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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Cosmic-ray lifetime in the Galaxy - Experimental results and models

The separation of the cosmic-ray Be isotopes achieved by two satellite experiments is discussed. From the measured isotopic ratio between the radioactive Be-10 (half-life = 1,500,000 years) and the stable Be-9, it is deduced that the cosmic rays propagate through matter with an average density of 0.24 = or - 0.07 atoms/ cu cm. The implications of this result for the cosmic-ray age is reviewed in the context of two models of confinement and propagation: the homogeneous model, normally identified with confinement ot the galactic gaseous disk, and a diffusion model in which the cosmic rays extend into a galactic halo. A newly deduced cosmic-ray pathlength distribution, a self-consistent model of solar modulation, and a set of fragmentation cross sections are calculated. It is concluded that in a homogeneous model, the cosmic-ray age is 15 (+7, -4) million years, about a factor longer than early estimates based on the abundances of the light nuclei Li, Be, and B and a nominal interstellar density of 1 atom/ cu cm.

Simpson, J. A.↗
NASA NTRS1993

A neural based intelligent flight control system for the NASA F-15 flight research aircraft

A flight control concept that can identify aircraft stability properties and continually optimize the aircraft flying qualities has been developed by McDonnell Aircraft Company under a contract with the NASA-Dryden Flight Research Facility. This flight concept, termed the Intelligent Flight Control System, utilizes Neural Network technology to identify the host aircraft stability and control properties during flight, and use this information to design on-line the control system feedback gains to provide continuous optimum flight response. This self-repairing capability can provide high performance flight maneuvering response throughout large flight envelopes, such as needed for the National Aerospace Plane. Moreover, achieving this response early in the vehicle's development schedule will save cost.

Urnes, James M.↗
NASA NTRS2016

Probing Acceleration and Turbulence at Relativistic Shocks in Blazar Jets

Diffusive shock acceleration (DSA) at relativistic shocks is widely thought to be an important acceleration mechanism in various astrophysical jet sources, including radio-loud active galactic nuclei such as blazars. Such acceleration can produce the non-thermal particles that emit the broad-band continuum radiation that is detected from extragalactic jets. An important recent development for blazar science is the ability of Fermi-Large Area Telescope spectroscopy to pin down the shape of the distribution of the underlying non-thermal particle population. This paper highlights how multiwavelength spectra spanning optical to X-ray to gamma-ray bands can be used to probe diffusive acceleration in relativistic, oblique, magnetohydrodynamic (MHD) shocks in blazar jets. Diagnostics on the MHD turbulence near such shocks are obtained using thermal and non-thermal particle distributions resulting from detailed Monte Carlo simulations of DSA. These probes are afforded by the characteristic property that the synchrotron vF(sub v), peak energy does not appear in the gamma-ray band above 100 MeV. We investigate self-consistently the radiative synchrotron and inverse Compton signatures of the simulated particle distributions. Important constraints on the diffusive mean free paths of electrons, and the level of electromagnetic field turbulence are identified for three different case study blazars, Mrk 501, BL Lacertae and AO 0235+164. The X-ray excess of AO 0235+164 in a flare state can be modelled as the signature of bulk Compton scattering of external radiation fields, thereby tightly constraining the energy-dependence of the diffusion coefficient for electrons. The concomitant interpretations that turbulence levels decline with remoteness from jet shocks, and the probable significant role for non-gyroresonant diffusion, are posited.

Baring, Matthew G.↗
NASA NTRSDate not supplied

Data & Reasoning Fabric: Minimum Viable Product

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. DRF activities will identify, test and - as needed - research and develop critical core technologies. In order to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end users.

UAM↗
NASA NTRS2023

Huntsville Urban Development II: Utilizing NASA Earth Observations to Map the Urban Heat Island and Evaluate Vulnerability in Huntsville, Alabama

Huntsville, Alabama has seen a boom in growth over recent years. One consequence of this urban expansion is the exacerbation of the Urban Heat Island (UHI) effect across the city. This project identified the areas within Huntsville the greatest potential for heat reduction and community health benefits from tree-planting efforts. The team created maps of land surface temperature (LST), the normalized difference vegetation index (NDVI), and the normalized difference built-up index (NDBI) over June through August from 2019 to 2022 using data from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 9 OLI-2 and TIRS-2. The team identified areas with high LST, low NDVI, and high NDBI as areas with the greatest potential for heat reduction via tree-planting. Social factors relating to age, race, income, and self-reported health were adapted from Tree Equity Score to map community need for tree cover. When combining social with environmental factors, the team determined areas with the greatest potential for UHI mitigation: west-central and north downtown Huntsville. The team’s partner organization, the City of Huntsville, can use this priority map to guide their future tree-planting, and weigh the factors assessed according to their preference.

James Karroum↗
DOE OSTI2021

J eff = $\frac{3}{2}$ metallic phase and unconventional superconductivity in GaTa 4 Se 8

By means of density functional theory plus dynamical mean-field theory (DFT+DMFT) calculations and resonant inelastic x-ray scattering (RIXS) experiments, we investigate the high-pressure phases of the spin-orbit-coupled Jeff = 3/2 insulator GaTa 4 Se 8 . Its metallic phase, derived from the Mott state by applying pressure, is found to carry J eff = 3/2 moments. The characteristic excitation peak in the RIXS spectrum maintains its destructive quantum interference of J eff at the Ta L2-edge up to 10.4 GPa. Our exact diagonalization based DFT+DMFT calculations including spin-orbit coupling also reveal that the J eff = 3/2 character can be clearly identified under high pressure. These results establish the intriguing nature of the correlated metallic magnetic phase, which represents the first confirmed example of J eff =3/2 moments residing in a metal. They also indicate that the pressure-induced superconductivity is likely unconventional and influenced by these J eff = 3/2 moments. Based on a self-energy analysis, we furthermore propose the possibility of doping-induced superconductivity related to a spin-freezing crossover.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗
DOE OSTI2026

Multi-Artifact Analysis of Self-Admitted Technical Debt in Scientific Software

Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to identify, categorize, and evaluate scientific debt, a specialized form of SATD in SSW, and assess the extent to which traditional SATD categories capture these domain-specific issues. Method: We conduct a multi-artifact analysis across code comments, commit messages, pull requests, and issue trackers from 23 open-source SSW projects. We construct and validate a curated dataset of scientific debt, develop a multi-source SATD classifier to guide SATD management, and conduct a practitioner validation to assess the practical relevance of scientific debt. Results: Our classifier performs strongly across 900,358 artifacts from 23 SSW projects. SATD is most prevalent in pull requests and issue trackers, underscoring the value of multi-artifact analysis. Models trained on traditional SATD often miss scientific debt, emphasizing the need for its explicit detection in SSW. Practitioner validation confirmed that scientific debt is both recognizable and useful in practice. Conclusions: Scientific debt represents a unique form of SATD in SSW that that is not adequately captured by traditional categories and requires specialized identification and management. Our dataset, classification analysis, and practitioner validation results provide the first formal multi-artifact perspective on scientific debt, highlighting the need for tailored SATD detection approaches in SSW.

Melin, Eric [Boise State University]↗
DOE OSTI2025

Model form and sensitivity analysis of CALPHAD-based nucleation models in b-stabilized Ti alloys

Accurate prediction of α-phase nucleation and growth in β-stabilized titanium alloys is crucial for designing heat treatments to optimize mechanical properties in additively manufactured lightweight components. Ideally, predictions of nucleation and growth would incorporate both top-down observations of past experimental heat treatments and bottom-up modeling of phase transformations; however, the appropriate method of combining these information sources is not self-evident. Combining top-down and bottom-up information requires a unified form of model that can connect between spatiotemporal scales, as well as sets of fitting parameters that can be identified by each data source. The selection of which parameters to fit to which data source can be made based on expert opinion, or by performing a sensitivity analysis. In solid-solid nucleation, direct observation of the nucleation and growth process is challenging. Most data on the heat treatment-controlled phase transformations are not in-situ. To predict the process and outcome of the nucleation, growth and coarsening of precipitates, theoretical models of the nucleation pathway are used to bridge the gap. Many sources of uncertainty affect the modeling of this nucleation process. It can be influenced by small variations in the thermomechanical processing history, chemical composition, and initial microstructure. If molecular dynamics (MD) simulations are used to determine thermodynamic quantities and inform CALPHAD modeling, additional uncertainty can be introduced and accounted for using Bayesian methods. Top-down uncertainties require additional steps to quantify. The influence of nucleation model form on the sensitivity of predictions to input parameters and physical conditions is the focus of this study. Classical nucleation theory (CNT) allows modeling to formulate the nucleation as homogeneous or, more commonly, heterogeneous. Non-classical nucleation models are also increasingly explored as a means of reconciling top-down and bottom-up data. In this study, the sensitivity of the intragranular nucleation of α in a β-annealed, slow-cooled aging (BASCA) heat treatment of β-stabilized Ti5553 alloy is explored using CNT and both heterogeneous and homogeneous assumptions. The Kampmann-Wagner Numerical model of precipitate nucleation and growth is employed. Using open-source tools (pyCalphad and thermodynamic modeling of TiMo as a surrogate system, a sensitivity analysis is performed to measure variations in key parameters, including chemical driving force, interfacial energy, and diffusivity, as they relate to predictions of precipitate number density. The inclusion of top-down and bottom-up data in selection of nucleation model form is discussed.

Rodriguez Negron, A. M.↗
DOE OSTI2025

Q3 Report for FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

This report describes the work and activities carried out towards the completion of each of the following milestones in FY25 Q3: 1. Demonstrate workflow for generating self-consistent CESOL plasma profiles + first wall and divertor loading prediction and generate the CAT plasma and neutron loading needed for further engineering analysis. • Benchmark between two first wall heat flux mapping methods, identify importance of various heat flux sources and physics impact of using fully coupled CESOL vs post-analysis evaluation. 2. Generate medium fidelity parametrized CAD. • Generate parametrized CAD components for the CAT example case via either user-defined modules called within the geometry generation or by defeatured/parametrized CAD, including DCLL blanket matched to divertor boundary and magnets. Define materials, labels, and boundary conditions for passing the mesh to CFD tools. 3. Demonstrate multiphysics magnet analysis. • Demonstrate magnet analysis workflow called from the FREDA workflow, and 4. Demonstrate nuclear analysis. • Add model to OpenFOAM and/or other codes possibly including Diablo to account for tritium diffusion in solids.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2005

From Here to Autonomicity: Self-Managing Agents and the Biological Metaphors that Inspire Them

We seek inspiration for self-managing systems from (obviously, pre-existing) biological mechanisms. Autonomic Computing (AC), a self-managing systems initiative based on the biological metaphor of the autonomic nervous system, is increasingly gaining momentum as the way forward for integrating and designing reliable systems, while agent technologies have been identified as a key enabler for engineering autonomicity in systems. This paper looks at other biological metaphors such as reflex and healing, heart- beat monitors, pulse monitors and apoptosis for assisting in the realization of autonomicity.

Sterritt, Roy↗
NASA NTRS2017

Principles of Sociology in Systems Engineering

Systems engineering involves both the integration of the system and the integration of the disciplines which develop and operate the system. Integrating the disciplines is a sociological effort to bring together different groups, often with different terminology, to achieve a common goal, the system. The focus for the systems engineer is information flow through the organization, between the disciplines, to ensure the system is developed and operated with all relevant information informing system decisions. Robert K. Merton studied the sociological principles of the sciences and the sociological principles he developed apply to systems engineering. Concepts such as specification of ignorance, common terminology, opportunity structures, role-sets, and the reclama (reconsideration) process are all important sociological approaches that should be employed by the systems engineer. In bringing the disciplines together, the systems engineer must also be wary of social ambivalence, social anomie, social dysfunction, insider-outsider behavior, unintended consequences, and the self-fulfilling prophecy. These sociological principles provide the systems engineer with key approaches to manage the information flow through the organization as the disciplines are integrated and share their information. This also helps identify key sociological barriers to information flow through the organization. This paper will discuss this theoretical basis for the application of sociological principles to systems engineering.

Watson, Michael D.↗
NASA NTRS2019

PROCSIMA: Diffractionless Beamed Propulsion for Breakthrough Interstellar Missions

The following report describes a new propulsion concept based on self-guiding of combined light and particle beam and explores the physics, technology and design principles needed to implement such a system for an interstellar fly-by mission to Proxima b. While the relevant self-focusing mechanism has been considered in an optical context, this is the first application to space propulsion known to the authors.The purpose of the present study is to provide a broad overview of the pertinent physics and design principles, credibly assess propulsion capabilities, and lay a comprehensive foundation for further, more targeted investigations of critical system elements and processes. Starting from basic principles, this report describes the equations of motion and physical phenomena needed to establish the feasibility of self-guiding and furthermore analyze the production and sustainment of the self-guided beam. Compared with laser or particle beam propulsion alone, the self-guided beam concept introduces a plethora of light-matter interactions and additional complexities, imposing certain constraints on the geometric and physical characteristics of the beam sources. In particular, we have the identified the particle beam as a crucial element of the proposed concept. System constraints are quantitatively analyzed and then explored by developing and applying a mission design process to a Proxima b flyby mission as well as a nearer-term mission to the solar gravitational lens point.

Mechanism↗
DOE OSTI2021

Extensins: Self-Assembly, Crosslinking, and the Role of Peroxidases

The extensin (EXT) network is elaborated by the covalent intermolecular crosslinking of EXT glycoprotein monomers, and its proper assembly is important for numerous aspects of basic wall architecture and cellular defense. In this review, we discuss new advances in the secretion of EXT monomers and the molecular drivers of EXT network self-assembly. Many of the functions of EXTs are conferred through covalent crosslinking into the wall, so we also discuss the different types of known intermolecular crosslinks, the enzymes that are involved, as well as the potential for additional crosslinks that are yet to be identified. EXTs also function in wall architecture independent of crosslinking status, and therefore, we explore the role of non-crosslinking EXTs. As EXT crosslinking is upregulated in response to wounding and pathogen infection, we discuss a potential regulatory mechanism to control covalent crosslinking and its relationship to the subcellular localization of the crosslinking enzymes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Resilient Hydrogels from the Nanoscale to the Macroscale

Biological systems illustrate how a material composed of fragile molecular components can collectively be highly resilient. While the average protein, cell, or even tissue may not last more than a few weeks, many animals and plants live for more than a century. Continual component regeneration and multiple systems to resist mechanical and chemical damage together make this longetivity possible. This project sought to develop biomimetic methods to enable a specific type of material, a hydrogel, to resist damage across multiple scales using distinct, modular damage protection mechanisms. Because hydrogels share many features with biological tissues, they are an ideal substrate for exploring biomimetic strategies for designing resilience and self-repair. We specifically focused in this study on DNA-crosslinked hydrogels, which contain DNA strands that can serve as material to link it together or to control its current state or properties. We investigated how new tools from dynamic DNA nanotechnology could make it possible to actively recover from damage by continually growing and forming a materials shape, or by identifying damage and directing an adaptive response to that damage involving chemical synthesis to reconstruct a structure. We developed embedded molecular sensors able to detect and strain of the gel before damage occurred and react to counteract damage. We also developed methods to continually create shapes and patterns using chemical processes that will allow those patterns to reform when they are damaged. The ability to design resilient materials capable of self-repair has important implications for materials engineering. Instead of designing a material to withstand the worst stresses it may encounter, we could instead design a material to survive under average conditions, but self-repair or reconfigure to resist impending damage. Resilient, self-repairing hydrogels will also have diverse applications such as sensors or actuators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Variable Laser Ignition Pathways in Al/Pt Reactive Multilayers across 10 Decades of Pulse Duration

Pulsed laser irradiation is used to investigate the local initiation of rapid, self-propagating formation reactions in Al/Pt multilayers. The single pulse direct laser ignition of these 1.6 μm thick freestanding foils was characterized over 10 decades of pulse duration (10 ms to 150 fs). Finite element, reactive heat transport modeling of the near-threshold conditions has identified three distinct ignition pathways. For milli- to microsecond pulses, ignition occurs following sufficient absorption of laser energy to enable diffusion of Al and Pt between layers such that the heat released from the corresponding exothermic reaction overcomes conductive losses outside the laser-irradiated zone. When pulse duration is decreased into the nanosecond regime, heat is concentrated near the surface such that the Al locally melts, and a portion of the top-most bilayers react initially. The favorable kinetics and additional heat enable ignition. Further reducing pulse duration to hundreds of femtoseconds leads to a third ignition pathway. In this work, while much of the energy from these pulses is lost to ablation, the remaining heat beneath the crater can be sufficiently concentrated to drive a transverse self-propagating reaction, wherein the heat released from mixing at each interface occurs under kinetic conditions capable of igniting the subsequent layer.

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

Soft crystal martensites: An in situ resonant soft x-ray scattering study of a liquid crystal martensitic transformation

Liquid crystal blue phases (BPs) are three-dimensional soft crystals with unit cell sizes orders of magnitude larger than those of classic, atomic crystals. The directed self-assembly of BPs on chemically patterned surfaces uniquely enables detailed in situ resonant soft x-ray scattering measurements of martensitic phase transformations in these systems. The formation of twin lamellae is explicitly identified during the BPII-to-BPI transformation, further corroborating the martensitic nature of this transformation and broadening the analogy between soft and atomic crystal diffusionless phase transformations to include their strain-release mechanisms.

36 MATERIALS SCIENCE↗