Search NASA⌕ Search

SEARCH · Search NASA

Results for “embeddings”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Embedded Web Technology: Applying World Wide Web Standards to Embedded Systems

Embedded Systems have traditionally been developed in a highly customized manner. The user interface hardware and software along with the interface to the embedded system are typically unique to the system for which they are built, resulting in extra cost to the system in terms of development time and maintenance effort. World Wide Web standards have been developed in the passed ten years with the goal of allowing servers and clients to intemperate seamlessly. The client and server systems can consist of differing hardware and software platforms but the World Wide Web standards allow them to interface without knowing about the details of system at the other end of the interface. Embedded Web Technology is the merging of Embedded Systems with the World Wide Web. Embedded Web Technology decreases the cost of developing and maintaining the user interface by allowing the user to interface to the embedded system through a web browser running on a standard personal computer. Embedded Web Technology can also be used to simplify an Embedded System's internal network.

Ponyik, Joseph G.↗
DOE OSTI2024

Integrating subsystem embedding subalgebras and coupled cluster Green’s function: a theoretical foundation for quantum embedding in excitation manifold

Here, in this study, we introduce a novel approach to coupled-cluster Green's function (CCGF) embedding by seamlessly integrating conventional CCGF theory with the state-of-the-art sub-system embedding sub-algebras coupled cluster (SES-CC) formalism. This integration focuses primarily on delineating the characteristics of the sub-system and the corresponding segments of the Green's function, defined explicitly by active orbitals. Crucially, our work involves the adaptation of the SES-CC paradigm, addressing the left eigenvalue problem through a distinct form of Hamiltonian similarity transformation. This advancement not only facilitates a comprehensive representation of the interaction between the embedded sub-system and its surrounding environment but also paves the way for the quantum mechanical description of multiple embedded domains, particularly by employing the emergent quantum flow algorithms. Our theoretical underpinnings further set the stage for a generalization to multiple embedded sub-systems. This expansion holds significant promise for the exploration and application of non-equilibrium quantum systems, enhancing the understanding of system–environment interactions. In doing so, the research underscores the potential of SES-CC embedding within the realm of quantum computations and multi-scale simulations, promising a good balance between accuracy and computational efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Moving Embedded Boundary Approach for the Compressible Navier-Stokes Equations in a Block-Structured Adaptive Refinement Framework

A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured adaptive mesh refinement (SAMR) framework of AMReX [1], [91], [92]. We leverage the SAMR capability to obtain quantitatively accurate results whilst using robust, second-order finite volume schemes. A conservative, unsplit, cut-cell approach is utilized and a ghost-cell approach is developed for computing the flux on the moving, embedded boundary faces. A third-order least-squares formulation has been developed to compute the wall velocity gradients, and was found to significantly improve the performance of the solver in terms of the quantitative comparison of surface quantities such as the skin friction coefficient. Various test cases are performed to validate the method, and compared with analytical, experimental, and other numerical results in literature. Inviscid and viscous test cases are performed that span a wide regime of flow speeds - acoustic (harmonically pulsating sphere), smooth flows (expansion fan created by a receding piston) and flows with shocks (shock-cylinder interaction, shock-wedge interaction, pitching NACA 0012 airfoil and shock-cone interaction). A closed system with moving boundaries - an oscillating piston in a cylinder, showed that the percentage error in mass within the system decreases with refinement, demonstrating that the numerical scheme is conservative with grid refinement, but is not discretely conservative. Viscous test cases involve that of a horizontally moving cylinder at Re = 40, an inline oscillating cylinder at Re = 100, and a transversely oscillating cylinder at Re = 185. The judicious use of adaptive mesh refinement with appropriate refinement criteria to capture the regions of interest leads to well-resolved flow features, and good quantitative comparison is observed with the results available in literature.

adaptive refinement↗
DOE OSTI2021

Application of the locally self-consistent embedding approach to the Anderson model with non-uniform random distributions

Highlights: • Typical Medium Theory (TMT) for the Anderson Localization. • Locally Self-Consistent Multiple Scattering Method (LSMS) for Random Disordered Systems. • Linear Scaling Computational Method for Random Disordered Systems. We apply the recently developed embedding scheme for the locally self-consistent method to random disorder electrons systems. The method is based on the locally self-consistent multiple scattering theory and the typical medium theory. The locally self-consistent multiple scattering theory divides a system into many small designated local interaction zones. The subsystem within each local interaction zone is embedded in a self-consistent field from the typical medium theory. This approximation allows the study of random systems with large numbers of sites. We present results for the three dimensional Anderson model with different random disorder potential distributions. Using the typical density of states as an indicator of Anderson localization, we find that the method can capture the localization for commonly studied disorder potentials. These include the uniform distribution, the Gaussian distribution, and even the unbounded Cauchy distribution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Propagation of positive discharges in an air bubble having an embedded water droplet

Low temperature plasmas generated inside gas bubbles immersed in water is an effective method of rapidly transferring plasma generated reactive species to the water for applications in biomedicine, agriculture and environment. Reactive species are generally produced in the gas phase plasma and then solvate into the liquid. The large surface-to-volume ratio (SVR) of the bubble accelerates this process. In generating bubbles in water, aerosols and droplets are also contained within the bubble. These droplets also have a large SVR and so can be rapidly plasma activated. However, the presence of the droplets can also impact the propagation of the plasma in the bubble. In this paper, results are discussed from computational and experimental investigations of the formation and evolution of discharges in an air bubble immersed in water with an embedded water droplet. The computations were performed with a two-dimensional plasma hydrodynamics model. Experiments were performed with a quasi-2D bubble apparatus. In bubbles having a droplet, a plasma filament typically bridges from the powered electrode to the droplet, and then from the droplet to the bubble surface. A surface-hugging streamer also occurs on the inner bubble surface and on the surface of the droplet. Both surface streamers result in part from surface charge accumulation and can dominate the formation of reactive species that transport into the droplet. Increasing droplet conductivity suppresses propagation of the surface discharge and leads to a lower density of aqueous reactive species. Increasing conductivity of the surrounding water does not change the overall structure of the discharge but does slightly elevate the discharge intensity. Finally, the size and shape of the embedded droplet can significantly affect the formation and propagation of the streamer.

2D fluid modelling↗
DOE OSTI2022

A weighted state redistribution algorithm for embedded boundary grids

State redistribution is an algorithm that stabilizes cut cells for embedded boundary grid methods. This work extends the earlier algorithm in several important ways. First, state redistribution is extended to three spatial dimensions. Second, we discuss several algorithmic changes and improvements motivated by the more complicated cut cell geometries that can occur in higher dimensions. In particular, we introduce a weighted version with less dissipation in an easily generalizable framework. Third, we demonstrate that state redistribution can also stabilize a solution update that includes both advective and diffusive contributions. Notably, the stabilization algorithm is shown to be effective for incompressible as well as compressible reacting flows. Finally, we discuss the implementation of the algorithm for several exascale-ready simulation codes based on AMReX, demonstrating ease of use in combination with domain decomposition, hybrid parallelism and complex physics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural embedding: learning the embedding of the manifold of physics data

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Functionalized Embedded Monometallic Nickel Catalysts for Enhanced Hydrogen Evolution: Performance and Stability

Viable anionic exchange membrane (AEM) electrolysis for H 2 production requires highly active hydrogen evolution reaction (HER) catalysts that are also robust and cost-effective. Traditionally juxtaposition of two transition metals with one being more oxophilic is necessary for easier access to water in the inner Helmholtz plane as a source of protons in the crucial Volmer step for enhanced HER activity. However, they are prone to passivation at anodic potentials or deactivation via the formation of sub-surface metal hydrides at high cathodic overpotentials. Here we report a method of enabling close juxtaposition of Ni-Ni-oxide surfaces by functionalizing Ni-based catalysts via the careful thermal treatment of carbon-supported Ni nano-particles chelated with a select organic chelating compound referred to as cupferron. This functionalization results in an embedded Ni surface with a clear graphitic coating engendering long-term stability for passivation and subsurface hydride formation. The stability and activity of the functionalized Ni catalyst are demonstrated in half-cell RDE and full-cell AEM membrane-based hydrogen pump experiments. In the case of the latter, the functionalized Ni outperforms carbon-supported Pt.

25 ENERGY STORAGE↗
DOE OSTI2021

Natural Charge-Transfer Analysis: Eliminating Spurious Charge-Transfer States in Time-Dependent Density Functional Theory via Diabatization, with Application to Projection-Based Embedding

For many types of vertical excitation energies, linear-response time-dependent density functional theory (LR-TDDFT) offers a useful degree of accuracy combined with unrivaled computational efficiency, although charge-transfer excitation energies are often systematically and dramatically underestimated, especially for large systems and those that contain explicit solvent. As a result, low energy electronic spectra of solution-phase chromophores often contain tens to hundreds of spurious charge-transfer states, making LR-TDDFT needlessly expensive in bulk solution. More nefariously, intensity borrowing by the low-energy charge-transfer states can affect intensities of the valence excitations even if those excitation energies are accurate. At higher excitation energies, it is difficult to distinguish spurious CT states from genuine charge-transfer-to-solvent (CTTS) excitations. In this work, we introduce an automated diabatization scheme that enables fast and effective screening of the CTTS acceptor space in bulk solution. Our procedure introduces the concept of “natural charge-transfer orbitals”, which provide a means to isolate characteristic pairs of orbitals that are most likely to participate in a CTTS excitation. The projection of these orbitals onto solvent-centered virtual orbitals provides a criterion for defining the most important solvent molecules in a given excitation. We apply this method to analyze an ab initio molecular dynamics (MD) trajectory of I-(aq) and report the lowest-energy CTTS band in the absorption spectrum. Our results are in excellent agreement with experimental measurements for bulk I-(aq), and only one-third of the water molecules in the I-(H2O)96 simulation cell need to be described with LR-TDDFT in order to obtain excitation energies that are converged to < 0.1 eV. The tools introduced herein will improve the accuracy, efficiency, and usability of LR-TDDFT in solution-phase environments

Carter-Fenk, Kevin D.↗
DOE OSTI2022

Parallel simulated annealing with embedded machine learning and multifidelity models for reactor core design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Embedding of Fiber Optic Sensors in Stainless Steel for Sensing in Harsh Environments

Structural health monitoring is critical for components working in harsh environments to assess their safety and integrity. This can be realized with advanced sensors, such as fiber optic sensors, embedded in these components. Recent studies have used ultrasonic consolidation (UC) and laser-based additive manufacturing (AM) techniques to embed fiber optic sensors in metals. However, sensor embedding using these techniques has been challenging, with gaps and voids around the embedded fiber. Successful embedding of fiber optic sensors requires good contact between the fiber and the matrix to eliminate potential gas/liquid leaks in critical systems such as in nuclear and aerospace services. Defect-free fiber/matrix bond is also essential for accurate measurement of parameters of interest, such as temperature and strain. This study developed a novel fiber embedding technique based on electric field-assisted sintering (EFAS). Sapphire optical fibers with diameters of 125 µm and 250 µm were embedded in stainless steel (SS) 316L using EFAS. The embedding was completed by sintering SS 316L powders at a pressure of 50MPa, hold time of 5 min, and temperatures of 1000°C, 1100°C, and 1200°C. Joule heating mechanism during EFAS enabled rapid sintering of the SS 316L powder with resulting sample densities over 99%. Microscopy analysis performed on the cross-sections revealed a defect-free bond between the embedded fiber and SS 316L. The good integrity of the embedded fiber was evidenced by optical transmittance measurement. Optical attenuation of the fiber before and after embedment was examined in the near-infrared region. Scanning electron microscopy was employed to investigate the grain morphology of SS 316L near the embedded fiber. Hardness distribution in the sintered part was also investigated to study the effect of the embedded fiber on the mechanical properties of the as-fabricated component. The results indicated that EFAS was able to embed fiber in SS with good fiber functionality and part quality.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

In situ embedment of type K sheathed thermocouples with directed energy deposition

Advanced nuclear reactor systems require new technologies for heat transfer and system monitoring. Additive manufacturing (AM) offers the design flexibility to allow in-situ sensor embedment through smart manufacturing for real-time monitoring, and performance of these systems. Here, this study focuses on experiments investigating the feasibility of in-situ sensor embedment using directed energy deposition (DED). Type K thermocouples are embedded into 316L stainless steel (SS) samples using two different configurations (e.g., exposed and embedded tips) and two designs (e.g., flush to substrate) within an AM base. Embedded sensor samples are analyzed via in-situ measurements and high-temperature performance validation tests at 350ºC and 900ºC. Temperature performance results at both temperature tests show good agreement with manufacturer specifications proving that these sensors could still capture accurate temperature readings after in-situ embedment during DED processing. An additional optimization experiment was conducted on the exposed tip configuration using a surrogate thermocouple to improve tolerances and the embedment process. Results improved tolerances, lower porosity, smaller gaps between the sensor and base, and better junction contact for the sensor. Although further optimization of this embedment strategy is necessary to improve the structural stability and tolerances within the component, this research strategy provides a proof-of-feasibility for DED embedment with commercial sheathed thermocouples. This research provides early impact on embedment of sensor for multiple materials and complex geometric components..

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Benchmarking an Embedded Adaptive Sampling Configuration Interaction Method for Surface Reactions: H 2 Desorption from and CH 4 Dissociation on Cu(111)

Embedded (emb-) correlated wavefunction (CW) theory enables accurate assessments of both ground- and excited-state reaction mechanisms involved in heterogeneous catalysis. Embedded multireference second-order perturbation theory (emb-MRPT2) based on reference wavefunctions generated via embedded complete active space self-consistent field (emb-CASSCF) theory is currently state-of-the-art. However, the factorial scaling of CASSCF limits the size of active space and the complexity of systems that can be studied. In this work, we assess the efficacy of an alternative CW method, adaptive sampling configuration interaction (ASCI)–which enables large active spaces to be used–for studying surface reactions. We couple ASCI with density functional embedding theory (DFET) and benchmark its performance for two reactions: H 2 desorption from and CH 4 dissociation on the Cu(111) surface. Unlike embedded complete active space second-order perturbation theory (emb-CASPT2) that accurately reproduces a measured H 2 desorption barrier, embedded ASCI, using a very large active space (though one that still comprises a small portion of the full set of orbitals) fails to do so. Adding an extra correlation term from embedded Møller–Plesset second-order perturbation theory (emb-MP2) improves the desorption barrier and endothermicity predictions. Thus, the inaccuracy of embedded ASCI comes from the missing dynamic correlation from the many other electrons and orbitals not included in the active space. For CH 4 dissociation, again embedded ASCI overestimates the dissociation barrier compared to emb-CASPT2 predictions. Adding dynamic correlation from emb-MP2 helps correct the barrier. However, this composite approach suffers from double counting of correlation within embedded ASCI followed by emb-MP2 calculations. We therefore conclude that the state-of-the-art emb-MRPT2 based on reference wavefunctions generated via emb-CASSCF remains the method of choice for studying surface reactions. emb-ASCI is useful when large active spaces beyond the limit of emb-CASSCF are essential, such as to study complex surface reactions with significant multiconfigurational character (static correlation) but weak dynamic correlation.

08 HYDROGEN↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗