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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Upstream and Downstream Boundary Conditions Control the Physical and Biological Development of River Deltas

Relative sea level rise is depleting coastal land. Success in coastal restoration depends on understanding interactions between physical and biological processes that influence landscape change. We present results from flume experiments examining the coevolution of vegetation and delta channel networks. We show that channel mobility—controlled by upstream and downstream boundary conditions and delta size—controls vegetation colonization. The spatial patterns of plants in turn control the stability and spatial distribution of channels. Experiments with low–channel mobility develop large, dense plant patches that become denser over time and stabilize channels. Experiments with more mobile channels have sparse vegetation patterns that limit vegetation growth within existing patches, maintaining sparse, patchy vegetation. Thus, the ecogeomorphic development of deltas depends on the rate of channel lateral migration and avulsion, suggesting that upstream and downstream boundary conditions control both the physical and biological evolution of coastal landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics–Informed Neural Networks of the Saint–Venant Equations for Downscaling a Large–Scale River Model

Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2018

Machine learning at the energy and intensity frontiers of particle physics

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Magnetic field mapping of inaccessible regions using physics-informed neural networks

A difficult problem concerns the determination of magnetic field components within an experimentally inaccessible region when direct field measurements are not feasible. In this paper, we propose a new method of accessing magnetic field components using non-disruptive magnetic field measurements on a surface enclosing the experimental region. Magnetic field components in the experimental region are predicted by solving a set of partial differential equations (Ampere’s law and Gauss’ law for magnetism) numerically with the aid of physics-informed neural networks (PINNs). Prediction errors due to noisy magnetic field measurements and small number of magnetic field measurements are regularized by the physics information term in the loss function. We benchmark our model by comparing it with an older method. The new method we present will be of broad interest to experiments requiring precise determination of magnetic field components, such as searches for the neutron electric dipole moment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Photoelectron spectroscopy in molecular physical chemistry

Photoelectron spectroscopy has long been a powerful method in the toolbox of experimental physical chemistry and molecular physics. Recent improvements in coincidence methods, charged-particle imaging, and electron energy resolution have greatly expanded the variety of environments in which photoelectron spectroscopy can be applied, as well as the range of questions that can now be addressed. In this Perspectives Article, we focus on selected recent studies that highlight these advances and research areas. Furthermore, the topics include reactive intermediates and new thermochemical data, high-resolution comparisons of experiment and theory using methods based on pulsed-field ionisation (PFI), and the application of photoelectron spectroscopy as an analytical tool to monitor chemical reactions in complex environments, like model flames, catalytic or high-temperature reactors.

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

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Halogenated PETN derivatives: interplay between physical and chemical factors in explosive sensitivity

Determining the factors that influence and can help predict energetic material sensitivity has long been a challenge in the explosives community. Decades of literature reports identify a multitude of factors both chemical and physical that influence explosive sensitivity; however no unifying theory has been observed. Recent work by our team has demonstrated that the kinetics of “trigger linkages” (i.e., the weakest bonds in the energetic material) showed strong correlations with experimental drop hammer impact sensitivity. These correlations suggest that the simple kinetics of the first bonds to break are good indicators for the reactivity observed in simple handling sensitivity tests. Herein we report the synthesis of derivatives of the explosive pentaerythritol tetranitrate (PETN) in which one, two or three of the nitrate ester functional groups are substituted with an inert group. Experimental and computational studies show that explosive sensitivity correlates well with Q (heat of explosion), due to the change in the number of trigger linkages removed from the starting material. In addition, this correlation appears more significant than other observed chemical or physical effects imparted on the material by different inert functional groups, such as heat of formation, heat of explosion, heat capacity, oxygen balance, and the crystal structure of the material.

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

Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.

cyber-physical security↗
DOE OSTI2015

Optical modeling and physical performances evaluations for the JT-60SA ECRF antenna

The purpose of this work is the optical modeling and physical performances evaluations of the JT-60SA ECRF launcher system. The beams have been simulated with the electromagnetic code GRASP® and used as input for ECCD calculations performed with the beam tracing code GRAY, capable of modeling propagation, absorption and current drive of an EC Gaussion beam with general astigmatism. Full details of the optical analysis has been taken into account to model the launched beams. Inductive and advanced reference scenarios has been analysed for physical evaluations in the full poloidal and toroidal steering ranges for two slightly different layouts of the launcher system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A physical model for the reverse leakage current in (In,Ga)N/GaN light-emitting diodes based on nanowires

We investigated the origin of the high reverse leakage current in light emitting diodes (LEDs) based on (In,Ga)N/GaN nanowire (NW) ensembles grown by molecular beam epitaxy on Si substrates. To this end, capacitance deep level transient spectroscopy (DLTS) and temperature-dependent current-voltage (I-V) measurements were performed on a fully processed NW-LED. The DLTS measurements reveal the presence of two distinct electron traps with high concentrations in the depletion region of the p-i-n junction. These band gap states are located at energies of 570 ± 20 and 840 ± 30 meV below the conduction band minimum. The physical origin of these deep level states is discussed. The temperature-dependent I-V characteristics, acquired between 83 and 403 K, show that different conduction mechanisms cause the observed leakage current. On the basis of all these results, we developed a quantitative physical model for charge transport in the reverse bias regime. By taking into account the mutual interaction of variable range hopping and electron emission from Coulombic trap states, with the latter being described by phonon-assisted tunnelling and the Poole-Frenkel effect, we can model the experimental I-V curves in the entire range of temperatures with a consistent set of parameters. Our model should be applicable to planar GaN-based LEDs as well. Furthermore, possible approaches to decrease the leakage current in NW-LEDs are proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

The physical mechanism on the threshold voltage temperature stability improvement for GaN HEMTs with pre-fluorination argon treatment

In this paper, a normally-off AlGaN/GaN MIS-HEMT with improved threshold voltage (V{sub TH}) thermal stability is reported with investigations on its physical mechanism. The normally-off operation of the device is achieved from novel short argon plasma treatment (APT) prior to the fluorine plasma treatment (FPT) on Al{sub 2}O{sub 3} gate dielectrics. For the MIS-HEMT with FPT only, its V{sub TH} drops from 4.2 V at room temperature to 0.5 V at 200 °C. Alternatively, for the device with APT-then-FPT process, its V{sub TH} can retain at 2.5 V at 200 °C due to the increased amount of deep-level traps that do not emit electrons at 200 °C. This thermally stable V{sub TH} makes this device suitable for high power applications. The depth profile of the F atoms in Al{sub 2}O{sub 3}, measured by the secondary ion mass spectroscopy, reveals a significant increase in the F concentration when APT is conducted prior to FPT. The X-ray photoelectron spectroscopy (XPS) analysis on the plasma-treated Al{sub 2}O{sub 3} surfaces observes higher composition of Al-F bonds if APT was applied before FPT. The enhanced breaking of Al-O bonds due to Ar bombardment assisted in the increased incorporation of F radicals at the surface during the subsequent FPT process. The Schrödinger equation of Al{sub 2}O{sub x}F{sub y} cells, with the same Al-F compositions as obtained from XPS, was solved by Gaussian 09 molecular simulations to extract electron state distribution as a function of energy. The simulation results show creation of the deeper trap states in the Al{sub 2}O{sub 3} bandgap when APT is used before FPT. Finally, the trap distribution extracted from the simulations is verified by the gate-stress experimental characterization to confirm the physical mechanism described.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A physics-based solver to optimize the illumination of cylindrical targets in spherically distributed high power laser systems

In recent years, our understanding of high energy density plasmas has played an important role in improving inertial fusion confinement and in emerging new fields of physics, such as laboratory astrophysics. Every new idea required developing innovative experimental platforms at high power laser facilities, such as OMEGA or NIF. These facilities, designed to focus all their beams onto spherical targets or hohlraum windows, are now required to shine them on more complex targets. While the pointing on planar geometries is relatively straightforward, it becomes problematic for cylindrical targets or target with more complex geometries. This publication describes how the distribution of laser beams on a cylindrical target can be done simply by using a set of physical laws as a pointing procedure. The advantage of the method is threefold. First, it is straightforward, requiring no mathematical enterprise besides solving ordinary differential equations. Second, it will converge if a local optimum exists. Finally, it is computationally inexpensive. Experimental results show that this approach produces a geometrical beam distribution that yields cylindrically symmetric implosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

X-point ion orbit physics in scrape-off layer and generation of a localized electrostatic potential perturbation around X-point

It has been well-known that the logarithmically vanishing poloidal magnetic field strength Bθ near the magnetic X-point in a tokamak plasma yields the so-called ion X-loss or X-transport of ions when the ion temperature is high enough [C.S. Chang et al., Phys. Plasmas 9, 3884 (2002)], which has a notable impact on the physics just inside the magnetic separatrix surface; e.g., most prominently, to force a well-structure in the global negative radial electric field hence a pressure pedestal buildup. In this brief communication, we show that a similar effect exists in the scrape-off (SOL) layer near the X-point that can create a localized electrostatic potential structure around the X-point and have a significant impact on the near-SOL physics in the case when the ion dynamics are not collision-dominated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Physics of picosecond pulse laser ablation

This study investigates the physical processes involved in picosecond pulse (20-28 ps FWHM) laser ablation of Al 6061, 316L stainless steel, and undoped crystalline Si ($\langle$100$\rangle$) over a range of laser wavelength (355 nm and 1064 nm) and fluence (0.1-40 J/cm 2 ). Experimental measurements of material ablation rate show enhanced removal at the 355 nm wavelength, primarily due to laser-plasma interaction (LPI) within the ablative plume that approaches an order of magnitude increase over the measured removal at 1064 nm. A transition in the ablation rate at 355 nm is identified around ~10 J/cm 2 above which the removal efficiency increases by a factor of two to three. Multi-physics radiation hydrodynamic simulations, considering LPI effects and utilizing a novel mixed-phase equation of state model, show that the transition in ablation efficiency is due to the onset of melt ejection through cavitation, where laser-driven shock heating sets the depth of melt penetration and the ensuing release wave from the ablation surface drives cavitation through the imposition of tensile strain within the melt. High-speed pump-probe imaging of the ejecta and ejecta collection studies, as well as scanning electron microscopy of the ablation craters, support the proposed cavitation mechanism in the higher fluence range. The ablation process is vitally influenced by LPI effects and the thermophysical properties of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The chemical physics of sequential infiltration synthesis—A thermodynamic and kinetic perspective

Sequential infiltration synthesis (SIS) is an emerging materials growth method by which inorganic metal oxides are nucleated and grown within the free volume of polymers in association with chemical functional groups in the polymer. SIS enables the growth of novel polymer-inorganic hybrid materials, porous inorganic materials, and spatially templated nanoscale devices of relevance to a host of technological applications. Although SIS borrows from the precursors and equipment of atomic layer deposition (ALD), the chemistry and physics of SIS differ in important ways. These differences arise from the permeable three-dimensional distribution of functional groups in polymers in SIS, which contrast to the typically impermeable two-dimensional distribution of active sites on solid surfaces in ALD. In SIS, metal-organic vapor-phase precursors dissolve and diffuse into polymers and interact with these functional groups through reversible complex formation and/or irreversible chemical reactions. In this perspective, we describe the thermodynamics and kinetics of SIS and attempt to disentangle the tightly coupled physical and chemical processes that underlie this method. Here, we discuss the various experimental, computational, and theoretical efforts that provide insight into SIS mechanisms and identify approaches that may fill out current gaps in knowledge and expand the utilization of SIS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Physics of E × B discharges relevant to plasma propulsion and similar technologies

This paper provides perspectives on recent progress in understanding the physics of devices in which the external magnetic field is applied perpendicular to the discharge current. This configuration generates a strong electric field that acts to accelerate ions. The many applications of this set up include generation of thrust for spacecraft propulsion and separation of species in plasma mass separation devices. These “E × B” plasmas are subject to plasma–wall interaction effects and to various micro- and macroinstabilities. In many devices we also observe the emergence of anomalous transport. This perspective presents the current understanding of the physics of these phenomena and state-of-the-art computational results, identifies critical questions, and suggests directions for future research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗