action-dependent action according to the variational principle of Herglotz. This is directly analogous to the variational principle of Hamilton commonly used to derive the dynamics of conservative systems. In a similar fashion, just as the second variation of a conservative system’s action can be used to infer whether that system’s possible trajectories are dynamically stable, so too can the second variation of the action-dependent action be used to infer whether the possible trajectories of non-conservative and dissipative systems are dynamically stable. In this paper, I show, generalizing earlier analyses of the second variation of the action for conservative systems, how to calculate the second variation of the action-dependent action and how to apply it to two physically important systems: a time-independent harmonic oscillator and a time-dependent harmonic oscillator.

Physics↗
DOE OSTI2021

Dynamics and energy coupling of gas puff Z-pinches on a fast linear transformer driver

Gas puff Z-pinch experiments with annular Ar and Ne gas shells have been conducted on the Compact Experimental System for Z-pinch and Ablation Research (CESZAR) linear transformer driver (LTD) with 500 kA current and 160 ns rise time. Here, we present results from the first systematic gas puff Z-pinch experiments using a fast (≤ 200 ns) LTD as a driver, in which we show that 7% of the stored energy in the capacitors is coupled to plasma kinetic energy as estimated via self-emission and laser schlieren images. 0D and 1D simulations—which do not allow instability growth and thus reach greater maximum average velocities—using initial conditions inferred from experimental implosion trajectories predict coupling in excess of 10% of the stored energy. Additionally, the Ar and Ne implosions were comparably massed and thus achieved similar maximum kinetic energies, though the Ne pinches were more stable and the x-ray pulses were longer and produced higher yield: 2–5 ns and 0.21–0.52 J (0.15–0.37 J/cm) of Ar K-shell and 12–25 ns and 2.2–3.9 J (1.6–2.6 J/cm) of Ne K-shell, respectively. The difference in stability is most likely attributed to variations in initial conditions such as density distribution and gas breakdown initiation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modeling of convective cells, turbulence, and transport induced by a radio-frequency antenna in the tokamak boundary plasma

The edge turbulence model Hermes (Dudson et al 2017 Plasma Phys. Control. Fusion 59 05401) is set up for plasma boundary simulations with an radiofrequency (RF) antenna, using parameters characteristic of a tokamak edge. Cartesian slab geometry is used with thin plate limiters representing the ion cyclotron range of frequency (ICRF) antenna side-wall limiters. Ad-hoc DC electric biasing of the limiters, motivated by calculations with VSim (Nieter et al 2004 J. Comput. Phys. 196 448), represents an induced RF sheath rectified potential in the plasma turbulence model. Flux-driven turbulence simulations demonstrate a realistic distribution of plasma profiles and fluctuations. There is a clear effect of the antenna sheath voltage leading to formation of convective cells; bias-induced convective transport flattens the scrape-off layer density profile and fluctuations penetrate into the shadow region of the limiters as the bias voltage increases. Turbulent transport for impurity ions is inferred by following ion trajectories in the simulated plasma turbulence fields, showing Bohm-like effective diffusion rates. All in all, the model elucidates the key physical phenomena governing the effects of ICRF-induced antenna biasing on the tokamak boundary plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors

Abstract We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere S 2 , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For 40 keV electrons in a 70% He 30% CO 2 gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26 ∘ ) compared to 0.556 (64 ∘ ) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.

Computer Science↗
DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗
NASA NTRS2019

The Seismic Signatures of Recently Formed Impact Craters on Mars

We investigated the seismic signatures of recent impact crater clusters on Mars that would be recorded by the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight)seismometers. We used a database of 77 measured and dated impact sites, with craters with diameters between 2.1 and 33.8 m, along with inferred impact angle, bolide trajectory, and varying target material properties to empirically scale for the momentum, expected seismic source function, and radiation pattern of impacts. The impact source is simulated in a local 3‐Dfinite difference wave propagation code and coupled to teleseismic distances by scaling the spectra of 1‐D global synthetic seismograms. We use the InSight seismometer noise floors to estimate detectability of impact(s) across azimuth and distance. Our experiments reveal that impact clusters have a higher peak corner frequency resulting from energy contributed by smaller craters to the power spectrum. We also find that the time separation between individual impacts in a cluster is small (<10–15 ms) and requires a seismometer closely situated to the source (<10 km) and a high sampling rate (>100 Hz) to resolve individual impacts within the cluster. Two of the clusters in our database(>20‐m effective diameter) would have been detectable by InSight, with the assumptions that the Martian background noise and seismic attenuation are both low. Joint detection of surface changes from newly formed crater(s) in images and by InSight will provide precise source locations that are crucial for constraining the internal structure of Mars

N. C. Schmerra↗
DOE OSTI2025

Differentially Private Adaptive Noise Injection (DP-ANI) v1.0

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This software implements a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.

Peisert, Sean [Lawrence Berkeley National Laborato↗
DOE OSTI2021

Analytic solutions for Asay foil trajectories with implications for ejecta source models and mass measurements

We consider the trajectory of an Asay foil ejecta diagnostic for scenarios where ejecta are produced at a singly shocked planar surface and fly ballistically through a perfect vacuum to the sensor. We do so by building upon a previously established mathematical framework derived for the analytic study of stationary sensors. First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source model. The result is an integrodifferential equation of motion for the foil trajectory. This equation yields an easily calculable closed-form implicit solution for the foil trajectory in instant-production scenarios. From there, we derive a boundary condition that particle velocity distributions must satisfy if their associated foil trajectories are to exhibit a smooth initial acceleration, as occurs in some experiments. This condition is identical to one derived previously from a consideration of piezoelectric voltage data obtained in similar experiments. We also compare techniques for inferring accumulated ejecta masses from foil trajectories, first by deriving the exact solution, and then by quantifying the error imposed by a frequently used approximate solution (both subject to the assumption of instantaneous ejecta production). Finally, we examine the common practice of presenting inferred cumulative ejecta masses as a function of implied ejecta velocity, establishing the conditions under which this methodology is most meaningful.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Spatiotemporal Dynamics of the Relative Abundance of Soil Nutrient‐Degrading Enzyme‐Encoding Genes Across Continental US Ecoregions

Understanding the spatiotemporal patterns in the relative abundance of soil extracellular enzyme‐encoding genes is critical for predicting microbial responses to environmental change and their potential role in nutrient cycling. Yet, integrating novel metagenomic observations with spatiotemporal environmental gradients to infer regional patterns and future trajectories has remained unclear. To address this gap, we applied a machine learning (ML) approach, integrating soil metagenomic data with environmental variables—soil properties, topography, vegetation, and climate—to predict the relative abundance of enzyme‐encoding genes for soil carbon (C), nitrogen (N), and phosphorus (P) across surface soils of the continental United States. We assessed potential responses under future emission scenarios (SSP2‐4.5 and SSP5‐8.5) by comparing a baseline (1985–2014) to a future period (2071–2100). The ML model explained 57%–63% of baseline variation. Precipitation was identified as the most influential factor for the relative abundance of C‐ and N‐degrading enzyme‐encoding genes, while slope length, representing horizontal distance that water can travel downslope, was the primary driver for P‐degrading enzyme‐encoding genes abundance. Projections revealed spatially heterogeneous shifts across continental US ecoregions: the relative abundance of C‐ and N‐degrading enzyme‐encoding genes decreased in wetter ecoregions and increased in drier ecoregions under future climate, while P‐degrading enzyme‐encoding genes abundance decreased significantly in semiarid and Mediterranean ecoregions. This study demonstrates the utility of metagenomic data for mapping soil genetic potential and predicting its regional response to environmental change, to inform ecosystem management strategies.

extracellular enzyme-encoding genes↗
DOE OSTI2021

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales-picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure–function relationships require atomistic knowledge of the populated states in the ensemble. Here, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗
NASA NTRS2006

The Interplay Between Strategic And Adaptive Control Mechanisms In Plastic Recalibration Of Locomotor Function

We have previously shown that viewing simulated rotary self-motion during treadmill locomotion causes immediate strategic modifications (Richards et al. 2004) as well as an after effect reflecting adaptive modification of the control of position and trajectory during over-ground locomotion (Mulavara et al. 2005). The process of sensorimotor adaptation is comprised of both strategic and adaptive control mechanisms. Strategic control involves cognitive, on-line corrections to limb movements once one is aware of a sensory discordance. Over an extended period of exposure to the sensory discordance, new strategic sensorimotor coordination patterns are reinforced until they become more automatic, and therefore adaptive, in nature. The objective of this study was to investigate how strategic changes in trunk control during exposure to simulated rotary self-motion during treadmill walking influences adaptive modification of locomotor heading direction during over-ground stepping. Subjects (n = 10) walked on a motorized linear treadmill while viewing a wide field-of-view virtual scene for 24 minutes. The scene was static for the first 4 minutes and then, for the last 20 minutes, depicted constant rate self-motion equivalent to walking in a counter-clockwise, circular path around the perimeter of a room. Subjects performed five stepping trials both before and after the exposure period to assess after effects. Results from our previous study showed a significant change in heading direction (HD) during post-exposure step tests that was opposite the direction in which the scene rotated during the adaptation period. For the present study, we quantified strategic modifications in trunk movement control during scene exposure using normalized root mean square (R(sub p)) variation of the subject's 3D trunk positions and normalized sum of standard deviations (R(sub o)) variation of 3D trunk orientations during scene rotation relative to that during static scene presentation. Associated 95% confidence intervals, CI(sub P) and CI(sub O), were calculated to investigate the variation of strategic modifications during scene exposure. Repeated measures ANOVA and individual subject regression analyses showed that R(sub P) and R(sub O) (i.e. strategic modifications) for trunk fore/aft (X) and yaw movements, respectively, decreased significantly over the exposure period. Furthermore, we found a significant correlation between the magnitude change in HD and the rate at which the variation of strategic modifications in trunk X decreased. We also found evidence of a correlation between HD and the rate at which strategic modifications in trunk yaw decreased (p = .06). We infer that adaptive recalibration of locomotor trajectory using optic flow stimuli depends on the rate at which strategic intervention is reduced.

Richards, J. T.↗
DOE OSTI2025

Amplification of laser imprint in the presence of strong, externally imposed, target-normal magnetic fields

Here, an experiment was performed on the OMEGA EP Laser System to investigate the effects of magnetic fields on laser imprint. A 30 µ⁢m thick CH target was driven by a single beam delivering 3.0 kJ of UV energy in a 5 ns square pulse without smoothing by spectral dispersion. The Rayleigh-Taylor amplification of this beam's imprint on the target surface was monitored using face-on gated x-ray radiography from a Gd backlighter. Magnetic fields of up to 45 T were applied normal to the target surface. Analysis of the resulting radiographs shows a 60 ± 13% increase in the spectrally resolved surface perturbation amplitudes, consistent at all times and for all unsaturated frequencies. This consistency indicates that the increase in perturbation amplitudes was due to a change in the initial amplitudes rather than Rayleigh-Taylor growth. This is supported by the trajectory of individual modes and the inferred time-averaged perturbation growth rates. Laser imprint is therefore inferred to have increased due to strong magnetic fields that remain in the conduction zone of the target, suppressing off-axis electron motion and limiting the effects of thermal smoothing.

direct drive↗
NASA NTRS1976

Investigation of a new stony meteorite from Mauritania with some additional data on its find site - Aouelloul crater

The Zerga meteorite, a small stony meteorite found at Aouelloul crater, is a fragment of a larger mass with a pre-atmospheric radius between 20 and 125 cm. The meteorite is a typical amphoterite, with monomict brecchia which has undergone at least one recrystallization episode. The terrestrial age of the meteorite is roughly an order of magnitude smaller than the K-Ar gas-retention age (about 3.1 million years) of the glassy impactite found at the crater, which is close to the age of the nearby Tenoumer crater (about 2.5 million years). The similar ages of the two craters, together with their alignment with the Temimichat Ghallaman crater, suggest a simultaneous triple impact following the disruption of a large meteorite moving on a very shallow earth trajectory. The unusually shallow original depths inferred for two of the craters may be explained by low impact angles.

Fudali, R. F.↗