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Results for “transfer entropy”

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NASA NTRS1993

Effects of shock strength on shock turbulence interaction

Direct numerical simulation (DNS) and linear analysis (LIA) of isotropic turbulence interacting with a shock wave are performed for several upstream shock normal Mach numbers (M(sub 1)). Turbulence kinetic energy (TKE) is amplified across the shock wave, but this amplification tends to saturate beyond M(sub 1) = 3.0. TKE amplification and Reynolds stress anisotropy obtained in DNS are consistent with LIA predictions. Rapid evolution of TKE immediate downstream of the shock wave persists for all shock strengths and is attributed to the transfer between kinetic and potential modes of turbulence energy through acoustic fluctuations. Changes in energy spectra and various length scales across the shock wave are predicted by LIA, which is consistent with DNS results. Most turbulence length scales decrease across the shock. Dissipation length scale (rho-bar q(exp 3) / epsilon), however, increases slightly for shock waves with M(sub 1) less than 1.65. Fluctuations in thermodynamic variables behind the shock wave stay nearly isentropic for M(sub 1) less than 1.2 and deviate significantly from isentropy for the stronger shock waves due to large entropy fluctuation generated through the interaction.

Lee, Sangsan↗

NASA NTRS1972

Boundary-Layer Edge Conditions and Transition Reynolds Number Data for a Flight Test at Mach 20 (Reentry F)

A flight experiment, designated Reentry F, was conducted to measure heat-transfer rates for laminar, transitional, and turbulent boundary layers on a 5 deg half-angle cone 3.962 m (13 ft) long with a preflight nose radius of 2.54 mm (0.10 in.). Data were obtained over an altitude range from 36.58 to 18.29 km (120 000 to 60 000 ft) at a flight velocity of about 6.096 km/sec (20 000 ft/sec). The nominal values of the free-stream total enthalpy, sharp-cone Mach number, and the wall-to-total enthalpy ratio were 18 MJ/kg (8000 Btu/lb), 15, and 0.03, respectively. Calculated boundary-layer edge conditions that account for effects of the entropy layer and corresponding local transition Reynolds numbers are reported in the present paper. Fully developed turbulent flow occurred with essentially constant boundary-layer edge conditions near the sharp-cone values. Transition data were obtained with local edge Mach numbers ranging from about 5.55 to 15. Transition Reynolds numbers, based on local condition, were as high as 6.6 x 10(exp 7) with an edge Mach number of about 14.4 at an altitude of 24.38 km (80 000 ft). The transition could be correlated with previous flight data taken over a Mach number range from 3 to 12 in terms of parameters including the effects of local unit Reynolds number, boundary-layer wall-to-edge enthalpy ratio, and local Mach number.

Johnson, Charles B.↗

NASA NTRS2004

Thermodynamic Conditions Favorable to Superlative Thunderstorm Updraft, Mixed Phase Microphysics and Lightning Flash Rate

Satellite observations of lightning flash rate have been merged with proximal surface station thermodynamic observations toward improving the understanding of the response of the updraft and lightning activity in the tropical atmosphere to temperature. The tropical results have led in turn to an examination of thermodynamic climatology over the continental United States in summertime and its comparison with exceptional electrical conditions documented in earlier studies. The tropical and mid-latitude results taken together support an important role for cloud base height in regulating the transfer of Convective Available Potential Energy (CAPE) to updraft kinetic energy in thunderstorms. In the tropics, cloud base height is dominated by the dry bulb temperature over the wet bulb temperature as the lightning-regulating temperature in regions characterized by moist convection. In the extratropics, an elevated cloud base height may enable larger cloud water concentrations in the mixed phase region, a favorable condition for the positive charging of large ice particles that may result in thunderclouds with a reversed polarity of the main cloud dipole. The combined requirements of instability and cloud base height serve to confine the region of superlative electrification to the vicinity of the ridge in moist entropy in the western Great Plains.

Williams, E.↗

NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

NASA NTRS2021

NICER Discovery of Millisecond X-Ray Pulsations and an Ultracompact Orbit in IGR J17494-3030

We report the detection of 376.05 Hz (2.66 ms) coherent X-ray pulsations in NICER observations of a transient outburst of the low-mass X-ray binary IGR J17494−3030 in 2020 October/November. The system is an accreting millisecond X-ray pulsar in a 75-minute ultracompact binary. The mass donor is most likely a ≃0.02 Mꙩ finite-entropy white dwarf composed of He or C/O. The fractional rms pulsed amplitude is 7.4%, and the soft (1–3 keV) X-ray pulse profile contains a significant second harmonic. The pulsed amplitude and pulse phase lag (relative to our mean timing model) are energy dependent, each having a local maximum at 4 and 1.5 keV, respectively. We also recovered the X-ray pulsations in archival 2012 XMM-Newton observations, allowing us to measure a long-term pulsar spin-down rate of ύ= -2.1(7) x (10)^(-14) Hz/s and to infer a pulsar surface dipole magnetic field strength of ≃10^(9)G. We show that the mass transfer in the binary is likely nonconservative, and we discuss various scenarios for mass loss from the system.

Mason Ng↗

DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗

NASA NTRS2023

Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.

Image coding↗

NASA NTRS2014

Forced Reconnection in the Near Magnetotail: Onset and Energy Conversion in PIC and MHD Simulations

Using two-dimensional particle-in-cell (PIC) together with magnetohydrodynamic (MHD) Q1 simulations of magnetotail dynamics, we investigate the evolution toward onset of reconnection and the subsequent energy transfer and conversion. In either case, reconnection onset is preceded by a driven phase, during which magnetic flux is added to the tail at the high-latitude boundaries, followed by a relaxation phase, during which the configuration continues to respond to the driving. The boundary deformation leads to the formation of thin embedded current sheets, which are bifurcated in the near tail, converging to a single sheet farther out in the MHD simulations. The thin current sheets in the PIC simulation are carried by electrons and are associated with a strong perpendicular electrostatic field, which may provide a connection to parallel potentials and auroral arcs and an ionospheric signal even prior to the onset of reconnection. The PIC simulation very well satisfies integral entropy conservation (intrinsic to ideal MHD) during this phase, supporting ideal ballooning stability. Eventually, the current intensification leads to the onset of reconnection, the formation and ejection of a plasmoid, and a collapse of the inner tail. The earthward flow shows the characteristics of a dipolarization front: enhancement of Bz, associated with a thin vertical electron current sheet in the PIC simulation. Both MHD and PIC simulations show a dominance of energy conversion from incoming Poynting flux to outgoing enthalpy flux, resulting in heating of the inner tail. Localized Joule dissipation plays only a minor role.

two-dimensional↗

DOE OSTI2025

Intrinsic activation energies for ring contraction of allylic cations in zeolites

Cyclic carbocations are important intermediates in zeolite-catalyzed chemistries such as methanol-to-hydrocarbons conversion, naphthenes ring opening, or coke formation. While information about their thermodynamic stability exists, little is known about the kinetics of formation and transformation of cyclic carbocations in zeolites. To fill this knowledge gap, ring contraction of the 1,3,5,5-tetramethylcyclohexenyl cation (C 10 H 17 + ), a representative of 6-membered ring allylic cations, was investigated by in situ UV–vis and IR spectroscopy. Protonic forms of zeolites served as catalysts, at temperatures from 80 °C to 135 °C. Significant oligomerization and hydride transfer in BEA and FAU hampered kinetics analysis, whereas ring contraction dominated in the channels of MOR. The reactant cation, characterized by an electronic absorption at 314 nm and an allylic stretch at 1549 cm −1 , contracted to both a 1,3-alkyl-substituted cyclopentenyl cation (287 nm and 1506 cm −1 ) and a 1,2,3-alkyl-substituted cyclopentenyl cation (297 nm and 1489 cm −1 ). Collection of time-resolved IR spectra and fitting of the intensities with various kinetic models revealed a third transformation, which is expected from thermodynamics: the 1,3-alkyl-substituted cyclopentenyl cation isomerizes to the 1,2,3-alkyl-substituted cyclopentenyl cation. Series of IR spectra recorded at different temperatures delivered intrinsic activation enthalpies (entropies) in MOR of 67 ± 2 kJ mol −1 (−130 ± 6 J mol −1 K −1 ) and 90 ± 3 kJ mol −1 (−70 ± 8 J mol −1 K −1 ) for the contraction to 1,3- and 1,2,3-substituted species, and of 88 ± 5 kJ mol −1 (−84 ± 12 J mol −1 K −1 ) for the isomerization. The findings characterize one path – via contraction of larger rings – to different cyclopentenyl species in zeolites; and the associated, moderate activation energies suggest such transformations contribute to many complex hydrocarbon reaction networks.

Acid catalysis↗

DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗

NASA NTRS2008

Effect of Adding a Regenerator to Kornhauser's MIT "Two-Space" (Gas-Spring+Heat Exchanger) Test Rig

This study employed entropy-based second law post-processing analysis to characterize the various thermodynamic losses inside a 3-space solution domain (gas spring+heat exchanger+regenerator) operating under conditions of oscillating pressure and oscillating flow. The 3- space solution domain is adapted from the 2-space solution domain (gas spring+heat exchanger) in Kornhauser's MIT test rig by modifying the heat exchanger space to include a porous regenerator system. A thermal nonequilibrium model which assumes that the regenerator porous matrix and gas average temperatures can differ by several degrees at a given axial location and time during the cycle is employed. An important and primary objective of this study is the development and application of a thermodynamic loss post-processor to characterize the major thermodynamic losses inside the 3-space model. It is anticipated that the experience gained from thermodynamic loss analysis of the simple 3-space model can be extrapolated to more complex systems like the Stirling engine. It is hoped that successful development of loss post-processors will facilitate the improvement of the optimization capability of Stirling engine analysis codes through better understanding of the heat transfer and power losses. It is also anticipated that the incorporation of a successful thermal nonequilibrium model of the regenerator in Stirling engine CFD analysis codes, will improve our ability to accurately model Stirling regenerators relative to current multidimensional thermal-equilibrium porous media models.

Ebiana, Asuquo B.↗

DOE OSTI2024

Magnetic dipole γ-ray strength functions in the crossover from spherical to deformed neodymium isotopes

We calculate the magnetic dipole $\gamma$-ray strength functions in a chain of even-mass neodymium isotopes $^{144-152}$Nd in the framework of the configuration-interaction (CI) shell model. We infer the strength function by applying the maximum entropy method (MEM) to the exact imaginary-time response function calculated with the shell-model Monte Carlo (SMMC) method. The success of the MEM depends on the choice of a good strength function as a prior distribution. We investigate two choices for the prior strength function: the static path approximation (SPA) and the quasiparticle random-phase approximation (QRPA). We find that the QRPA is a better approximation at low temperatures (i.e., near the ground state), while the SPA is a better choice at finite temperatures. We identify a low-energy enhancement (LEE) in the MEM deexcitation $M1$ strength functions of the even-mass neodymium isotopes and compare with recent experimental results for the total deexcitation $\gamma$-ray strength functions. The LEE is already seen in the SPA strength function but not in the QRPA strength function, indicating the importance of large-amplitude static fluctuations around the mean field in reproducing the LEE. Our method is currently the only one which can reproduce LEE in heavy open-shell nuclei where conventional CI shell model calculations are prohibited. With the onset of deformation as number of neutrons increases along the chain of neodymium isotopes, we observe that some of the LEE strength transfers to a low-energy excitation, which we interpret as a finite-temperature ``scissors'' mode. Here, we also observe a finite-temperature spin-flip mode.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗

DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗

NASA NTRS2019

Nonmodal Growth of TravelingWaves on Blunt Cones at Hypersonic Speeds

The existing database of transition measurements in hypersonic ground facilities has established that, as the nosetip bluntness is increased, the onset of boundary layer transition over a circular cone at zero angle of attack shifts downstream. However, this trend is reversed at sufficiently large values of the nose Reynolds number, so that the transition onset location eventually moves upstream with a further increase in nose-tip bluntness. Because modal amplification is too weak to initiate transition at moderate-to-large bluntness values, nonmodal growth has been investigated as the potential basis for a physics-based model for the frustum transition. The present analysis investigates the nonmodal growth of traveling disturbances initiated within the nose-tip vicinity that peak within the entropy layer. Results show that, with increasing nose bluntness, both planar and oblique traveling disturbances experience appreciable energy amplification up to successively higher frequencies. For moderately blunt cones, the initial nonmmodal growth is followed by a partial decay that is more than overcome by an eventual, modal growth as Mack-mode waves. For larger bluntness values, the Mack-mode waves are not amplified anywhere upstream of the experimentally measured transition location, but the traveling modes still undergo a significant amount of nonmodal growth. This finding does not provide a definitive link between optimal growth and the onset of transition, but it is qualitatively consistent with the experimental observations that frustum transition in the absence of sufficient Mack-mode amplification implies a double peak in disturbance amplification and the appearance of transitional events above the boundary-layer edge.

Hypersonic speed↗