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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

eCC++ : A Compiler Construction Framework for Embedded Domain-Specific Languages

eCC++ is a new compiler construction framework for embedding domain-specific programming languages within C++. That is, the host language is C++, the guest language is the DSL to be embedded in C++, and eCC++ is the tool that enables the embedding. The eCC++ framework is composed of three main components: a front-end, an API for program verification based on a functional and declarative intermediate representation (IR), and a Multi-Level Intermediate Representation (MLIR) code generator. The eCC++ front-end consists of a library of C++ classes and operators that can be used to define the guest language. Guest sources are compiled with any standard C++ compiler, and when run, the resulting executable generates an eCC++ IR representation of the program, which can be verified within the eCC++ framework. Finally, eCC++ allows for high-level and domain-specific optimizations before generating MLIR. In summary, eCC++ aims to act as a generic front-end that enables embedding guest languages into C++, and provides necessary compiler technology for program verification, targeting the existing capabilities in the MLIR infrastructure. The paper evaluates the eCC++ expressiveness and usability describing the process of embedding GraphIt, a high-performance graph language in C++.

Gonzalez Tallada, Marc↗
DOE OSTI2022

Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.

Liu, Minzhao↗
DOE OSTI2020

Graphite-Embedded High-Performance Insulated Metal Substrate for Wide-Bandgap Power Modules

Emerging wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (MOSFETs) and gallium nitride high-electron-mobility transistors can handle high power in reduced semiconductor areas better than conventional Si-based devices owing to superior material properties. With increased power loss density in a WBG-based converter and reduced die size in power modules, thermal management of power devices must be optimized for high performance. Here, we present a graphite-embedded insulated metal substrate (thermally-annealed-pyrolytic-graphite-embedded insulated metal substrate—IMSwTPG) designed for WBG power modules. Theoretical thermal performance analysis of graphite-embedded metal cores is presented, with design details for IMSwTPG with embedded graphite to replace a direct-bonded copper (DBC) substrate. The proposed IMSwTPG is compared with an aluminum nitride-based DBC substrate using finite-element thermal analysis for steady-state and transient thermal performance. The solutions’ thermal performances are compared under different coolant temperature and thermal loading conditions, and the proposed substrate's electrical performance is validated with static and dynamic characterization. Using graphite-embedded substrates, junction-to-case thermal resistance of SiC MOSFETs can be reduced up to 17%, and device current density can be increased by 10%, regardless of the thermal management strategy used to cool the substrate. Reduced transient thermal impedance of up to 40% of dies owing to increased heat capacity is validated in transient thermal simulations and experiments. The half-bridge power module's electrical performance is evaluated for on -state resistance, switching performance, and switching loss at three junction temperature conditions. The proposed substrate solution has minimal impact on conduction and switching performance of SiC MOSFETs.

42 ENGINEERING↗
DOE OSTI2017

Visual Exploration of Semantic Relationships in Neural Word Embeddings

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Electroreduction of Zirconia Using Embedded Electrodes

Disc-shaped samples of 8 mol.% Y 2 O 3 -stabilized zirconia (YSZ) with two embedded Pt + YSZ circular electrodes and contacting Pt wires were made by sintering in air at 1500°C. In one sample, four Pt probes were embedded for local electric potential measurements. Porous surface Pt electrodes were applied on the two exposed surfaces. The samples were heated in air over a temperature range from 800°C to 900°C. A DC voltage between 1 V and 1.7 V was applied across the surface electrodes and the embedded electrodes with the positive connected to the surface electrodes. Furthermore, this resulted in the formation of black zirconia between the two embedded electrodes by electrochemical pumping out oxygen. In the sample with embedded Pt probes, the local potential was measured as a function of time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Embedded Sensors and Controls to Improve Component Performance and Reliability -- Loop-scale Testbed Design Report

Embedded instrumentation and control systems that can operate in extreme environments are challenging to design and operate. Extreme environments limit the options for sensors and actuators and degrade their performance. Because sensors and actuators are necessary for feedback control, these limitations mean that designing embedded instrumentation and control systems for the challenging environments of nuclear reactors requires advanced technical solutions that are not available commercially. This report details the development of testbed that will be used for cross-cutting embedded instrumentation and control research for nuclear power applications. This research is funded by the Department of Energy's Nuclear Energy Enabling Technology program's Advanced Sensors and Instrumentation topic. The design goal of the loop-scale testbed is to build a low temperature pump that utilizes magnetic bearing that will be incorporated into a water loop to test control system performance and self-sensing techniques. Specifically, this testbed will be used to analyze control system performance in response to nonlinear and cross-coupling fluid effects between the shaft axes of motion, rotordynamics and gyroscopic effects, and impeller disturbances. This testbed will also be used to characterize the performance losses when using self-sensing position measurement techniques. Active magnetic bearings are a technology that can reduce failures and maintenance costs in nuclear power plants. They are particularly relevant to liquid salt reactors that operate at high temperatures (700 C). Pumps used in the extreme environment of liquid salt reactors provide many engineering challenges that can be overcome with magnetic bearings and their associated embedded instrumentation and control. This report will give details of the mechanical design and electromagnetic design of the loop-scale embedded instrumentation and control testbed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

The Urban Energy-Water Nexus: Utility-Level Water Flows and Embedded Energy

There are limited open source data available for determining water production/treatment and required energy for cities across the United States. This database represents the culmination of a two-year effort to obtain data from cities across the United States via open records requests in order to determine the state of the U.S. urban energy-water nexus. Data were requested at the daily or monthly scale when available for 127 cities across the United States, represented by 253 distinct water and sewer districts. Data were requested from cities larger than 100,000 people and from each state. In the case of states that did not have cities that met these criteria, the largest cities in those states were selected. The resulting database represents a drinking water service population of 81.4 million and a wastewater service population of 86.2 million people. Average daily demands for the United States were calculated to be 560 liters per capita for drinking water and 500 liters per capita of wastewater. The embedded energy within each of these resources is 340 kWh/1000 m3 and 430 kWh/1000 m3, respectively. Drinking water data at the annual scale are available for production volume (89 cities) and for embedded energy (73 cities). Annual wastewater data are available for treated volume (104 cities) and embedded energy (90 cities). Monthly data are available for drinking water volume and embedded energy (73 and 56 cities) and wastewater volume and embedded energy (88 and 70 cities). Please see the two related papers for this metadata are included with this submission. Each folder name is a city that contributed data to the collection effort (City+State Abbreviation). Within each folder is a .csv file with drinking water and wastewater volume and energy data. A READ-ME file within each folder details the contents of the folder within any relevant information pertaining to data collection. Data are on the order of a monthly timescale when available, and yearly if not. Please cite the following papers when using the database: Chini, C.M. and Stillwell, A.S. (2017). The State of U.S. Urban Water: Data and the Energy-Water Nexus. Water Resources Research. 54(3). DOI: https://doi.org/10.1002/2017WR022265 Chini, C.M., and Stillwell, A. (2016). Where are all the data? The case for a comprehensive water and wastewater utility database. Journal of Water Resources Planning and Management. 143(3). DOI: 10.1061/(ASCE)WR.1943-5452.0000739

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1986

Embedded mesh solution of the 2-D Euler equations - Evaluation of interface formulations

Solution of the steady 2-D Euler equations using mesh embedding, or local grid refinement, with a cell-centered finite volume scheme is investigated. Embedded regions which are topologically similar to the global grid are considered. An isoenergetic model for the governing equations is used in Jameson's finite volume multistage scheme with modifications to the boundary conditions and smoothing. A detailed study of the embedding interface flux and smoothing formulations is conducted. Taylor expansion analysis reveals that local second order spatial accuracy is not possible if a conservative interface flux formulation is used. The analysis also gives constraints for local first order accuracy. An energy stability analysis indicates that downwind weighting of interface fluxes causes local instabilities. Analysis shows that conservative interface smoothing formulations must have a locally convective component, but that correct interface formulations allow globally dissipative smoothing. Embedded mesh solutions obtained with this scheme are presented for a transonic airfoil. They show that if embedding interfaces are close to the shocks, then small modifications in the interface location can have large effects on converge and solution accuracy.

Allmaras, S. R.↗
NASA NTRS1991

The nature, origin and evolution of embedded star clusters

The recent development of imaging infrared array cameras has enabled the first systematic studies of embedded protoclusters in the galaxy. Initial investigations suggest that rich embedded clusters are quite numerous and that a significant fraction of all stars formed in the galaxy may begin their lives in such stellar systems. These clusters contain extremely young stellar objects and are important laboratories for star formation research. However, observational and theoretical considerations suggest that most embedded clusters do not survive emergence from molecular clouds as bound clusters. Understanding the origin, nature, and evolution of embedded clusters requires understanding the intimate physical relation between embedded clusters and the dense molecular cloud cores from which they form.

Lada, Charles J.↗
DOE OSTI2014

Diverse Power Iteration Embeddings and Its Applications

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detection and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Explaining word embeddings with perfect fidelity: a case study in predicting research impact

The best-performing approaches for scholarly document quality prediction are based on embedding models. In addition to their performance when used in classifiers, embedding models can also provide predictions even for words that were not contained in the labelled training data for the classification model, which is important in the context of the ever-evolving research terminology. Although model-agnostic explanation methods, such as Local interpretable model-agnostic explanations, can be applied to explain machine learning classifiers trained on embedding models, these produce results with questionable correspondence to the model. We introduce a new feature importance method, Self-Model Entities Rated (SMER), for logistic regression-based classification models trained on word embeddings. We show that SMER has theoretically perfect fidelity with the explained model, as the average of logits of SMER scores for individual words (SMER explanation) exactly corresponds to the logit of the prediction of the explained model. Quantitative and qualitative evaluation is performed through five diverse experiments conducted on 50,000 research articles (papers) from the CORD-19 corpus. In conclusion, through an AOPC curve analysis, we experimentally demonstrate that SMER produces better explanations than LIME, SHAP and global tree surrogates.

Coarse-grained models↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Projective embedding of dynamical systems: Uniform mean field equations

Herein we study embeddings of continuous dynamical systems in larger dimensions via projector operators. We call this technique PEDS, projective embedding of dynamical systems, as the stable fixed point of the original system dynamics are recovered via projection from the higher dimensional space. In this paper we provide a general definition and prove that for a particular type of rank-1 projector operator, the uniform mean field projector, the equations of motion become a mean field approximation of the dynamical system. While in general the embedding depends on a specified variable ordering, the same is not true for the uniform mean field projector. We prove a variety of results on the relationship between the spectrum of the Jacobian for fixed points in the original and in the embedded system. Direct applications of PEDS can be non-convex optimization and machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Accelerating Embedding Potential Optimization by Reconstructing the Pseudo-Valence Electron Density

Density functional embedding theory (DFET) enables use of electronic structure methods with higher accuracy than density functional theory in a local region, with applications thus far ranging from (photo/electro)catalysis to reactions in solution. DFET partitions a large collection of atoms into smaller groups that interact via a shared embedding (interaction) potential V emb , determined via functional optimization. The optimized effective potential (OEP) process used to optimize V emb is time-consuming and becomes a computational bottleneck due to sharp, oscillating features of V emb near nuclei. Here, similar to pseudopotential theory, by reconstructing electron densities used in the OEP process from smoother pseudo-valence-only (PVO) electron densities as proxies for total densities of the full system and subsystems, we can retain accuracy in the embedded electronic structure calculations while potentially reducing the overhead of V emb construction, within the projector augmented-wave (PAW) formalism. We explore three different chemical reactions as exemplars to test PVO–DFET, namely, H 2 dissociative adsorption on a Cu(111) surface, H 2 O adsorption on a Pt(111) surface, and aqueous [Ca 2+ –SO 4 2– ] ion-pair formation. The PVO approximation works well for all three systems with minimal loss of accuracy (∼10–70 meV error relative to the original exact-derivative (ED) approach) while accelerating V emb generation for the Cu and Pt systems respectively by 20× and 5×. Given proper numerical convergence parameters, the spatial distributions of differences between PVO- and ED-based V emb outside the core regions are small, explaining the exceptional agreement between the two approaches. Finally, we anticipate that this more efficient PVO–DFET approximation will be useful whenever computation of V emb is much more expensive than subsequent embedded high-level electron correlation calculations.

approximation↗
DOE OSTI2018

Subspace Density Matrix Functional Embedding Theory: Theory, Implementation, and Applications to Molecular Systems

We introduce the subspace density matrix functional embedding theory (sDMFET), in which optimization of the nonlocal embedding potential and subsequent embedded correlated wave function calculations are carried out within a truncated subspace determined by a Schmidt decomposition. As compared to the original density matrix functional embedding theory [K. Yu and E. A. Carter, Proceedings of the National Academy of Sciences 2017, 114, E10861], the computational cost of sDMFET is significantly reduced while the accuracy is preserved. Here, we perform test calculations for both covalently and noncovalently bound molecular systems to demonstrate the feasibility of our theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Fully Quantum Embedding with Density Functional Theory for Full Configuration Interaction Quantum Monte Carlo

We here develop a fully quantum embedded version of initiator full configuration interaction quantum Monte Carlo (i–FCIQMC) and apply it to study an ionic bond (lithium hydride, LiH) and a covalent bond (hydrogen flouride, HF) physisorbed to a benzene molecule. The embedding is performed using a recently developed Huzinaga projection operator approach, which affords good synergy with i–FCIQMC by minimizing the number of orbitals in the calculation. When considering the dissociation energy of these bonds into closed-shell ionic fragments, we find that i–FCIQMC embedded in density functional theory (i–FCIQMC-in-DFT) delivers comparable accuracy with coupled cluster singles and doubles with perturbative triples embedded in density functional theory (CCSD(T)-in-DFT). In treating the bond dissociation energy curve of HF, i–FCIQMC-in-DFT has improved accuracy over CCSD(T)-in-DFT due to the presence of strong correlation. Lastly, we discuss the implications of the new i–FCIQMC-in-DFT method as applied to bond breaking in catalysis.

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

Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Quantum mechanical embedding methods hold the promise to transform not just the way calculations are performed, but to significantly reduce computational costs and improve scaling for macro molecular systems containing hundreds if not thousands of atoms. The field of embedding has grown increasingly broad with many approaches of different intersecting flavors. Here, we lay out the methods into two streams: QM:MM and QM:QM, showcasing the advantages and disadvantages of both. We provide a review of the literature, the underpinning theories including our contributions, and we highlight current applications with select examples spanning both materials and life sciences. Lastly, we conclude with prospects and future outlook on embedding, and our view on the use of universal test case scenarios for cross comparisons of the many available (and future) embedding theories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗