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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structured Neural Network Modeling for Developing Digital Twins Models of Hydropower Generation Units

Dynamic modeling is a key part in the development of digital twin (DT) for dynamic systems. This is true for hydropower systems, where whole system modeling including penstock, turbine and generators, etc is important in realizing actuate modeling for the real systems. On the other hand, in response to the large variations of the power demand due to increased penetration of renewables such as wind and solar, hydropower systems are now required to operate in a large power generation range. This situation triggers the nonlinear characteristics of the generation unit with respect to its models. As such, it is imperative to use data driven modeling such as neural networks to learn the nonlinear dynamics of the hydropower generation unit. To achieve this objective, this study constructs a modeling and learning algorithm integrated with multiple structured neural network models for the modeling of turbine shaft speed, penstock pressure, and generator power output based on the generator power control setpoint, field current, and field voltage. In addition, the study uses the hydropower data from Tacoma Public Utilities to train and validate the proposed neural network algorithm. The results have shown that this structured neural network modeling approach can learn the system dynamics effectively by using the real-time data collected from the hydropower system with the desired modeling results.

Wang, Hong↗
DOE OSTI2020

Interfacial spin–orbit torques

Spin–orbit torques offer a promising mechanism for electrically controlling magnetization dynamics in nanoscale heterostructures. While spin–orbit torques occur predominately at interfaces, the physical mechanisms underlying these torques can originate in both the bulk layers and at interfaces. Classifying spin–orbit torques based on the region that they originate in provides clues as to how to optimize the effect. While most bulk spin–orbit torque contributions are well studied, many of the interfacial contributions allowed by symmetry have yet to be fully explored theoretically and experimentally. To facilitate progress, we review interfacial spin–orbit torques from a semiclassical viewpoint and relate these contributions to recent experimental results. In this study, we show the relationship between different interface transport parameters within the same model. For charges and spins flowing perpendicular to the interface, interfacial spin–orbit coupling both modifies the mixing conductance of the magnetoelectronic circuit theory and gives rise to spin memory loss. For in-plane electric fields, interfacial spin–orbit coupling gives rise to torques described by spin–orbit filtering, spin swapping, and precession. In addition, these same interfacial processes generate spin currents that flow into the non-magnetic layer. For in-plane electric fields in trilayer structures, the spin currents generated at the interface between one ferromagnetic layer and the non-magnetic spacer layer can propagate through the non-magnetic layer to produce novel torques on the other ferromagnetic layer.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

LiF–NaF–ZrF 4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF 4 , 42LiF–29NaF–29ZrF 4 , and 26LiF–37NaF–37ZrF 4 . Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF 4 salt mixtures. It is found that as ZrF 4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li + , Na + ) obtained from RIM calculations decreases, while multivalent Zr 4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF 4 mol% salts such as in 38LiF–51NaF–11ZrF 4 , an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF 4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF 4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF 4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Structure of BTK kinase domain with the second-generation inhibitors acalabrutinib and tirabrutinib

Bruton’s tyrosine kinase (BTK) is the target of the therapeutic agent, Ibrutinib, that treats chronic lymphocyte leukemia (CLL), mantle cell lymphoma (MCL) and other B cell malignancies. Ibrutinib is a first in class, covalent BTK inhibitor that limits B-cell survival and proliferation. Designing new inhibitors of BTK has been an important objective for advancing development of improved therapeutic agents against cancer and autoimmune disorders. Based on the success of Ibrutinib, several second-generation irreversible BTK inhibitors have been developed that exhibit fewer off-target effects. However, the binding-mode and their interaction with Btk have not been experimentally determined and evaluated at atomic resolution. Here we determined the first crystal structure of the BTK kinase domain in complex with acalabrutinib. In addition, we report a structure of the BTK/tirabrutinib complex and compare these structures with previously solved structures. The structures provide insight in the superior selectivity reported for acalabrutinb and guide future BTK inhibitor development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Predicting Rupture Pressure and Leak Rate of Alloy 600 Tubes with ODSCC and Comparing with Test Results (Technical Letter Report)

This report summarizes structural integrity work performed for the U.S. NRC that predicts the rupture pressure and leak rate of flawed steam generator (SG) tubes. The work includes pressure testing of straight tubes with lab-induced outer diameter stress corrosion cracks (ODSCC). Existing models for predicting rupture pressure and leak rate of straight tubes with well-defined, rectangular, electro-discharge-machined (EDM) flaws were modified and used to predict the measured rupture pressures and leak rates from straight tubes with ODSCC. Models previously developed for EDM notches in straight tubes were modified for ODSCC by using the equivalent rectangular crack (ERC) method.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Observation of Skewed Electromagnetic Wakefields in an Asymmetric Structure Driven by Flat Electron Bunches

Relativistic charged -particle beams that generate intense longitudinal fields in accelerating structures also inherently couple to transverse modes. The effects of this coupling may lead to beam breakup instability and thus must be countered to preserve beam quality in applications such as linear colliders. Beams with highly asymmetric transverse sizes (flat beams) have been shown to suppress the initial instability in slab -symmetric structures. However, as the coupling to transverse modes remains, this solution serves only to delay instability. In order to understand the hazards of transverse coupling in such a case, we describe here an experiment characterizing the transverse effects on a flat beam, traversing near a planar dielectric lined structure. Further, the measurements reveal the emergence of a previously unobserved skew-quadrupolelike interaction when the beam is canted transversely, which is not present when the flat beam travels parallel to the dielectric surface. We deploy a multipole field fitting algorithm to reconstruct the projected transverse wakefields from the data. We generate the effective kick vector map using a simple two -particle theoretical model, with particle -in -cell simulations used to provide further insight for realistic particle distributions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Birefringent Glass‐Engraved Quasi‐Linear Nanograting Metasurface Based on Self‐Organizing Process for Large Aperture High Power Laser Applications

All-glass metasurface “nanograting” structures that exhibit birefringence in the formed layer are reported. The key enabler of this work is ion beam processing at an angle sufficiently off-normal incidence, inducing self-assembly of a deposited metal layer into quasi-linear metallic features that can function as an etching mask. As a result, a fused silica metasurface, monolithic to the underlying substrate, is demonstrated at 375 nm wavelength to exhibit a phase delay angle of 30° between the principal axes. The capability of an angled etch mask replenishment process is also demonstrated for achieving deeper etch depth and for increasing the grating period, another first – to the best of the knowledge. This is the first display of a technology capable of fabricating glass-engraved near-linear grating structure with a feature-to-feature period as small as 118.6 nm. Furthermore, this technology has the potential to generate grating-like structures with periods as small as 12.4 nm, as demonstrated here with reactive ion beam processing assisted mask assembly. Furthermore, these structures are shown to have reflectivity < 0.4% across the wavelength band 350 nm – 1000 nm. Such a technology can enable laser-durable grating structures for the deep-UV and even down to soft X-ray wavelengths.

Ray, Nathan J. [Lawrence Livermore National Labora↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
DOE OSTI2026

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials.

high-entropy materials↗
DOE OSTI2021

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. Here, the framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2025

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗
DOE OSTI2022

Method and system for arbitrary optical pulse generation

A system, method, and apparatus for continuous seed laser pulses supplied to a CW pumped pre-amplifier and/or power-amplifier chain comprises an optical modulator configured to impress pulse signals on an optical signal, a waveform generator configured to establish a structure of the optical signal, and a keep-alive circuit that generates a continuous electrical pulse pattern provided to the optical modulator, wherein the system provides a continuous seed laser pulse structure.

Johnson, David↗
DOE OSTI2021

Insights into the emergence of mass from studies of pion and kaon structure

There are two mass generating mechanisms in the standard model of particle physics (SM). One is related to the Higgs boson and fairly well understood. The other is embedded in quantum chromodynamics (QCD), the SM’s strong interaction piece; and although responsible for emergence of the roughly 1 GeV mass scale that characterises the proton and hence all observable matter, the source and impacts of this emergent hadronic mass (EHM) remain puzzling. As bound states seeded by a valence-quark and -antiquark, pseudoscalar mesons present a simpler problem in quantum field theory than that associated with the nucleon. Consequently, there is a large array of robust predictions for pion and kaon properties whose empirical validation will provide a clear window onto many effects of both mass generating mechanisms and the constructive interference between them. This has now become significant because new-era experimental facilities, in operation, construction, or planning, are capable of conducting such tests and thereby contributing greatly to resolving the puzzles of EHM. These aspects of experiment, phenomenology, and theory, along with contemporary successes and challenges, are reviewed herein. In addition to providing an overview of the experimental status, we focus on recent progress made using continuum Schwinger function methods and lattice-regularised QCD. Further, advances made using other theoretical tools are also sketched. Our primary goal is to highlight the potential gains that can accrue from a coherent effort aimed at finally reaching an understanding of the character and structure of Nature’s Nambu–Goldstone modes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Hollow plasma structure during the breakdown phase of nanosecond pulsed pin–pin discharges generated with overvoltage

Atmospheric pressure nanosecond repetitive pulsed discharges are of interest for applications in combustion, material processing, plasma medicine and gas treatment. Here, the observation of a hollow structure at the ignition stage of such discharge in a pin–pin geometry is reported. Plasma images show that the phenomenon occurs in a variety of gases (helium, humid helium, argon and air) suggesting that it is in first approximation non-species specific. The phenomenon is investigated in detail in pure helium discharges for peak applied voltages between 3 and 6 kV and 200 ns voltage pulse duration by performing time-resolved optical emission spectroscopy measurements of electron density, gas temperature and electric field. All the measurements were performed at the mid-gap during the early stages of the discharge, near the breakdown. The transition from a homogeneous filament to a hollow structure occurs between 4 and 5 kV in helium. Electron density measurements by Stark broadening of the He I line at 492.2 nm show no significant difference in electron densities for the applied voltage between 3 and 6 kV. In all cases, a maximum electron density of about 3.5 × 10 21 m –3 was found. Electric field measurements performed by Stark polarization spectroscopy of the He I transition at 492.2 nm and of its forbidden counterpart show electric field strengths higher than the DC breakdown field of helium at atmospheric pressure consistent with the generation of the discharges with overvoltage. Moreover, a correlation between electric field values and the appearance of the hollow structure is observed and, remarkably, the phenomenon is not sensitive to the pre-ionization level (memory effect). Possible mechanisms for the phenomenon are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Nitrogen effects on radiation response in 12Cr ferritic/martensitic alloys

Ferritic/martensitic steels are one of the best candidates for structural materials for high dose applications in next generation nuclear reactors. The composition of structural materials must be optimized for reliable service during irradiation. This study reports the effect of interstitial nitrogen on radiation response in 12Cr ferritic/martensitic HT9 steels having a controlled nitrogen concentration. Results show that a high amount of ‘free’ nitrogen in the matrix stabilizes the interstitial clusters which leads to (i) larger loop sizes (ii) lower loop density and (iii) slightly reduced radiation induced hardening. It also affects diffusion mechanism of Ni and formation of Ni/Si-rich precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-power terahertz pulse generation from bias-free nanoantennas on graded composition InGaAs structures

We present a bias-free photoconductive emitter that uses an array of nanoantennas on an InGaAs layer with a linearly graded Indium composition. The graded InGaAs structure creates a built-in electric field that extends through the entire photoconductive active region, enabling the efficient drift of the photo-generated electrons to the nanoantennas. The nanoantenna geometry is chosen so that surface plasmon waves are excited in response to a 1550 nm optical pump to maximize photo-generated carrier concentration near the nanoantennas, where the built-in electric field strength is maximized. With the combination of the plasmonic enhancement and built-in electric field, high-power terahertz pulses are generated without using any external bias voltage. We demonstrate the generation of terahertz pulses with 860 µW average power at an average optical pump power of 900 mW, exhibiting the highest radiation power compared to previously demonstrated telecommunication-compatible terahertz pulse emitters.

42 ENGINEERING↗