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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

MODELING THE VARIATIONS OF DOSE RATE MEASURED BY RAD DURING THE FIRST MSL MARTIAN YEAR: 2012–2014

The Radiation Assessment Detector (RAD), on board Mars Science Laboratory’s (MSL) rover Curiosity, measures the energy spectra of both energetic charged and neutral particles along with the radiation dose rate at the surface of Mars. With these first-ever measurements on the Martian surface, RAD observed several effects influencing the galactic cosmic-ray (GCR) induced surface radiation dose concurrently: (a) short-term diurnal variations of the Martian atmospheric pressure caused by daily thermal tides, (b) long-term seasonal pressure changes in the Martian atmosphere, and (c) the modulation of the primary GCR flux by the heliospheric magnetic field, which correlates with long-term solar activity and the rotation of the Sun. The RAD surface dose measurements, along with the surface pressure data and the solar modulation factor, are analyzed and fitted to empirical models that quantitatively demonstrate how the long-term influences ((b) and (c)) are related to the measured dose rates. Correspondingly, we can estimate dose rate and dose equivalents under different solar modulations and different atmospheric conditions, thus allowing empirical predictions of the Martian surface radiation environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

VARIATIONS BETWEEN DUST AND GAS IN THE DIFFUSE INTERSTELLAR MEDIUM

Using the Planck far-infrared and Arecibo GALFA 21 cm line surveys, we identified a set of isolated interstellar clouds (approximately degree-sized on the sky and comprising 100 solar masses) and assessed the ratio of gas mass to dust mass. Significant variations of the gas/dust ratio are found both from cloud to cloud and within regions of individual clouds; within the clouds, the atomic gas per unit dust decreases by more than a factor of 3 compared with the standard gas/dust ratio. Three hypotheses are considered. First, the apparently low gas/dust ratio could be due to molecular gas. Comparing to Planck CO maps, the brightest clouds have a H{sub 2}/CO ratio comparable to Galactic plane clouds, but a strong lower limit is placed on the ratio for other clouds, such that the required amount of molecular gas is far higher than would be expected based on the CO upper limits. Second, we consider self-absorbed 21 cm lines and find that the optical depth must be ∼3, significantly higher than found from surveys of radio sources. Third, grain properties may change within the clouds: they become more emissive when they are colder, while not utilizing heavy elements that already have their cosmic abundance fully locked into grains. It is possible that all three processes are active, and follow-up studies will be required to disentangle them and measure the true total gas and dust content of interstellar clouds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CHARACTERIZATION OF THE K2-19 MULTIPLE-TRANSITING PLANETARY SYSTEM VIA HIGH-DISPERSION SPECTROSCOPY, AO IMAGING, AND TRANSIT TIMING VARIATIONS

K2-19 (EPIC201505350) is an interesting planetary system in which two transiting planets with radii ∼7 R{sub ⊕} (inner planet b) and ∼4 R{sub ⊕} (outer planet c) have orbits that are nearly in a 3:2 mean-motion resonance. Here, we present results of ground-based follow-up observations for the K2-19 planetary system. We have performed high-dispersion spectroscopy and high-contrast adaptive-optics imaging of the host star with the HDS and HiCIAO on the Subaru 8.2 m telescope. We find that the host star is a relatively old (≥8 Gyr) late G-type star (T{sub eff} ∼ 5350 K, M{sub s} ∼ 0.9 M{sub ⊙}, and R{sub s} ∼ 0.9 R{sub ⊙}). We do not find any contaminating faint objects near the host star that could be responsible for (or dilute) the transit signals. We have also conducted transit follow-up photometry for the inner planet with KeplerCam on the FLWO 1.2 m telescope, TRAPPISTCAM on the TRAPPIST 0.6 m telescope, and MuSCAT on the OAO 1.88 m telescope. We confirm the presence of transit timing variations (TTVs), as previously reported by Armstrong and coworkers. We model the observed TTVs of the inner planet using the synodic chopping formulae given by Deck and Agol. We find two statistically indistinguishable solutions for which the period ratios (P{sub c}/P{sub b}) are located slightly above and below the exact 3:2 commensurability. Despite the degeneracy, we derive the orbital period of the inner planet P{sub b} ∼ 7.921 days and the mass of the outer planet M{sub c} ∼ 20 M{sub ⊕}. Additional transit photometry (especially for the outer planet) as well as precise radial-velocity measurements would be helpful to break the degeneracy and to determine the mass of the inner planet.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Realization of power law inflation & variants via variation of the strong coupling constant

We present a model of power law inflation generated by variation of the strong coupling constant. We then extend the model to two varying coupling constants which leads to a potential consisting of a linear combination of exponential terms. Some variants of the latter may be self-consistent and can accommodate the experimental data of the Planck 2015 and other recent experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

Abstract A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP’s saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Thermodynamically consistent semi-compressible fluids: a variational perspective

This paper presents (Lagrangian) variational formulations for single and multicomponent semi-compressible fluids with both reversible (entropy-conserving) and irreversible (entropy-generating) processes. Semi-compressible fluids are useful in describing low-Mach dynamics, since they are soundproof. These models find wide use in many areas of fluid dynamics, including both geophysical and astrophysical fluid dynamics. Specifically, the Boussinesq, anelastic and pseudoincompressible equations are developed through a unified treatment valid for arbitrary Riemannian manifolds, thermodynamic potentials and geopotentials. By design, these formulations obey the 1st and 2nd laws of thermodynamics, ensuring their thermodynamic consistency. This general approach extends and unifies existing work, and helps clarify the thermodynamics of semi-compressible fluids. To further this goal, evolution equations are presented for a wide range of thermodynamic variables: entropy density s, specific entropy η, buoyancy b, temperature T, potential temperature θ and a generic entropic variable χ; along with a general definition of buoyancy valid for all three semicompressible models and arbitrary geopotentials. Finally, the elliptic equation for the pressure perturbation (the Lagrange multiplier that enforces semi-compressibility) is developed for all three equation sets in the case of reversible dynamics, and for the Boussinesq/anelastic equations in the case of irreversible dynamics; and some discussion is given of the difficulty in formulating it for the pseudoincompressible equations with irreversible dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

HEIGHT VARIATION OF THE VECTOR MAGNETIC FIELD IN SOLAR SPICULES

Proving the magnetic configuration of solar spicules has hitherto been difficult due to the lack of spatial resolution and image stability during off-limb ground-based observations. We report spectropolarimetric observations of spicules taken in the He i 1083 nm spectral region with the Tenerife Infrared Polarimeter II at the German Vacuum Tower Telescope of the Observatorio del Teide (Tenerife, Canary Islands, Spain). The data provide the variation with geometrical height of the Stokes I, Q, U, and V profiles, whose encoded information allows the determination of the magnetic field vector by means of the HAZEL inversion code. The inferred results show that the average magnetic field strength at the base of solar spicules is about 80 gauss, and then it decreases rapidly with height to about 30 gauss at a height of 3000 km above the visible solar surface. Moreover, the magnetic field vector is close to vertical at the base of the chromosphere and has mid-inclinations (about 50°) above 2 Mm height.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

“SLIMPLECTIC” INTEGRATORS: VARIATIONAL INTEGRATORS FOR GENERAL NONCONSERVATIVE SYSTEMS

Symplectic integrators are widely used for long-term integration of conservative astrophysical problems due to their ability to preserve the constants of motion; however, they cannot in general be applied in the presence of nonconservative interactions. In this Letter, we develop the “slimplectic” integrator, a new type of numerical integrator that shares many of the benefits of traditional symplectic integrators yet is applicable to general nonconservative systems. We utilize a fixed-time-step variational integrator formalism applied to the principle of stationary nonconservative action developed in Galley et al. As a result, the generalized momenta and energy (Noether current) evolutions are well-tracked. We discuss several example systems, including damped harmonic oscillators, Poynting–Robertson drag, and gravitational radiation reaction, by utilizing our new publicly available code to demonstrate the slimplectic integrator algorithm. Slimplectic integrators are well-suited for integrations of systems where nonconservative effects play an important role in the long-term dynamical evolution. As such they are particularly appropriate for cosmological or celestial N-body dynamics problems where nonconservative interactions, e.g., gas interactions or dissipative tides, can play an important role.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CORONAL MASS EJECTIONS AND THE SOLAR CYCLE VARIATION OF THE SUN’S OPEN FLUX

The strength of the radial component of the interplanetary magnetic field (IMF), which is a measure of the Sun’s total open flux, is observed to vary by roughly a factor of two over the 11 year solar cycle. Several recent studies have proposed that the Sun’s open flux consists of a constant or “floor” component that dominates at sunspot minimum, and a time-varying component due to coronal mass ejections (CMEs). Here, we point out that CMEs cannot account for the large peaks in the IMF strength which occurred in 2003 and late 2014, and which coincided with peaks in the Sun’s equatorial dipole moment. We also show that near-Earth interplanetary CMEs, as identified in the catalog of Richardson and Cane, contribute at most ∼30% of the average radial IMF strength even during sunspot maximum. We conclude that the long-term variation of the radial IMF strength is determined mainly by the Sun’s total dipole moment, with the quadrupole moment and CMEs providing an additional boost near sunspot maximum. Most of the open flux is rooted in coronal holes, whose solar cycle evolution in turn reflects that of the Sun’s lowest-order multipoles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Variational quantum eigensolver for approximate diagonalization of downfolded Hamiltonians using generalized unitary coupled cluster ansatz

In this paper we discuss the utilization of Variational Quantum Solver (VQE) and recently introduced Generalized Unitary Coupled Cluster (GUCC) formalism for the diagonalization of downfolded/effective Hamiltonians in active spaces. In addition to effective Hamiltonians defined by the downfolding of a subset of virtual orbitals we also consider their form defined by freezing core orbitals, which enables us to deal with larger systems. Furthermore, we also consider various solvers to identify solutions of the GUCC equations. We use N 2 , H 2 O, and C 2 H 4 , and benchmark systems to illustrate the performance of the combined framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Variational quantum reinforcement learning via evolutionary optimization

Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach *

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and/or regression of complex experimental data. Like other ML problems, VAEs require hyperparameter tuning, e.g. balancing the Kullback–Leibler and reconstruction terms. However, the training process and resulting manifold topology and connectivity depend not only on hyperparameters, but also their evolution during training. Because of the inefficiency of exhaustive search in a high-dimensional hyperparameter space for the expensive-to-train models, here we have explored a latent Bayesian optimization (zBO) approach for the hyperparameter trajectory optimization for the unsupervised and semi-supervised ML and demonstrated for joint-VAE with rotational invariances. We have demonstrated an application of this method for finding joint discrete and continuous rotationally invariant representations for modified national institute of standards and technology database (MNIST) and experimental data of a plasmonic nanoparticles material system. The performance of the proposed approach has been discussed extensively, where it allows for any high dimensional hyperparameter trajectory optimization of other ML models.

42 ENGINEERING↗
DOE OSTI2023

Combining variational autoencoders and physical bias for improved microscopy data analysis *

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO 3 , and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The customized codes of the required functions and classes to develop phyVAE is available at https://github.com/arpanbiswas52/phy-VAE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Variational quantum simulation of the critical Ising model with symmetry averaging

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)–style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. Here, we propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

1-dimensional spin chains↗
DOE OSTI2021

Variational study of the Kitaev-Heisenberg-Gamma model

Here we compute the low-energy excitation spectrum and the dynamical spin structure factor of the Kitaev-Heisenberg-Gamma model through a variational approach based on the exact fractionalized excitations of the pure Kitaev honeycomb model. This novel approach reveals the physical reason for the asymmetric stability of the Kitaev spin liquid phases around the ferromagnetic and antiferromagnetic Kitaev limits. Moreover, we demonstrate that the fractionalized excitations form bound states in specific regions of each Kitaev spin liquid phase and that certain phase transitions induced by Heisenberg and Gamma interactions are driven by the condensation of such a bound state. Remarkably, this bound state appears as a sharp mode in the dynamical spin structure factor, while its condensation patterns at the appropriate phase transitions provide a simple explanation for the magnetically ordered phases surrounding each Kitaev spin liquid phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

An iterative reconstruction algorithm for pole figure inversion using total variation regularization

This paper describes a new discrete method for inverting X-ray pole figures to estimate the orientation distribution function (ODF). The method employs the equal-volume `cubochoric' representation for uniform discretization of orientation space, SO(3). The forward-projection model is combined with an anisotropic total variation term to iteratively determine the ODF. Here, the efficacy of the new method is evaluated with both model and experimental data and compared with existing discrete and series expansion methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Rigid registration algorithm based on the minimization of the total variation of the difference map

Image registration is broadly used in various scenarios in which similar scenes in different images are to be aligned. However, image registration becomes challenging when the contrasts and backgrounds in the images are vastly different. This work proposes using the total variation of the difference map between two images (TVDM) as a dissimilarity metric in rigid registration. A method based on TVDM minimization is implemented for image rigid registration. The method is tested with both synthesized and real experimental data that have various noise and background conditions. The performance of the proposed method is compared with the results of other rigid registration methods. It is demonstrated that the proposed method is highly accurate and robust and outperforms other methods in all of the tests. The new algorithm provides a robust option for image registrations that are critical to many nano-scale X-ray imaging and microscopy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗