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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Linear shaped-charge jet optimization using machine learning methods

Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. In conclusion, we are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

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

Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo

Characterizing blood flow dynamics in vivo is critical to understanding the function of the vascular network under physiological and pathological conditions. Existing methods for hemodynamic imaging have insufficient spatial and temporal resolution to monitor blood flow at the cellular level in large blood vessels. By using an ultrafast line-scanning module based on free-space angular chirped enhanced delay, we achieved two-photon fluorescence imaging of cortical blood flow at 1,000 two-dimensional (2D) frames and 1,000,000 one-dimensional line scans per second in the awake mouse. This orders-of-magnitude increase in temporal resolution allowed us to measure cerebral blood flow at up to 49 mm/s and observe pulsatile blood flow at harmonics of heart rate. Directly visualizing red blood cell (RBC) flow through vessels down to >800 µm in depth, we characterized cortical layer–dependent flow velocity distributions of capillaries, obtained radial velocity profiles and kilohertz 2D velocity mapping of multifile blood flow, and performed RBC flux measurements from penetrating blood vessels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need

The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limited by the accuracy of the energies and atomic forces in the training dataset. Unfortunately, most of these datasets are computed with relatively low-accuracy QM methods, e.g. density functional theory with a moderate basis set. Due to the increased computational cost of more accurate QM methods, e.g. coupled-cluster theory with a complete basis set (CBS) extrapolation, most high-accuracy datasets are much smaller and often do not contain atomic forces. The lack of high-accuracy atomic forces is quite troubling, as training with force data greatly improves the stability and quality of the MLIP compared to training to energy alone. Because most datasets are computed with a unique level of theory, traditional single-fidelity (SF) learning is not capable of leveraging the vast amounts of published QM data. In this study, we apply multi-fidelity learning (MFL) to train an MLIP to multiple QM datasets of different levels of accuracy, i.e. levels of fidelity. Specifically, we perform three test cases to demonstrate that MFL with both low-level forces and high-level energies yields an extremely accurate MLIP—far more accurate than a SF MLIP trained solely to high-level energies and almost as accurate as a SF MLIP trained directly to high-level energies and forces. Therefore, MFL greatly alleviates the need for generating large and expensive datasets containing high-accuracy atomic forces and allows for more effective training to existing high-accuracy energy-only datasets. Indeed, low-accuracy atomic forces and high-accuracy energies are all that are needed to achieve a high-accuracy MLIP with MFL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compensation of Anisotropy in Spin Hall Devices for Neuromorphic Applications

Spintronic nano-oscillators and diodes with reduced nonlinearity benefit from low phase noise and improved device properties. Moreover, they could offer key advantages for realizing neuromorphic applications, such as spike-based neurons and frequency multiplexing in neural networks. Here, in this work, we experimentally demonstrate the reduction in nonlinearity of a spin Hall nano-oscillator (SHNO) by compensation of its effective magnetic anisotropy. The study involves optimization of Co/Ni multilayer growth to achieve the compensation, followed by spin-diode measurements on patterned microstrips to quantify their anisotropy. The relationship between the second- (H k 2 = 0.47 mT) and first-order ($H^{eff}_{k_1}$ = -0.8 mT) anisotropy fields reveals the existence of an easy cone, thereby validating the presence of compensation. Furthermore, we demonstrate a compensated spin diode that has a fixed frequency when the input power is varied. We then study the current-induced auto-oscillation properties of SHNOs on compensated films by patterning nanoconstrictions of 200 and 100 nm wide. The invariance of the resonance frequency and linewidth of the compensated SHNO with applied dc current indicates the absence of nonlinearity. This independence is maintained irrespective of the applied external fields and their orientations. The compensated SHNO obtained has a linewidth of 1.1 MHz and a peak output power of up to 1 pW/MHz.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Flow-Driven Branching in a Frangible Porous Medium

Channel formation and branching is widely seen in physical systems where movement of fluid through a porous structure causes the spatiotemporal evolution of the medium. We provide a simple theoretical framework that embodies this feedback mechanism in a multiphase model for flow through a frangible porous medium with a dynamic permeability. Numerical simulations of the model show the emergence of branched networks whose topology is determined by the geometry of external flow forcing. Furthermore, this allows us to delineate the conditions under which splitting and/or coalescing branched network formation is favored, with potential implications for both understanding and controlling branching in soft frangible media.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nuclear Responses with Neural-Network Quantum States

We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. Here, we demonstrate that a relatively simple nuclear Hamiltonian—based on a leading-order pionless EFT expansion and known to accurately reproduce ground-state energies of nuclei with 𝐴 ≤ 40—also provides a reliable description of the photoabsorption cross section.

Ab initio calculations↗
DOE OSTI2022

Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.

Ozmen, Ozgur↗
DOE OSTI2023

Clustering Acoustic Background Noise in the Stratosphere Using Machine Learning

Infrasound, characterized by low-frequency sound inaudible to humans (<20 Hz), emanates from natural and anthropogenic sources. Its efficacy for monitoring phenomena necessitates robust sensing networks. Traditional ground-based infrasound sensors have limitations due to atmospheric dynamics and noise interference. Balloon-bore sensors have emerged as an alternative, offering reduced noise and improved capabilities. This study bridges clustering algorithms with balloon borne infrasound data, a domain yet to be explored. Employing K-Means, DBSCAN, and GMM algorithms on normalized and reshaped data and only normalized data from a New Zealand-based NASA balloon flight, insights into background noise at stratospheric altitudes were revealed. Despite challenges arising from distinguishing signals amid unique background noise, this research provides vital reference material for noise analysis and calibration. Beyond infrasound event capture, the dataset enriches comprehension of background noise characteristics in the southern hemisphere.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Zero Trust Cybersecurity: Concepts and Models for Application

Zero Trust is a cybersecurity paradigm centered on the idea that a network breach is inevitable and so no user or asset should be implicitly trusted. Entities on the network are continuously monitored and access-granting decisions are based on dynamic risk assessment using multiple inputs. To limit the damage from an attack, privileges and lateral access are constrained by default. This report provides an overview of current models and constructs employed in building out these concepts into a zero trust architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surrogate Modeling of Landau Damping with Deep Operator Networks

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

plasma astrophysics↗
DOE OSTI2025

Nuclear responses with neural-network quantum states

We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. We demonstrate that a simple nuclear Hamiltonian, based on a leading-order pionless effective field theory expansion and known to accurately reproduce the ground-state energies of nuclei with $A\leq 20$ nucleons also provides a reliable description of the photoabsorption cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1980

Large eddy simulation of turbulent channel flow: ILLIAC 5 calculation

The capabilities of large eddy simulation in the prediction and analyses of wall-bounded turbulent shear flows are demonstrated. The dynamical equations for large scale field motions are derived. The computational grid network is described and its relation to the observed physical length scales in the flow are discussed. Some aspects of the mechanics and structure of the flow are examined both in the vicinity of the wall and in regions away from the wall. An attempt is made to correlate numerical results with laboratory observations. Other significant observations and conclusions are presented.

Kim, J.↗
NASA NTRS1983

Travelling wave effects in large space structures

Several aspects of travelling waves in Large Space Structures(LSS) are discussed. The dynamic similarity among LSS's, electric power systems, microwave circuits and communications network is noted. The existence of time lag between actuation and response is illuminated with the aid of simple examples, and their prediction is demonstrated. To prevent echoes, communications lines have matched terminations; this idea is applied to the design of dampers of one dimensional structures. Periodic structures act as mechanical band pass filters. Implications of this behavior are examined on a simple example. It is noted that the implication is twofold; continuum models of periodic lattice structures may err considerably; on the other hand, it is possible to design favorable transmission (and resonance) characteristics into the structure.

Vonflotow, A.↗
NASA NTRS1992

Implementation of an intelligent control system

A laboratory testbed facility which was constructed at NASA LeRC for the development of an Intelligent Control System (ICS) for reusable rocket engines is described. The framework of the ICS consists of a hierarchy of various control and diagnostic functions. The traditional high speed, closed-loop controller resides at the lowest level of the ICS hierarchy. Above this level resides the diagnostic functions which identify engine faults. The ICS top level consists of the coordination function which manages the interaction between an expert system and a traditional control system. The purpose of the testbed is to demonstrate the feasibility of the OCS concept by implementing the ICS as the primary controller in a simulation of the Space Shuttle Main Engine (SSME). The functions of the ICS which are implemented in the testbed are as follows: an SSME dynamic simulation with selected fault mode models, a reconfigurable controller, a neural network for sensor validation, a model-based failure detection algorithm, a rule based failure detection algorithm, a diagnostic expert system, an intelligent coordinator, and a user interface which provides a graphical representation of the event occurring within the testbed. The diverse nature of the ICS has led to the development of a distributed architecture consisting of specialized hardware and software for the implementation of the various functions. This testbed is made up of five different computer systems. These individual computers are discussed along with the schemes used to implement the various ICS components. The communication between computers and the timing and synchronization between components are also addressed.

Simon, D. L.↗
NASA NTRS1993

Application of artificial neural networks to the design optimization of aerospace structural components

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Berke, Laszlo↗
NASA NTRS1990

Auto-programmable impulse neural circuits

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Watula, D.↗
NASA NTRS1993

Trajectory design for the Deep Space Program Science Experiment (DSPSE) mission

In 1994, the Deep Space Program Science Experiment (DSPSE) spacecraft will become the first spacecraft to perform, in succession, both a lunar orbiting mission and a deep-space asteroid encounter mission. The primary mission objective is to perform a long-duration flight-test of various new-technology lightweight components, such as sensors, in a deep-space environment. The mission has two secondary science objectives: to provide high-resolution imaging of the entire lunar surface for mapping purposes and flyby imaging of the asteroid 1620 Geographos. The DSPSE mission is sponsored by the Strategic Defense Initiative Organization (SDIO). As prime contractor, the Naval Research Laboratory (NRL) is building the spacecraft and will conduct mission operations. The Goddard Space Flight Center's (GSFC) Flight Dynamics Division is supporting NRL in the areas of The Deep Space Network (DSN) will provide tracking support. The DSPSE mission will begin with a launch from the Western Test Range in late January 1994. Following a minimum 1.5-day stay in a low-Earth parking orbit, a solid kick motor burn will boost DSPSE into an 18-day, 2.5-revolution phasing orbit transfer trajectory to the Moon. Two burns to insert DSPSE into a lunar polar orbit suitable for the mapping mission will be followed by mapping orbit maintenance and adjustment operations over a period of 2 sidereal months. In May 1994, a lunar orbit departure maneuver, in conjunction with a lunar swingby 26 days later, will propel DSPSE onto a heliocentric transfer that will intercept Geographos on September 1, 1994. This paper presents the characteristics, deterministic delta-Vs, and design details of each trajectory phase of this unique mission, together with the requirements, constraints, and design considerations to which each phase is subject. Numerous trajectory plots and tables of significant trajectory events are included. Following a discussion of the results of a preliminary launch window analysis, a summary of the deterministic impulsive delta-V budget required to establish the baseline mission trajectory design is presented.

Carrington, D.↗