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At least 199 records · Page 11

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NASA NTRSDate not supplied

Eigenvalue Sensitivity Computations for Linear Stability Theory

To realize the drag reduction benefit of boundary-layer transition control strategies, it is crucial to integrate transition prediction into the vehicle design through an optimization process. The integration of transition prediction based on linear stability analysis into adjoint d design optimization requires coupling an adjoint enabled computational fluid dynamics (CFD) solver with an adjoint enabled linear stability code. In particular, the boundary-layer transition location is often predicted using the N-factor method based on linear stability theory (LST). Thus, sensitivity of the linear-stability eigenvalues constitute an essential building block for optimizing the laminar flow performance. The present paper describes an implementation of LST eigenvalue sensitivity analysis that can be easily coupled with a CFD solver. Specifically, we describe a discrete adjoint formulation for the transition location prediction based on the N-factor method. The verification of this formulation is carried out by comparing the adjoint-based sensitivity of the local growth rate of a given instability mode with respect to the disturbance frequency, and the adjoint-based sensitivity of the transition location with respect to spanwise wavenumber with those sensitivities computed using a finite-difference approximation. Finally, the adjoint LST formulation is applied to flat-plate boundary-layer flows at transonic, supersonic, and hypersonic conditions, to determine the behavior and sensitivities of the transition location with respect to a range of disturbance spanwise wavenumbers.

Boundary Layer Transition↗

DOE OSTI2021

Processable High Electron Mobility {pi}-Copolymers via Mesoscale Backbone Conformational Ordering

The synthesis and experimental/theoretical characterization of a new series of electron-transporting copolymers based on the naphthalene bis(4,8-diamino-1,5-dicarboxyl)amide (NBA) building block are reported. Comonomers are designed to test the emergent effects of manipulating backbone torsional characteristics, and density functional theory (DFT) analysis reveals the key role of backbone conformation in optimizing electronic delocalization and transport. The NBA copolymer conformational and electronic properties are characterized using a broad array of molecular/macromolecular, thermal, optical, electrochemical, and charge transport techniques. All NBA copolymers exhibit strongly aggregated morphologies with significant nanoscale order. Copolymer charge transport properties are investigated in thin-film transistors and exhibit excellent electron mobilities ranging from 0.4 to 4.5 cm(2) V-1 s(-1). Importantly, the electron transport efficiency correlates with the film mesoscale order, which emerges from comonomer-dependent backbone planarity and extension. These results illuminate the key NBA building block structure-morphology-bulk property design relationships essential for processable, electronics-applicable high-performance polymeric semiconductors.

n‐↗

DOE OSTI2021

Monolayer CrCl 3 as an Ideal Test Bed for the Universality Classes of 2D Magnetism

The monolayer halides CrX 3 (X=Cl, Br, I) attract significant attention for realizing 2D magnets with genuine long-range order (LRO), challenging the Mermin-Wagner theorem. In this work, we show that monolayer CrCl 3 has the unique benefit of exhibiting tunable magnetic anisotropy upon applying a compressive strain. This opens the possibility to use CrCl 3 for producing and studying both ferromagnetic and antiferromagnetic 2D Ising-type LRO as well as the Berezinskii-Kosterlitz-Thouless (BKT) regime of 2D magnetism with quasi-LRO. Using state-of-the-art density functional theory, we explain how realistic compressive strain could be used to tune the monolayer's magnetic properties so that it could exhibit any of these phases. Building on large-scale quantum Monte Carlo simulations, we compute the phase diagram of strained CrCl 3 , as well as the magnon spectrum with spin-wave theory. Our results highlight the eminent suitability of monolayer CrCl 3 to achieve very high BKT transition temperatures, around 50 K, due to their singular dependence on the weak easy-plane anisotropy of the material.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Bayesian model mixing with multireference energy density functional

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Field Testing of a Hierarchical Model-Free Transactive Control Strategy in a Residential House

Demand response plays an important role in addressing the uncertain, intermittent, and variable nature of renewable energy sources. However, to be effective, it requires a significant amount of demand load flexibility, in which buildings are important flexible assets for electricity systems. In this regard, many transactive control tools have been developed to harness this demand flexibility. Yet, such tools are mostly simulation-based or deal with a single load, which may not be sufficient to demonstrate their effectiveness. Towards this end, this paper presents a field implementation and testing of a hierarchical model-free transactive control strategy using residential building thermostatically controlled loads. The transactive control implementation incorporates elements of virtual battery, game theory, and model-free control mechanisms. Experimental field-testing results are provided to demonstrate the effectiveness of the proposed transactive control strategy.

Olama, Mohammed M.↗

NASA NTRS1998

An Investigation into the Elementary Temporal Structure of Solar Flare Hard X-Ray Bursts Using BATSE

The research performed under this contract is part of an on-going investigation to explore the finest time-resolution hard X-ray data available on solar flares. Since 1991, the Burst and Transient Source Experiment (BATSE) aboard the Compton Gamma Ray Observatory has provided almost continual monitoring of the Sun in the hard X-ray and gamma-ray region of the spectrum. BATSE provides for the first time a temporal resolution in the data comparable to the timescales on which flare particle energization occurs. Under this contract, we have employed an important but under-utilized BATSE data type, the Time-To-Spill (TTS) data, to address the question of how fine a temporal structure exists in flare hard X-ray emission. By establishing the extent to which "energy release fragments," or characteristic (recurrent) time structures, are building blocks of flare emission, it is possible to place constraints on particle acceleration theories. We have utilized a spectral estimation technique, known as Lomb's normalized periodogram, to overcome the challenge of computing the power spectra of the unevenly sampled TTS data. By comparing the flare's power spectra to the expected power arising from Poisson noise, we obtain measurements of the smallest, statistically significant timescales present in the data. We have found, in an initial sample of 100 flares, that the smallest statistically significant timescales detected in a single flare are: 89 ms (30 May 1991 11:26:06) in channel 0, 117 ms (17 May 1991 09:03:20) in channel 1, 167 ms (31 May 1991 16:53:12) in channel 2, and 1.55 s (06 June 1991 01:02:08) in channel 3. We have also found some evidence for the existence of preferred timescales, however, the significance of this finding awaits a larger sample of flares.

Newton , Elizabeth↗

NASA NTRS1998

An Investigation into the Elementary Temporal Structure of Solar Flare Hard X-Ray Bursts Using BATSE

Newton, Elizabeth↗

NASA NTRSDate not supplied

Independent Technical Assessment of NASA and External Quantum Sensing Capability

The recent FY 2020 federal Research and Development Budget Priorities memo addresses the leadership need in Quantum Information Science (QIS) directing agencies to “prioritize QIS research and development (R&D), which will build the technical and scientific base necessary to explore the next generation of QIS theory, devices, and applications.” Quantum Sensing (QS) is an integral part of QIS. NASA is a key part of the directive to forward American space exploration and commercialization by providing “capabilities that have broad potential applications in space and on Earth.” QS provides an arena for NASA to demonstrate leadership in both areas of the administrative directive. QS uses quantum properties to achieve unprecedented measurement sensitivity and performance, including quantum-enhanced methodologies that outperform their classical counterparts. Typical quantum sensors exploit techniques such as atomic systems, matter waves, quantum entanglement, quantum superposition of states, quantum illumination methods, and manipulation of photons and atoms, in general. Guided by advancements in our ability to generate, manipulate, and control quantum systems, the emerging quantum sensing technologies promise unrivalled sensitivity, resolution, and precision, potentially leading to game-changing applications. Significant gains include technologies important for a range of NASA missions such as remote sensing, in situ measurements, metrology, interferometry, quantum communication, ranging, imaging, radar and lidar receivers, and gravity measurements. NASA Engineering and Safety Center has convened an independent external panel, comprising of Quantum Sensing Experts from Government, DoD, academia, and Federally funded Research and Development Center to conduct an independent technical assessment of the agency's capabilities in Quantum Sensing to understand NASA's internal needs and competencies related to Quantum Sensing and compare agency capabilities with those available externally including industry, academia, and other government agencies. The outcomes of the assessment will help the agency in establishing appropriate strategies and investments to develop and maintain the state-of-the-art sensing competence and capabilities required to meet the agency’s future needs. The External Experts Panel (EEP) is collaborating with HQ, various NASA Center and the representatives in the NASA Quantum Sensing Community of Practice (QS CoP), a part of NASA Sensors and Instrumentation Technical Fellow Technical Discipline Team, in obtaining common, current understanding of agency mission needs where QS can be an important enabler for future needs, and any programs, projects, assets, and technologists working in QS. The EEP organized a Quantum Sensing Workshop of practitioners from industry, academia, other government agencies, external experts, and interested NASA personnel to gather the assessment information. EEP also conducted information gathering on the industry at large, educational institutions, and other government agency research efforts for capture in the assessment database. At the conclusion of this assessment, EEP team will develop findings and conclusions describing NASA’s capabilities for the mission needs, NASA's relative position on new, enabling technologies, and an analysis of the gaps that may present any risks to near-term or far-term mission needs. This presentation will give details of the NASA and External Quantum Sensing Assessment outcomes, findings, observations, and its recommendation to NASA as how it can advance Quantum Sensing technologies and techniques for its science and explorations related missions.

External Quantum Sensing Capability↗

DOE OSTI2022

Thermal modeling of directed energy deposition additive manufacturing using graph theory

Purpose: The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach: Here, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings: The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications: This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications: The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value: This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

42 ENGINEERING↗

NASA NTRS1999

Intent Specifications: An Approach to Building Human-Centered Specifications

This paper examines and proposes an approach to writing software specifications, based on research in systems theory, cognitive psychology, and human-machine interaction. The goal is to provide specifications that support human problem solving and the tasks that humans must perform in software development and evolution. A type of specification, called intent specifications, is constructed upon this underlying foundation.

Leveson, Nancy G.↗

NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗

DOE OSTI2024

Extended Fayans energy density functional: optimization and analysis

The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even–even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model by about 30%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Solvable time-like cosets and holography beyond AdS

We build a novel time-like coset sigma-model describing type-II superstring theory in a charged rotating black-brane background that interpolates between a local AdS 3 in the IR and a linear-dilaton geometry in the UV. This allows one to peform a systematic study of holography in non-AdS backgrounds which are smoothly connected to AdS 3 . We construct massless closed string states vertex operators in the NS-NS sector, calculate the corresponding two-point correlation functions, and discuss holographic interpretation of our results from 4+1 dimensional boundary field theory point of view. Compactifying the theory on T 4 , we show that the spectrum of a single long string with unit winding agrees with the spectrum of a CFT 2 deformed by TT¯. We also calculate correlation functions of operators of the dual 1+1 dimensional non-conformal boundary field theory using worldsheet techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Analyzing the co-evolution of green technology diffusion and consumers’ pro-environmental attitudes: An agent-based model

Massive diffusion of green technologies is significant for building a cleaner world. However, the process of technology diffusion is usually slow and complex. An in-depth understanding of the mechanism regarding green technology diffusion is an essential precondition for effectively stimulating this process. Green technology diffusion heavily involves both social and technological changes. Although existing studies have provided rich knowledge about identifying critical drivers and barriers affecting green technology diffusion, research that considers consumers’ pro-environmental attitudes and green technology diffusion as an evolving system is still sparse. Aiming at exploring the co-evolution of consumers’ pro-environmental attitudes and green technology diffusion, this paper builds an agent-based model that integrates the relative agreement model with technology diffusion theories to conduct a sequence of controlled numerical experiments, which progressively unveil how attitudinal and technological factors impact green technology diffusion. The main findings include that (1) improving consumers’ pro-environmental attitudes is prominently beneficial to green technology diffusion and maturation; (2) technology maturity has very limited impact on consumers’ first-time purchases but significantly affects consumers’ satisfaction, which would further impact consumers’ repeat purchases; (3) consumers that do not support green technologies frequently emerge during the evolution of attitudes (despite the high technology maturity), which corresponds to the emergence of the anti-environmental groups observed in the real world; (4) active interactions between non-adopters enable their attitudes to converge, which results in the polarization of consumers’ attitudes.

ABM↗

DOE OSTI2025

A noncollinear density functional theory ansatz for the phononic and thermodynamic properties of α -Pu

Plutonium's phase diagram is host to complex structures and interactions that make the description of its ground state properties elusive. Using all-electron density functional theory calculations, we study the thermodynamic properties of α-Pu. To do this, we build on recent work in the literature by introducing a novel noncollinear magnetic ansatz for α-Pu's ground state. We show that the noncollinear ansatz improves the description of the experimental phonon density of states, heat capacity, and thermal expansion over previous results obtained with a collinear ansatz. Here, despite lacking a bond-equivalent picture of the bonding, these new results for α-Pu, along with recent results on δ-Pu, demonstrate the efficacy of noncollinear ansatzes for the description of plutonium.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Incompatibility as a Resource for Programmable Quantum Instruments

Quantum instruments represent the most general type of quantum measurement, as they incorporate processes with both classical and quantum outputs. In many scenarios, it may be desirable to have some “on-demand” device that is capable of implementing one of many possible instruments whenever the experimenter desires. We refer to such objects as programmable instrument devices (PIDs), and this paper studies PIDs from a resource-theoretic perspective. A physically important class of PIDs are those that do not require quantum memories to implement, and these are naturally “free” in this resource theory. Additionally, these free objects correspond precisely to the class of unsteerable channel assemblages in the study of channel steering. The traditional notion of measurement incompatibility emerges as a resource in this theory since any PID controlling an incompatible family of instruments requires a quantum memory to build. We identify an incompatibility preorder between PIDs based on whether one can be transformed into another using processes that do not require additional quantum memories. Necessary and sufficient conditions are derived for when such transformations are possible based on how well certain guessing games can be played using a given PID. Ultimately our results provide an operational characterization of incompatibility, and they offer semi-device-independent tests for incompatibility in the most general types of quantum instruments. Published by the American Physical Society 2024

Physics↗

NASA NTRS1991

Development of a perturbation generator for vortex stability studies

Theory predicts vortex instability when subjected to certain types of disturbances. It was desired to build a device which could introduce controlled velocity perturbations into a trailing line vortex in order to study the effects on stability. A perturbation generator was designed and manufactured which can be attached to the centerbody of an airfoil type vortex generator. Details of design tests and manufacturing of the perturbation generator are presented. The device produced controlled perturbation with frequencies in excess of 250 Hz. Preliminary testing and evaluation of the perturbation generator performance was conducted in a 4 inch cylindrical pipe. Observations of vortex shedding frequencies from a centerbody were measured. Further evaluation with the perturbation generator attached to the vortex generator in a 2 x 3 foot wind tunnel were also conducted. Hot-wire anemometry was used to confirm the perturbation generator's ability to introduce controlled frequency fluctuations. Comparison of the energy levels of the disturbances in the vortex core was made between locations 42 chord lengths and 15 chord lengths downstream.

Riester, J. E.↗