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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Extracting Non-propagating Oscillatory Fields in Concrete to Detect Distributed Cracking

We introduce work to find and locate distributed subsurface cracks in concrete by extracting non-propagating oscillatory fields. The medium of interest is concrete, but the approach also applies to other types of inhomogeneous media. The theoretical basis of the work is first presented through a one-dimensional point-scatterer model that considers the wavefield set up by multiple distinct scatterers. More complex scattering scenarios are then investigated using numerical simulation. The numerical models consider two types of scatterers: elliptic large-scale particles distributed throughout a medium and small-sized cracks localized within a damage zone. The theoretical and numerical analyses show that forward propagating waves undergo distinct scattering behavior within the crack damaged zone: non-propagating resonance-like oscillatory fields are set up within the cracked zone that are distinct from the scatter caused by the large-scale particles. We employ frequency-wavenumber (f-k) domain analysis to extract the energy of non-propagating oscillatory fields and thus to detect and locate zones of distributed cracking. The proposed approach is evaluated using numerical simulation and experimental data collected from concrete specimens that contain simulated distributed cracks. The results reflect the location of distributed crack zones in discrete random media such as concrete can be successfully detected.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Using Dedal to share and reuse distributed engineering design information

The overall goal of the project is to facilitate the reuse of previous design experience for the maintenance, repair and redesign of artifacts in the electromechanical engineering domain. An engineering team creates information in the form of meeting summaries, project memos, progress reports, engineering notes, spreadsheet calculations and CAD drawings. Design information captured in these media is difficult to reuse because the way design concepts are referred to evolve over the life of a project and because decisions, requirements and structure are interrelated but rarely explicitly linked. Based on protocol analysis of the information seeking behavior of designer's, we defined a language to describe the content and the form of design records and implemented this language in Dedal, a tool for indexing, modeling and retrieving design information. We first describe the approach to indexing and retrieval in Dedal. Next we describe ongoing work in extending Dedal's capabilities to a distributed environment by integrating it with World Wide Web. This will enable members of a design team who are not co-located to share and reuse information.

Baya, Vinod↗
NASA NTRS1995

Inductive Approaches to Improving Diagnosis and Design for Diagnosability

The first research area under this grant addresses the problem of classifying time series according to their morphological features in the time domain. A supervised learning system called CALCHAS, which induces a classification procedure for signatures from preclassified examples, was developed. For each of several signature classes, the system infers a model that captures the class's morphological features using Bayesian model induction and the minimum message length approach to assign priors. After induction, a time series (signature) is classified in one of the classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. A second area of research assumes two sources of information about a system: a model or domain theory that encodes aspects of the system under study and data from actual system operations over time. A model, when it exists, represents strong prior expectations about how a system will perform. Our work with a diagnostic model of the RCS (Reaction Control System) of the Space Shuttle motivated the development of SIG, a system which combines information from a model (or domain theory) and data. As it tracks RCS behavior, the model computes quantitative and qualitative values. Induction is then performed over the data represented by both the 'raw' features and the model-computed high-level features. Finally, work on clustering for operating mode discovery motivated some important extensions to the clustering strategy we had used. One modification appends an iterative optimization technique onto the clustering system; this optimization strategy appears to be novel in the clustering literature. A second modification improves the noise tolerance of the clustering system. In particular, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus making post-clustering analysis easier.

Fisher, Douglas H.↗
NASA NTRS2016

A Flexible Method for Producing F.E.M. Analysis of Bone Using Open-Source Software

This project, performed in support of the NASA GRC Space Academy summer program, sought to develop an open-source workflow methodology that segmented medical image data, created a 3D model from the segmented data, and prepared the model for finite-element analysis. In an initial step, a technological survey evaluated the performance of various existing open-source software that claim to perform these tasks. However, the survey concluded that no single software exhibited the wide array of functionality required for the potential NASA application in the area of bone, muscle and bio fluidic studies. As a result, development of a series of Python scripts provided the bridging mechanism to address the shortcomings of the available open source tools. The implementation of the VTK library provided the most quick and effective means of segmenting regions of interest from the medical images; it allowed for the export of a 3D model by using the marching cubes algorithm to build a surface mesh. To facilitate the development of the model domain from this extracted information required a surface mesh to be processed in the open-source software packages Blender and Gmsh. The Preview program of the FEBio suite proved to be sufficient for volume filling the model with an unstructured mesh and preparing boundaries specifications for finite element analysis. To fully allow FEM modeling, an in house developed Python script allowed assignment of material properties on an element by element basis by performing a weighted interpolation of voxel intensity of the parent medical image correlated to published information of image intensity to material properties, such as ash density. A graphical user interface combined the Python scripts and other software into a user friendly interface. The work using Python scripts provides a potential alternative to expensive commercial software and inadequate, limited open-source freeware programs for the creation of 3D computational models. More work will be needed to validate this approach in creating finite-element models.

gravitational physiology↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

All-Atom Simulations Uncover Structural and Dynamical Properties of STING Proteins in the Membrane System

Recent studies have shown that the stimulator of interferon gene (STING) protein plays a central role in the immune system by facilitating the production of type I interferons in cells. The STING signaling pathway is also a prominent activator of cancer-killing T cells that initiate a powerful adaptive immune response. Since biomolecular signaling pathways are complicated and not easily identified through traditional experiments, molecular dynamics (MD) has often been used to study structural and dynamical responses of biological pathways. In this work, we carried out MD simulations for full-length chicken and human STING (chSTING and hSTING) proteins. Specifically, we investigated ligand-bound closed (holo) and ligand-unbound open (apo) forms of STING in the membrane system by comparing their conformational and dynamical differences. Our research provides clues for understanding the mechanism of the STING signaling pathway by uncovering detailed insights for the examined systems: the residues from each chain in the binding pocket are strongly correlated to one another in the open STING structure compared with those in the closed STING structure. Ligand-bound closed STING displays ~174° rotation of the ligand-binding domain (LBD) relative to the open STING structure. The dynamical analysis of residue Cys148 located in the linker region of hSTING does not support the earlier hypothesis that Cys148 can form disulfide bonds between adjacent STING dimers. We also demonstrate that using the full-length proteins is critical, since the MD simulations of the LBD portion alone cannot properly describe the global conformational properties of STING.

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

Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems

Abstract Ecosystem biomimicry is a promising pathway for sustainable development. However, while typical form- and process-level biomimicry is prevalent, system-level ecosystem biomimicry remains a nascent practice in numerous engineering fields. This critical review takes an interdisciplinary approach to synthesize trends across case studies, evaluate design methodologies, and identify future opportunities when applying ecosystem biomimicry to engineering practices, including cyber systems (CS), physical systems (PS), and cyber-physical systems (CPS). After systematically sourcing publications from major databases, the papers were first analyzed at a meta level for their bibliographic context and for statistical correlations among categorical variables. Then, we investigated deeper into the engineering applications and design methodologies. Results indicate that CPS most frequently mimic organisms and ecosystems, while CS and PS frequently mimic populations-communities and molecules-tissues-organ systems, respectively (statistically highly significant). An indirect approach is most often used for mimicry at organizational levels from populations to ecosystems, while a direct approach frequently suits levels from molecules to organisms (highly significant). Dominant themes across engineering applications include symbiotic organism search algorithms for CS and ecological network analysis for CPS, while PS are highly diverse. For design methodologies, this work summarizes and details ten well-documented biomimetic process models among literature, which addresses an outdated concern for a lack of systematic methods for ecosystem biomimicry. In addition to the Biomimetics Standard ISO 18458, these methods include the Natural Step and Techno-Ecological Synergy framework, among others. Further, the analyses revealed future opportunities from less utilized design methods (e.g. interdisciplinary teams tackling indirect, ecosystem-level projects) to well-established engineering concepts ready for technological advancement (e.g. implementing membrane computing for physical applications). For future studies, this review provides a comprehensive reference for ecosystem biomimetic design practices and application opportunities across multiple engineering domains.

42 ENGINEERING↗
DOE OSTI2019

Mechanisms of Enhanced Thermal Stability of Polarization in Lead-Free (Bi 1/2 Na 1/2 ) 0.94 Ba 0.06 TiO 3 /ZnO Ceramic Composites

(Bi 1/2 Na 1/2 )TiO 3 -based solid solutions, one of the major systems of lead-free piezoelectric ceramics, exhibit a low thermal depolarization temperature ( T d ~100°C). It was reported that by incorporating 30 mol% ZnO particles to form a ceramic composite of (Bi 1/2 Na 1/2 ) 0.94 Ba 0.06 TiO 3 /ZnO, the depolarization process can be shifted up to ~250 °C. In the present work, a variety of advanced transmission electron microscopy techniques, including in situ heating, annular bright-field, high-angle annular dark-field, geometric phase analysis, energy-dispersive spectrum and electron energy-loss spectroscopy, are employed to investigate the underlying mechanisms for the enhanced thermal stability of polarization in the composite. Furthermore, it is found that the abrupt depolarization in (Bi 1/2 Na 1/2 ) 0.94 Ba 0.06 TiO 3 at T d becomes diffused over a wide temperature window up to the temperature at maximum dielectric constant ( T m ) under the combined actions of the incorporation of Zn into the perovskite lattice, the presence of residual stresses, and the pinning effect on micron-sized domains provided by ZnO particles.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Evolving software reengineering technology for the emerging innovative-competitive era

This paper reports on a multi-tool commercial/military environment combining software Domain Analysis techniques with Reusable Software and Reengineering of Legacy Software. It is based on the development of a military version for the Department of Defense (DOD). The integrated tools in the military version are: Software Specification Assistant (SSA) and Software Reengineering Environment (SRE), developed by Computer Command and Control Company (CCCC) for Naval Surface Warfare Center (NSWC) and Joint Logistics Commanders (JLC), and the Advanced Research Project Agency (ARPA) STARS Software Engineering Environment (SEE) developed by Boeing for NAVAIR PMA 205. The paper describes transitioning these integrated tools to commercial use. There is a critical need for the transition for the following reasons: First, to date, 70 percent of programmers' time is applied to software maintenance. The work of these users has not been facilitated by existing tools. The addition of Software Reengineering will also facilitate software maintenance and upgrading. In fact, the integrated tools will support the entire software life cycle. Second, the integrated tools are essential to Business Process Reengineering, which seeks radical process innovations to achieve breakthrough results. Done well, process reengineering delivers extraordinary gains in process speed, productivity and profitability. Most importantly, it discovers new opportunities for products and services in collaboration with other organizations. Legacy computer software must be changed rapidly to support innovative business processes. The integrated tools will provide commercial organizations important competitive advantages. This, in turn, will increase employment by creating new business opportunities. Third, the integrated system will produce much higher quality software than use of the tools separately. The reason for this is that producing or upgrading software requires keen understanding of extremely complex applications which is facilitated by the integrated tools. The radical savings in the time and cost associated with software, due to use of CASE tools that support combined Reuse of Software and Reengineering of Legacy Code, will add an important impetus to improving the automation of enterprises. This will be reflected in continuing operations, as well as in innovating new business processes. The proposed multi-tool software development is based on state of the art technology, which will be further advanced through the use of open systems for adding new tools and experience in their use.

Hwang, Phillip Q.↗
DOE OSTI2024

Assessing decision boundaries under uncertainty

In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this paper offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The approach proposed in this paper is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties

X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.

Moldabekov, Zhandos A. [Helmholtz-Zentrum Dresden-↗
NASA NTRS1993

A sensitive continuum analysis method for gamma ray spectra

In this work we examine ways to improve the sensitivity of the analysis procedure for gamma ray spectra with respect to small differences in the continuum (Compton) spectra. The method developed is applied to analyze gamma ray spectra obtained from planetary mapping by the Mars Observer spacecraft launched in September 1992. Calculated Mars simulation spectra and actual thick target bombardment spectra have been taken as test cases. The principle of the method rests on the extraction of continuum information from Fourier transforms of the spectra. We study how a better estimate of the spectrum from larger regions of the Mars surface will improve the analysis for smaller regions with poorer statistics. Estimation of signal within the continuum is done in the frequency domain which enables efficient and sensitive discrimination of subtle differences between two spectra. The process is compared to other methods for the extraction of information from the continuum. Finally we explore briefly the possible uses of this technique in other applications of continuum spectra.

Thakur, Alakh N.↗
NASA NTRS1993

Cloud detection using satellite measurements of infrared and visible radiances for ISCCP

This paper, the first of three, describes the cloud detection part of the International Satellite Cloud Climatology Project (ISCCP) analysis. Key features of the cloud detection alogrithm are (1) use of space and time radiance variation tests over several different space and time domains to account for the global variety of cloudy and clear characteristics, (2) estimation of clear radiance values for every time and place, and, (3) use of radiance thresholds that vary with the type of surface and climate regime. Design of the detection algorithm was supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes. A summary of these statistical results is presented to illustrate how the cloud detection method works in a variety of circumstances. The sensitivity of the results to changing test parameter values is determined to provide a first estimate of the uncertainty of ISCCP cloud amounts. These test results (which exclude polar regions) suggest detection uncertainties of about 10% with possible negative biases of 5% (especially at night).

Rossow, Illiam B.↗
NASA NTRS1994

Wing-Body Aeroelasticity Using Finite-Difference Fluid/Finite-Element Structural Equations on Parallel Computers

In recent years significant advances have been made for parallel computers in both hardware and software. Now parallel computers have become viable tools in computational mechanics. Many application codes developed on conventional computers have been modified to benefit from parallel computers. Significant speedups in some areas have been achieved by parallel computations. For single-discipline use of both fluid dynamics and structural dynamics, computations have been made on wing-body configurations using parallel computers. However, only a limited amount of work has been completed in combining these two disciplines for multidisciplinary applications. The prime reason is the increased level of complication associated with a multidisciplinary approach. In this work, procedures to compute aeroelasticity on parallel computers using direct coupling of fluid and structural equations will be investigated for wing-body configurations. The parallel computer selected for computations is an Intel iPSC/860 computer which is a distributed-memory, multiple-instruction, multiple data (MIMD) computer with 128 processors. In this study, the computational efficiency issues of parallel integration of both fluid and structural equations will be investigated in detail. The fluid and structural domains will be modeled using finite-difference and finite-element approaches, respectively. Results from the parallel computer will be compared with those from the conventional computers using a single processor. This study will provide an efficient computational tool for the aeroelastic analysis of wing-body structures on MIMD type parallel computers.

Byun, Chansup↗
NASA NTRS2012

Experimenting with the GMAO 4D Data Assimilation

The Global Modeling and Assimilation Office (GMAO) has been working to promote its prototype four-dimensional variational (4DVAR) system to a version that can be exercised at operationally desirable configurations. Beyond a general circulation model (GeM) and an analysis system, traditional 4DV AR requires availability of tangent linear (TL) and adjoint (AD) models of the corresponding GeM. The GMAO prototype 4DVAR uses the finite-volume-based GEOS GeM and the Grid-point Statistical Interpolation (GSI) system for the first two, and TL and AD models derived ITom an early version of the finite-volume hydrodynamics that is scientifically equivalent to the present GEOS nonlinear GeM but computationally rather outdated. Specifically, the TL and AD models hydrodynamics uses a simple (I-dimensional) latitudinal MPI domain decomposition, which has consequent low scalability and prevents the prototype 4DV AR ITom being used in realistic applications. In the near future, GMAO will be upgrading its operational GEOS GCM (and assimilation system) to use a cubed-sphere-based hydrodynamics. This versions of the dynamics scales to thousands of processes and has led to a decision to re-derive the TL and AD models for this more modern dynamics, thus taking advantage of a two-dimensional MPI decomposition and improved scalability properties. With the aid of the Transformation of Algorithms in FORTRAN (l'AF) automatic adjoint generation tool and some hand-coding, a version of the cubed-sphere-based TL and AD models, with a simplified vertical diffusion scheme, is now available, enabling multiple configurations of standard implementations of 4DV AR in GEOS. Concurrent to this development, collaboration with the National Centers for Environmental Prediction (NCEP) and the Earth System Research Laboratory (ESRL) has allowed GMAO to implement a hybrid-ensemble capability within the GEOS data assimilation system. Both 3Dand 4D-ensemble capabilities are presently available thus allowing GMAO to now evaluate the performance and benefit of various ensemble and variational assimilation strategies. This presentation will cover the most recent developments taking place at GMAO and show results from various comparisons from traditional techniques to more recent ensemble-based ones.

Todling, R.↗
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Statistics and sensitivity of axion wind detection with the homogeneous precession domain of superfluid helium-3

The homogeneous precession domain (HPD) of superfluid He 3 has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling g a N N competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in order to make realistic projections for a full-fledged experimental program. We include the effects of clock error and measurement error in a concrete readout scheme using superconducting qubits and quantum metrology. This work also provides a more general framework to describe the statistics associated with the axion gradient coupling through the treatment of a transient resonance with a nonstationary background in a time-series analysis. Incorporating an optimal data-taking and analysis strategy, we project a sensitivity approaching g a N N ∼ 10 − 12 GeV − 1 across a decade in axion mass. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗